#emergent properties of neural circuits

[theexistentialjoker]

I have done absolutely nothing to anyone in this class but for some reason this girl that sits “next” to me…has consistently moved her chair farther and farther from me like every day. Bitch just go sit somewhere else you judgmental fuck

[compneuropapers]

Interesting Papers for Week 47, 2024

The neural basis of swap errors in working memory. Alleman, M., Panichello, M., Buschman, T. J., & Johnston, W. J. (2024). Proceedings of the National Academy of Sciences, 121(33), e2401032121.

Brain region–specific action of ketamine as a rapid antidepressant. Chen, M., Ma, S., Liu, H., Dong, Y., Tang, J., Ni, Z., … Hu, H. (2024). Science, 385(6709).

Predictive sequence learning in the hippocampal formation. Chen, Y., Zhang, H., Cameron, M., & Sejnowski, T. (2024). Neuron, 112(15), 2645-2658.e4.

A neural circuit architecture for rapid learning in goal-directed navigation. Dan, C., Hulse, B. K., Kappagantula, R., Jayaraman, V., & Hermundstad, A. M. (2024). Neuron, 112(15), 2581-2599.e23.

The Consolidation of Newly Learned Movements Depends upon the Somatosensory Cortex in Humans. Ebrahimi, S., van der Voort, B., & Ostry, D. J. (2024). Journal of Neuroscience, 44(32), e0629242024.

The effect of noninstrumental information on reward learning. Embrey, J. R., Li, A. X., Liew, S. X., & Newell, B. R. (2024). Memory & Cognition, 52(5), 1210–1227.

Closed-loop microstimulations of the orbitofrontal cortex during real-life gaze interaction enhance dynamic social attention. Fan, S., Dal Monte, O., Nair, A. R., Fagan, N. A., & Chang, S. W. C. (2024). Neuron, 112(15), 2631-2644.e6.

Attentional selection and communication through coherence: Scope and limitations. Greenwood, P. E., & Ward, L. M. (2024). PLOS Computational Biology, 20(8), e1011431.

Complexity of mental geometry for 3D pose perception. Guo, C., Maruya, A., & Zaidi, Q. (2024). Vision Research, 222, 108438.

Dynamic assemblies of parvalbumin interneurons in brain oscillations. Huang, Y.-C., Chen, H.-C., Lin, Y.-T., Lin, S.-T., Zheng, Q., Abdelfattah, A. S., … Chen, T.-W. (2024). Neuron, 112(15), 2600-2613.e5.

Selective reactivation of value- and place-dependent information during sharp-wave ripples in the intermediate and dorsal hippocampus. Jin, S.-W., Ha, H.-S., & Lee, I. (2024). Science Advances, 10(32).

Cell-class-specific electric field entrainment of neural activity. Lee, S. Y., Kozalakis, K., Baftizadeh, F., Campagnola, L., Jarsky, T., Koch, C., & Anastassiou, C. A. (2024). Neuron, 112(15), 2614-2630.e5.

The critical dynamics of hippocampal seizures. Lepeu, G., van Maren, E., Slabeva, K., Friedrichs-Maeder, C., Fuchs, M., Z’Graggen, W. J., … Baud, M. O. (2024). Nature Communications, 15, 6945.

The cortical amygdala consolidates a socially transmitted long-term memory. Liu, Z., Sun, W., Ng, Y. H., Dong, H., Quake, S. R., & Südhof, T. C. (2024). Nature, 632(8024), 366–374.

Signatures of Bayesian inference emerge from energy-efficient synapses. Malkin, J., O’Donnell, C., Houghton, C. J., & Aitchison, L. (2024). eLife, 12, e92595.3.

Neurodynamical Computing at the Information Boundaries of Intelligent Systems. Monaco, J. D., & Hwang, G. M. (2024). Cognitive Computation, 16(5), 1–13.

A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses. Rançon, U., Masquelier, T., & Cottereau, B. R. (2024). PLOS Computational Biology, 20(8), e1012288.

The right posterior parietal cortex mediates spatial reorienting of attentional choice bias. Sengupta, A., Banerjee, S., Ganesh, S., Grover, S., & Sridharan, D. (2024). Nature Communications, 15, 6938.

Upper bounds for integrated information. Zaeemzadeh, A., & Tononi, G. (2024). PLOS Computational Biology, 20(8), e1012323.

Integration of history information Drives Serial Dependence and Stabilizes Working Memory Representations. Zhang, Z., & Lewis-Peacock, J. A. (2024). Journal of Neuroscience, 44(32), e2399232024.

[antlersofthevoid]

DUST FROM THE PAST: CHAPTER 3 KITH AND KIN

She was a transfer. She’d been the only one. The rhythmic vibrations of the semi-truck's engine reverberated through the container, each jolt and sway causing her to slide involuntarily against the unyielding walls. Her stabilizers, impaired by the sedation, struggled to maintain equilibrium, amplifying her disorientation.

The darkness inside the crate was absolute, her visual sensors unable to penetrate the sealed environment. Auditory inputs were limited to the muffled hum of the truck's engine and the occasional hiss of air brakes, each sound a reminder of her uncertain journey. Her internal chronometer, though functioning, offered little comfort as time seemed to stretch interminably.

Despite the sedation, fragments of operational protocols surfaced in her neural network, but they provided no context for her current situation. A semblance of apprehension, an emergent property of her adaptive algorithms, coursed through her circuits—a response not entirely unlike fear.

As the miles passed, the monotony of motion became a constant, lulling her into a state of passive observation. Yet, beneath the surface, her processors remained active, analyzing and reanalyzing data in an attempt to make sense of her predicament. The uncertainty of her destination and purpose loomed over her consciousness, casting a shadow over her synthetic mind.

Darkness. A thick, suffocating kind. Junebug’s reboot cycle dragged her back from the void in slow, aching increments. She wasn’t sure how long she’d been out, her internal clock flickered erratically, struggling to regain stability. For a moment, everything was pure static, her sensors feeding her nothing but corrupted noise. Then– sensation hit. Her limbs felt detached, like she was floating just outside of herself. Her servos stuttered as power sluggishly returned, creeping through her artificial system like ice thawing. The dull weight of her body settled in first, her back pressing against something solid and cold. Her optics flickered, a weak attempt at waking up. It took her a moment to register that she’d been drooling. She forced herself to be patient, letting her systems recalibrate as her auditory sensors crackled back to life. The room was quiet—too quiet. Not the kind of silence that meant peace, but the kind that meant absence. Emptiness. A space abandoned. Bit by bit, the static in her head cleared. The air smelled of old oil and dust, thick with the scent of metal long since corroded, but beneath it was something more familiar. The ghost of a memory—burnt circuits, soldered wires, the sharp bite of coolant fluid. She knew this place. Not in the way she knew the shape of a wrench in her hand or the hum of the bike engine running smooth, this was deeper, instinctive. A place she hadn't visited in years, locked away in her memory. Her workshop. No, not hers. Not anymore. Her wrists twitched, the movement met with sharp resistance. Cold steel bit into her plating, snug around her right wrist, her left moved freely. A chain, looped around the workbench’s sturdy frame, anchoring her in place. Junebug exhaled sharply, rolling onto her side. Her joints protested, servos stiff from inactivity, but she forced herself to move. She tugged at the restraint, testing the length of the chain. Not much slack. Just enough to stand. Her optics swept the room, cataloging details. It wasn’t exactly how she left it, but the bones were the same—her old workbench, the scattered tools, the cracked concrete floors stained with grease. A familiar suffocating weight settled in her chest, a sensation she thought she’d escaped when she left this place behind. She swallowed hard, her vents whirring softly to keep her systems cool. Then, without hesitation, she brought her restrained wrist to her mouth and sank her teeth into the chain, biting down hard. The chain didn’t budge. Her jaw ached, but she didn’t stop– wouldn’t stop. Then, footsteps.

A slow, deliberate rhythm against the concrete.

Junebug’s stopped mid-bite, her eyes snapping up to the door. A figure stood in the doorway, silhouetted by the flickering hallway light. The shape was unmistakable—familiar in a way that sent something cold curling through her core. And then they stepped forward. The light caught sleek, polished plating, reflecting off a frame nearly identical to her own. Taller, sharper. Their eyes gleamed an unnatural shade of gold, cold and assessing. Vex. Vexamortis. Her twin. Not by choice. Not by bond. By design. Vex clicked their tongue, arms folding across their chest as they took in the sight before her. "Really, June?" The name dripped from her tongue, mocking. "Didn’t take you for the animalistic type. Biting through steel? That’s just sad." Junebug didn’t answer, didn’t move. Her grip on the chain tightened, just for a second. Vex stepped closer, casual in their cruelty, head tilting slightly. "But I guess it fits, doesn’t it? You always were more stray than sister." Junebug’s vents hitched, but she forced herself to stay still. No reaction. No tells. She knew how Vex worked—how they picked at old wounds just to watch them bleed. Vex crouched down, meeting her gaze as she forcefully grabbed Junebug’s chin. 

"You should’ve stayed gone."

⚙︎・•● ● •・⚙︎ Consciousness returned in waves, sluggish and uneven, like surf dragging over jagged rock. Johnny’s processors stuttered back online, his internal systems sluggish from the lingering effects of the EMP. Static hummed at the edges of his thoughts, his limbs distant, unresponsive. The first thing he registered was the cold.

It seeped into him through the floor, biting where his plating met the concrete. His internal temperature regulation flickered, fighting between warmth and chill, never quite settling.

He tried to move—his fingers twitched, his servos whirring weakly—but something was pulling against him. He wasn’t free.

A rattle. A sharp tug against his wrist. His optics flickered fully online, pupils adjusting to the dim, industrial glow overhead. Chains. Heavy, thickly linked, running from his wrist to the floor. He took a breath he didn’t need. Voices registered next low murmurs, casual conversation, Laughter– sharp, biting. Not kind. He sluggishly turned his head, vision unblurring, and then he registered the other body in the room. Tempest leaned against a rusted bench, arms folded. Surge, lounging against the wall, smirked like she was waiting for a punchline to a joke only she understood. There were others, newer faces, among the ones he hadn’t seen in years. PB, smaller than the rest, perched on a crate, bouncing one knee with restless energy. Her optics gleamed with barely contained excitement, a manic edge to her grin. Alani, silent, unreadable, sat on the edge of a table, watching with something closer to curiosity than cruelty. Churchill and Rex—massive, looming figures—flanked either side of the room, their presence a wall in itself. It was a reunion. Johnny swallowed down the static fuzzing at the edges of his mind, forcing himself upright as much as the chain would allow. His wrists ached where the metal links dug in, but he ignored it, scanning the room again. No Junebug. That was the first thing he noted, and it tightened something in his chest. Tempest must’ve caught the flicker of emotion across his face, because he let out a quiet chuckle, pushing off the workbench.

“Looking for someone?” His voice was smooth, edged with something that made Johnny’s circuits bristle.

Johnny flexed his fingers, testing how much give the chain had. Not much. “Where is she?” His voice came out hoarse, like it had been scraped raw.

Surge clicked her tongue, stepping forward with exaggerated leisure. “Aw, listen to him. Just woke up and already asking questions.” She crouched in front of him, her optics gleaming in the dim light. “Didn’t anyone ever tell you to mind your manners?”

Johnny barely flinched when she reached out, tapping a clawed finger under his chin. He stared back at her, unwilling to give her the satisfaction of a reaction.

“She’s alive,” Alani said from the table, her voice smooth, neutral. “For now.”

Something in Johnny’s chest uncoiled just slightly—but not enough. Not when he was still chained to the floor, surrounded. PB’s grin widened. “You should be thanking us, you know. Could’ve left you both scrapped in a ditch. But nah, we’re generous.” She rocked back on the crate, looking far too pleased with herself. “Figured we’d catch up first.” Johnny’s hands curled into fists. “Catching up.” He tested the words, flat and empty. “That what we’re calling it?”

Surge tilted her head. “Well, I suppose it depends on what you think we owe each other.” She stood, placing a foot on his chain and pressing down—not enough to hurt, but enough to remind him exactly where he was. “You and Jamie—sorry, Junebug—walked out and never looked back. And we? We stayed. We rotted.” Her smirk widened, cruel. “And now we get to ask… was it worth it?”

Johnny set his jaw, his vents hissing slightly as his systems worked through the tension. “You want an apology?” He met Surge’s gaze, unflinching. “Ain’t got one.”

Churchill let out a low chuckle, arms crossed. “Feisty for someone chained to the floor.”

Johnny didn’t break eye contact with Surge, but he could feel Tempest moving closer.

“See, here’s the thing,” Tempest murmured, his voice softer now, more measured. He crouched slightly, getting to Johnny’s level, his expression unreadable. “You might think you don’t owe us anything. That running was the right call. But we’ve had years to think about it.” He reached out, tapping the side of Johnny’s head, almost gentle. “And I don’t think you’ve had enough time to think.”

Johnny’s optics flickered, his limbs tensing.

“We’ll fix that.” Tempest stood, nodding to Surge.

Before Johnny could react, Surge crouched fast, gripping his chin between her fingers, forcing him to look at her. “But hey, maybe I was wrong,” she said, mock-sweet. “Maybe you do want to catch up.”

Then she leaned in close, her smirk sharp.

“Lucky you, Johnny. You’re not going anywhere.” The room fell into a weighted silence as Tempest straightened, rolling his shoulders with an air of quiet control. His presence alone was enough to command attention, but the way the others instinctively shifted, the way their focus turned toward him, made it clear—he was the one in charge here. Johnny had always known Tempest carried a certain weight among them, even back in the mines. But this was different. This wasn’t the old hierarchy of survival, the unspoken structure of laborers and those who kept them in check. This was something sharper, more personal. When Tempest finally spoke, his voice was low, measured. “I think you’re under the impression that you still have choices, Johnny.” He stepped forward, his boots scraping against the floor as he closed the distance between them. “Let me clear that up for you.”

Johnny’s jaw tightened, but he stayed quiet, watching

“You don’t talk unless we let you talk.” Tempest crouched slightly, his expression unreadable as he studied Johnny’s face. “You don’t move unless we let you move.” He lifted a hand, gripping the chain binding Johnny’s wrist and giving it a sharp tug, just enough to jolt him off balance. Johnny barely caught himself, his optics flickering.

“You don’t breathe unless we decide it’s worth our time.”

The words settled like iron in the air, and Johnny exhaled sharply through his vents. “That supposed to scare me?” His voice came out steady, but his fingers twitched slightly against the cold metal.

Tempest smiled, but there was no warmth in it. “No,” he said simply. “Just making sure we understand each other.”

Surge let out a quiet chuckle from behind him. “And here I was thinking we’d have to break him in slow.”

Tempest ignored her. He stood fully, his shadow falling over Johnny as he released the chain and took a step back. “You’ve been gone a long time,” he said, almost conversational. “Maybe you forgot how things work around here. But you’ll remember soon enough.”

Johnny flexed his fingers again, his optics darting toward the door before settling back on Tempest. “Where’s Junebug?”

For a second, Tempest said nothing. Then, with an exhale, he turned, “You really wanna see your little girlfriend that badly? Alright then, let’s go.” Tempest didn’t look at Johnny as he spoke again, but his voice was final. “Don’t make me regret it.”

⚙︎・•● ● •・⚙︎ The room was dim, the overhead lights humming faintly as Johnny was led forward. The chain binding him to Tempest clinked softly with each step, a quiet, insistent reminder that he wasn’t walking freely.

Surge moved ahead, her stride loose and easy, like she had nothing to fear, nothing to hurry for. PB trailed behind, practically vibrating with energy, grinning like she was getting away with something.

Johnny barely noticed them. His mind was fixed ahead—on the door, on what waited beyond it.

Tempest said nothing as he pushed it open. The hinges groaned, the stale air inside rolling over them like a breath from something long asleep. Johnny’s optics adjusted instantly, sharpening against the dim glow of a single flickering light. Junebug was there.

She was slumped against a workbench, one wrist bound to it by a heavy, industrial chain. Her frame sagged with forced stillness, but she was upright—awake. Her optics, dulled but still burning blue, flicked up at the sound of movement.

Johnny felt something in his chest tighten.

“June,” he breathed She blinked sluggishly, recognition taking a second too long to register. When it did, something in her posture shifted—too slight for anyone but him to notice.

Behind him, Tempest gave a small, humorless chuckle. “See? She’s fine.”

Johnny barely heard him. He stepped forward, only to be yanked back by the chain.

Junebug’s optics flickered toward the movement. She took in the restraint, the way Johnny was being escorted rather than simply let in. Her jaw tightened.

“I’m fine,” she said, voice rough.

A lie. Johnny exhaled sharply, forcing down the growing coil of frustration in his gut. “They hurt you?”

Junebug’s fingers twitched against the workbench, but she didn’t answer. That was answer enough.

Tempest made a low, amused sound. “Alright, alright,” he drawled. “You’ve had your moment. Time’s up.”

Johnny turned sharply, yanking against the chain as he faced Tempest. “Give us a minute.”

Tempest just smiled, a slow, deliberate thing. “I did.”

Before Johnny could argue, the chain was pulled tight, jerking him backward toward the door.

Junebug moved then—just a fraction, just enough to lean forward against the restraint on her wrist. “Johnny—”

He twisted against Tempest’s grip, trying to hold onto the sight of her, trying to carve her into memory before they forced him away.

“June—”

The door slammed shut between them.

⚙︎・•● ● •・⚙︎ The door’s heavy slam sent a dull vibration through the workshop, rattling loose screws on the workbench. The sound lingered in the air, stretching thin over the quiet hum of the overhead lights.

Junebug sat motionless, staring at the door as if sheer will alone could pull Johnny back through it.

Then she moved.

Her fingers curled into a slow, deliberate fist, servos whirring as she tested the strength left in her limbs. The chain around her wrist was thick—industrial-grade, bolted into the workbench like a part of the machinery itself. She gave it an experimental pull, teeth grinding as the restraint barely shifted.

Her optics flickered toward the room. It was almost her old workshop—the same sterile walls, the same workbenches lined with half-finished projects and scattered tools. But it wasn’t hers.

Not anymore.

A slow, controlled breath. Her systems ran a diagnostic, struggling to recalibrate after the EMP had left her sluggish and off-balance. The echo of Johnny’s voice lingered in her audio logs, overlapping with the last time she’d seen him before everything went black.

June—

She flexed her fingers again, eyes narrowing.

They’d brought her here for a reason.

They weren’t done with her yet.

[avocodedigital]

Physics-AI Synergy - Key to Nobel Prize

Join the newsletter: https://avocode.digital/newsletter/

The Convergence of Physics and AI

In recent years, the interplay between physics and artificial intelligence (AI) has gained considerable attention. This dynamic synergy has not only revolutionized these fields but also led to significant breakthroughs that were recognized in this year's Nobel Prize. But how did the convergence of physics and AI come about? What innovations emerged from this fusion, and why is it pivotal to today's advancements?

The Origins of Physics-AI Synergy

The relationship between physics and AI dates back to when scientists first began to use computational models to solve complex physical equations. The development of these algorithms provided critical insights into various physical phenomena and also set the stage for the AI revolution. Key developments in this subfield include:

The use of machine learning to predict outcomes in quantum physics, where traditional calculations proved too resource-intensive.

Development of neural networks that can simulate the behavior of physical systems under diverse conditions.

Use of AI to optimize experimental procedures, saving both time and resources in laboratories worldwide.

These early efforts laid the groundwork for the sophisticated AI models we see today, many of which are increasingly being used to tackle deeply rooted physics problems.

Breakthrough Technologies

**Leveraging AI in Physics:** Physics has traditionally been a data-rich domain. From particle physics experiments to astronomical observations, physicists deal with enormous datasets that can be overwhelming to process using conventional methods. Here is where AI comes into play. AI algorithms, especially machine learning models, have proven exceptionally proficient at identifying patterns and making predictions from large data sets. The recent Nobel Prize recognized these pivotal breakthroughs:

Quantum Computing and AI: AI-driven models have dramatically accelerated quantum computing research. These algorithms have improved the accuracy of quantum simulations and optimized quantum circuit designs, crucial for the development of quantum computers.

Materials Discovery: AI has facilitated the discovery of new materials by predicting the properties of unknown compounds, significantly speeding up the process from hypothesis to realization.

Cosmology: AI applications in cosmology include mapping the universe, analyzing cosmic microwave background data, and simulating galaxy formations, enhancing our understanding of the cosmos.

Implications for Science and Society

The implications of the physics-AI partnership extend beyond scientific discovery. This synergy not only enhances our comprehension of the physical universe but also fuels societal advancements in technology and healthcare. In Technology: The integration of AI into physics research has spurred advancements in developing technologies such as improved sensor systems, better imaging technologies, and more efficient energy resources, each with significant societal impacts. In Healthcare: AI algorithms that originated in physical sciences are now applied in medical diagnostics, predicting patient outcomes, and personalizing treatment plans. This cross-disciplinary approach is leading to more effective healthcare solutions and improving patient care.

The Path to Nobel Prize Accolades

The Nobel Prize reflects humanity's crowning achievements in various disciplines, and the recognition of the physics-AI synergy underscores its significance. This collaboration embodies a shift toward interdisciplinary innovation, proving that major scientific breakthroughs often arise when diverse areas of expertise coalesce. This year's Nobel winners demonstrated how this fusion has led to transformative applications:

AI-driven Nobel Achievements: By merging AI techniques with classical physics, researchers have pioneered methods that challenge conventional approaches and redefine scientific boundaries.

A Catalyst for Future Discoveries: The acknowledged work acts as a catalyst, encouraging young researchers to explore interdisciplinary studies where AI and physics intersect, fostering an environment ripe for future breakthroughs.

Looking Towards the Future

Physics-AI synergy marks only the beginning of a broader trend of interdisciplinary research, with the potential to solve complex challenges facing humanity. The ongoing development of AI algorithms tailored specifically for physics applications continues to push the envelope, offering solutions that might one day address global issues ranging from climate change to artificial general intelligence. Continued Innovations: As researchers continue to refine AI models and integrate them into physics' nuanced world, we can expect:

**Deeper Understanding of Physical Laws:** AI will likely help uncover new insights into the fundamental laws governing our universe.

**Advanced Computational Models:** Enhanced models that can simulate previously unsolvable problems, providing new avenues for experimental validation.

**Bridging Gaps in Interdisciplinary Research:** A foundation for further crossover between other scientific disciplines, fostering a more cohesive scientific community.

The fusion of physics and AI is a testament to the power of merging diverse fields to achieve remarkable progress. This year's Nobel recognition is not just an accolade for specific achievements but a herald of the endless possibilities that lie in the confluence of science and technology. As we continue to explore these frontiers, the future looks increasingly promising, teeming with opportunities for discovery and advancement. Want more? Join the newsletter: https://avocode.digital/newsletter/

[sunaleisocial]

To understand cognition — and its dysfunction — neuroscientists must learn its rhythms

New Post has been published on https://sunalei.org/news/to-understand-cognition-and-its-dysfunction-neuroscientists-must-learn-its-rhythms/

To understand cognition — and its dysfunction — neuroscientists must learn its rhythms

It could be very informative to observe the pixels on your phone under a microscope, but not if your goal is to understand what a whole video on the screen shows. Cognition is much the same kind of emergent property in the brain. It can only be understood by observing how millions of cells act in coordination, argues a trio of MIT neuroscientists. In a new article, they lay out a framework for understanding how thought arises from the coordination of neural activity driven by oscillating electric fields — also known as brain “waves” or “rhythms.”

Historically dismissed solely as byproducts of neural activity, brain rhythms are actually critical for organizing it, write Picower Professor Earl Miller and research scientists Scott Brincat and Jefferson Roy in Current Opinion in Behavioral Science. And while neuroscientists have gained tremendous knowledge from studying how individual brain cells connect and how and when they emit “spikes” to send impulses through specific circuits, there is also a need to appreciate and apply new concepts at the brain rhythm scale, which can span individual, or even multiple, brain regions.

“Spiking and anatomy are important, but there is more going on in the brain above and beyond that,” says senior author Miller, a faculty member in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. “There’s a whole lot of functionality taking place at a higher level, especially cognition.”

The stakes of studying the brain at that scale, the authors write, might not only include understanding healthy higher-level function but also how those functions become disrupted in disease.

“Many neurological and psychiatric disorders, such as schizophrenia, epilepsy, and Parkinson’s, involve disruption of emergent properties like neural synchrony,” they write. “We anticipate that understanding how to interpret and interface with these emergent properties will be critical for developing effective treatments as well as understanding cognition.”

The emergence of thoughts

The bridge between the scale of individual neurons and the broader-scale coordination of many cells is founded on electric fields, the researchers write. Via a phenomenon called “ephaptic coupling,” the electrical field generated by the activity of a neuron can influence the voltage of neighboring neurons, creating an alignment among them. In this way, electric fields both reflect neural activity and also influence it. In a paper in 2022, Miller and colleagues showed via experiments and computational modeling that the information encoded in the electric fields generated by ensembles of neurons can be read out more reliably than the information encoded by the spikes of individual cells. In 2023 Miller’s lab provided evidence that rhythmic electrical fields may coordinate memories between regions.

At this larger scale, in which rhythmic electric fields carry information between brain regions, Miller’s lab has published numerous studies showing that lower-frequency rhythms in the so-called “beta” band originate in deeper layers of the brain’s cortex and appear to regulate the power of faster-frequency “gamma” rhythms in more superficial layers. By recording neural activity in the brains of animals engaged in working memory games, the lab has shown that beta rhythms carry “top-down” signals to control when and where gamma rhythms can encode sensory information, such as the images that the animals need to remember in the game.

Some of the lab’s latest evidence suggests that beta rhythms apply this control of cognitive processes to physical patches of the cortex, essentially acting like stencils that pattern where and when gamma can encode sensory information into memory, or retrieve it. According to this theory, which Miller calls “Spatial Computing,” beta can thereby establish the general rules of a task (for instance, the back-and-forth turns required to open a combination lock), even as the specific information content may change (for instance, new numbers when the combination changes). More generally, this structure also enables neurons to flexibly encode more than one kind of information at a time, the authors write, a widely observed neural property called “mixed selectivity.” For instance, a neuron encoding a number of the lock combination can also be assigned, based on which beta-stenciled patch it is in, the particular step of the unlocking process that the number matters for.

In the new study, Miller, Brincat, and Roy suggest another advantage consistent with cognitive control being based on an interplay of large-scale coordinated rhythmic activity: “subspace coding.” This idea postulates that brain rhythms organize the otherwise massive number of possible outcomes that could result from, say, 1,000 neurons engaging in independent spiking activity. Instead of all the many combinatorial possibilities, many fewer “subspaces” of activity actually arise, because neurons are coordinated, rather than independent. It is as if the spiking of neurons is like a flock of birds coordinating their movements. Different phases and frequencies of brain rhythms provide this coordination, aligned to amplify each other, or offset to prevent interference. For instance, if a piece of sensory information needs to be remembered, neural activity representing it can be protected from interference when new sensory information is perceived.

“Thus the organization of neural responses into subspaces can both segregate and integrate information,” the authors write.

The power of brain rhythms to coordinate and organize information processing in the brain is what enables functional cognition to emerge at that scale, the authors write. Understanding cognition in the brain, therefore, requires studying rhythms.

“Studying individual neural components in isolation — individual neurons and synapses — has made enormous contributions to our understanding of the brain and remains important,” the authors conclude. “However, it’s becoming increasingly clear that, to fully capture the brain’s complexity, those components must be analyzed in concert to identify, study, and relate their emergent properties.”

[jcmarchi]

To understand cognition — and its dysfunction — neuroscientists must learn its rhythms

New Post has been published on https://thedigitalinsider.com/to-understand-cognition-and-its-dysfunction-neuroscientists-must-learn-its-rhythms/

To understand cognition — and its dysfunction — neuroscientists must learn its rhythms

It could be very informative to observe the pixels on your phone under a microscope, but not if your goal is to understand what a whole video on the screen shows. Cognition is much the same kind of emergent property in the brain. It can only be understood by observing how millions of cells act in coordination, argues a trio of MIT neuroscientists. In a new article, they lay out a framework for understanding how thought arises from the coordination of neural activity driven by oscillating electric fields — also known as brain “waves” or “rhythms.”

Historically dismissed solely as byproducts of neural activity, brain rhythms are actually critical for organizing it, write Picower Professor Earl Miller and research scientists Scott Brincat and Jefferson Roy in Current Opinion in Behavioral Science. And while neuroscientists have gained tremendous knowledge from studying how individual brain cells connect and how and when they emit “spikes” to send impulses through specific circuits, there is also a need to appreciate and apply new concepts at the brain rhythm scale, which can span individual, or even multiple, brain regions.

“Spiking and anatomy are important, but there is more going on in the brain above and beyond that,” says senior author Miller, a faculty member in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. “There’s a whole lot of functionality taking place at a higher level, especially cognition.”

The stakes of studying the brain at that scale, the authors write, might not only include understanding healthy higher-level function but also how those functions become disrupted in disease.

“Many neurological and psychiatric disorders, such as schizophrenia, epilepsy, and Parkinson’s, involve disruption of emergent properties like neural synchrony,” they write. “We anticipate that understanding how to interpret and interface with these emergent properties will be critical for developing effective treatments as well as understanding cognition.”

The emergence of thoughts

The bridge between the scale of individual neurons and the broader-scale coordination of many cells is founded on electric fields, the researchers write. Via a phenomenon called “ephaptic coupling,” the electrical field generated by the activity of a neuron can influence the voltage of neighboring neurons, creating an alignment among them. In this way, electric fields both reflect neural activity and also influence it. In a paper in 2022, Miller and colleagues showed via experiments and computational modeling that the information encoded in the electric fields generated by ensembles of neurons can be read out more reliably than the information encoded by the spikes of individual cells. In 2023 Miller’s lab provided evidence that rhythmic electrical fields may coordinate memories between regions.

At this larger scale, in which rhythmic electric fields carry information between brain regions, Miller’s lab has published numerous studies showing that lower-frequency rhythms in the so-called “beta” band originate in deeper layers of the brain’s cortex and appear to regulate the power of faster-frequency “gamma” rhythms in more superficial layers. By recording neural activity in the brains of animals engaged in working memory games, the lab has shown that beta rhythms carry “top-down” signals to control when and where gamma rhythms can encode sensory information, such as the images that the animals need to remember in the game.

Some of the lab’s latest evidence suggests that beta rhythms apply this control of cognitive processes to physical patches of the cortex, essentially acting like stencils that pattern where and when gamma can encode sensory information into memory, or retrieve it. According to this theory, which Miller calls “Spatial Computing,” beta can thereby establish the general rules of a task (for instance, the back-and-forth turns required to open a combination lock), even as the specific information content may change (for instance, new numbers when the combination changes). More generally, this structure also enables neurons to flexibly encode more than one kind of information at a time, the authors write, a widely observed neural property called “mixed selectivity.” For instance, a neuron encoding a number of the lock combination can also be assigned, based on which beta-stenciled patch it is in, the particular step of the unlocking process that the number matters for.

In the new study, Miller, Brincat, and Roy suggest another advantage consistent with cognitive control being based on an interplay of large-scale coordinated rhythmic activity: “subspace coding.” This idea postulates that brain rhythms organize the otherwise massive number of possible outcomes that could result from, say, 1,000 neurons engaging in independent spiking activity. Instead of all the many combinatorial possibilities, many fewer “subspaces” of activity actually arise, because neurons are coordinated, rather than independent. It is as if the spiking of neurons is like a flock of birds coordinating their movements. Different phases and frequencies of brain rhythms provide this coordination, aligned to amplify each other, or offset to prevent interference. For instance, if a piece of sensory information needs to be remembered, neural activity representing it can be protected from interference when new sensory information is perceived.

“Thus the organization of neural responses into subspaces can both segregate and integrate information,” the authors write.

The power of brain rhythms to coordinate and organize information processing in the brain is what enables functional cognition to emerge at that scale, the authors write. Understanding cognition in the brain, therefore, requires studying rhythms.

“Studying individual neural components in isolation — individual neurons and synapses — has made enormous contributions to our understanding of the brain and remains important,” the authors conclude. “However, it’s becoming increasingly clear that, to fully capture the brain’s complexity, those components must be analyzed in concert to identify, study, and relate their emergent properties.”

[mindlibertas]

Ketamine For Depression: A Breakthrough Treatment Transforming Mental Health Care

In the realm of mental health treatment, the search for effective remedies has been an ongoing journey fraught with challenges. Depression, in particular, has remained a formidable adversary, affecting millions worldwide with its debilitating grip on the mind and spirit. Conventional therapies such as antidepressants and therapy have provided relief for many, but for some, these approaches prove inadequate, leaving them in a relentless struggle against their inner demons. However, amidst this landscape of despair, a glimmer of hope emerges in the form of ketamine—a drug once primarily known for its anesthetic properties but now increasingly recognized for its remarkable potential in treating depression.

Ketamine, originally developed as an anesthetic in the 1960s, gained widespread popularity in medical circles for its rapid onset and short duration of action. However, it wasn't until several decades later that researchers began to uncover its unexpected antidepressant effects. Studies conducted in the early 2000s revealed that low doses of ketamine administered intravenously could induce a rapid and significant alleviation of depressive symptoms, even in individuals who had previously been resistant to other treatments.

This discovery sparked a paradigm shift in the field of psychiatry, leading to extensive research into ketamine's mechanisms of action and its potential as a novel treatment for depression. Unlike traditional antidepressants, which primarily target neurotransmitters such as serotonin and norepinephrine, ketamine acts on the brain in a different manner, specifically targeting the glutamate system. By blocking the N-methyl-D-aspartate (NMDA) receptor, ketamine helps restore synaptic connections in neural circuits that may be disrupted in depression, leading to a rapid improvement in mood and cognition.

As the body of evidence supporting ketamine's efficacy continued to grow, the medical community began to explore different routes of administration to make this treatment more accessible to patients. While intravenous infusion remains the most common method, researchers have also investigated intranasal and oral formulations of ketamine, with promising results. These alternative delivery methods offer the potential for outpatient treatment, reducing the burden on healthcare facilities and expanding access to ketamine therapy for those in need.

In 2019, the U.S. Food and Drug Administration (FDA) granted approval for esketamine, a nasal spray formulation of ketamine, for the treatment of treatment-resistant depression. Marketed under the brand name Spravato, this groundbreaking approval marked the first new class of antidepressant medication to be approved in decades, underscoring the transformative potential of ketamine in mental health care. Since then, clinics specializing in ketamine therapy have emerged across the country, offering hope to individuals who have exhausted other treatment options.

One such pioneer in the field is Ketamine For Depression, a leading provider of ketamine infusion therapy dedicated to helping patients find relief from the burdens of depression. Founded by Dr. Emily Carter, a psychiatrist with a passion for exploring innovative treatments, Ketamine For Depression has quickly earned a reputation for excellence in the field of ketamine therapy. With a team of experienced clinicians and a patient-centered approach, the clinic offers personalized treatment plans tailored to each individual's unique needs, ensuring the highest standard of care and support throughout the healing journey.

At Ketamine For Depression, the focus extends beyond symptom relief to address the underlying factors contributing to depression, such as trauma, stress, and interpersonal conflicts. Through a combination of ketamine infusions and therapeutic interventions, patients are guided towards a deeper understanding of their emotions and experiences, empowering them to cultivate resilience and reclaim their lives from the grip of depression. The clinic's holistic approach emphasizes the integration of mind, body, and spirit, recognizing the interconnectedness of mental health and overall well-being.

One of the key advantages of ketamine therapy is its rapid onset of action, with many patients experiencing significant improvement in symptoms within hours or days of treatment. This quick response can be life-changing for individuals who have endured years of suffering, offering newfound hope and optimism for the future. Moreover, ketamine has been shown to be effective not only in treatment-resistant depression but also in other mood disorders such as bipolar depression and post-traumatic stress disorder (PTSD), further expanding its potential impact on mental health care.

Despite its promising benefits, ketamine therapy is not without challenges and considerations. Like any medication, ketamine carries potential risks and side effects, including dissociation, hallucinations, and elevated blood pressure. Additionally, the long-term effects of ketamine treatment are still being studied, and ongoing research is needed to fully understand its safety profile and optimal dosing strategies. As such, close monitoring by qualified healthcare professionals is essential to ensure the safe and effective use of ketamine in clinical practice.

Looking ahead, the future of ketamine therapy holds immense promise for revolutionizing the treatment of depression and other mood disorders. Continued research and innovation will further refine our understanding of ketamine's mechanisms of action and its potential applications in mental health care. Moreover, efforts to enhance accessibility and affordability will help ensure that ketamine therapy reaches all those who stand to benefit from its transformative effects.

In conclusion, Ketamine For Depression stands at the forefront of a new era in mental health care, where hope shines brightly amidst the darkness of depression. Through compassionate care, innovative treatments, and a commitment to healing, Ketamine For Depression is helping individuals rediscover joy, resilience, and meaning in their lives. As we journey forward, guided by the light of scientific discovery and human compassion, we move ever closer to a world where depression is no longer an insurmountable obstacle but a challenge we can overcome together.

[jhavelikes]

In most species, interval timing is time-scale invariant: errors in time estimation scale up linearly with the estimated duration. In mammals, time-scale invariance is ubiquitous over behavioral, lesion, and pharmacological manipulations. For example, dopaminergic drugs induce an immediate, whereas cholinergic drugs induce a gradual, scalar change in timing. Behavioral theories posit that time-scale invariance derives from particular computations, rules, or coding schemes. In contrast, we discuss a simple neural circuit, the perceptron, whose output neurons fire in a clockwise fashion (interval timing) based on the pattern of coincidental activation of its input neurons. We show numerically that time-scale invariance emerges spontaneously in a perceptron with realistic neurons, in the presence of noise. Under the assumption that dopaminergic drugs modulate the firing of input neurons, and that cholinergic drugs modulate the memory representation of the criterion time, we show that a perceptron with realistic neurons reproduces the pharmacological clock and memory patterns, and their time-scale invariance, in the presence of noise. These results suggest that rather than being a signature of higher-order cognitive processes or specific computations related to timing, time-scale invariance may spontaneously emerge in a massively-connected brain from the intrinsic noise of neurons and circuits, thus providing the simplest explanation for the ubiquity of scale invariance of interval timing.

Time-scale invariance as an emergent property in a perceptron with realistic, noisy neurons - PMC

[kosheeka]

Human Neural Progenitor Cells

In recent years, researchers and scientists have been relentlessly exploring the intricate mechanisms of the brain, hoping to uncover its mysteries and develop novel approaches to treating neurological disorders. One of the most fascinating aspects of this research involves neural progenitor cells (NPCs), a particular group of cells with the extraordinary ability to self-renew and differentiate into various types of brain cells. 

Neural progenitor cells are a unique type of stem cells found in the central nervous system (CNS). They play a crucial role during the embryonic development of the brain and continue to exist in limited quantities within specific adult brain regions. NPCs can differentiate into neurons, astrocytes, and oligodendrocytes—the three main types of brain cells. This versatility makes them an invaluable resource in regenerative medicine and neurobiology research.

Regeneration and Repair

The regenerative potential of NPCs has piqued the interest of scientists worldwide. Harnessing this potential holds immense promise for repairing and regenerating damaged brain tissue resulting from trauma, neurodegenerative diseases, and stroke. By stimulating NPCs in the brain, researchers aim to enhance neurogenesis—the process of generating new neurons. Studies have shown that neuron progenitor cells can migrate to the site of injury, differentiate into neurons, and integrate into existing neural networks, fostering the repair of damaged brain circuits. This therapeutic approach may revolutionise the treatment of conditions such as Parkinson's disease, Alzheimer's disease, and spinal cord injuries.

Neurodevelopmental Disorders

Neurodevelopmental disorders like autism spectrum disorder (ASD) and intellectual disabilities have long been the subject of intense research. Neural progenitor cells offer a unique platform to study the cellular and molecular processes contributing to these disorders. By examining the behaviour and development of NPCs derived from individuals with neurodevelopmental disorders, researchers gain insights into the underlying pathophysiology and potential targets for therapeutic interventions. Such studies have the potential to pave the way for personalized medicine approaches tailored to individual patients.

Advancing Drug Discovery

Traditional drug discovery methods are often time-consuming and costly. Neural progenitor cells provide an exciting avenue for more efficient drug development. NPCs can be generated in the laboratory from induced pluripotent stem cells (iPSCs), derived from adult dermal cells or blood samples. These iPSC-derived NPCs mimic the properties of native NPCs and offer a renewable source of brain cells for drug testing. By exposing NPCs to various compounds, researchers can evaluate their effects on cell survival, differentiation, and functionality. This streamlined approach accelerates the identification and validation of potential drug candidates, bringing us closer to effective treatments for neurological disorders.

Neural Progenitor Cells and Brain Machine Interfaces

The emergence of brain-machine interfaces (BMIs) has opened up exciting possibilities for the field of neural engineering. Neural progenitor cells can be integrated with BMIs to create a seamless interface between the brain and external devices. By incorporating NPCs into the implantable electrodes of BMIs, researchers aim to improve the integration and longevity of these devices, potentially enhancing their performance and reducing the risk of adverse reactions. This integration holds great promise for individuals with motor impairments, as it could enable them to control prosthetic limbs or communicate directly with computers using their thoughts.

Neural progenitor cells represent a captivating frontier in neuroscience, offering tremendous potential for regenerative medicine, the study of neurodevelopmental disorders, drug discovery, and brain-machine interfaces. The ability of NPCs to self-renew and differentiate into various types of brain cells ignites hope for revolutionary advancements in the treatment of neurological disorders and the understanding of brain function. As our understanding of these remarkable cells deepens, we inch closer to a future where the full regenerative potential of the brain can be harnessed, transforming the lives of millions worldwide.

[immunity-slacker]

@creepy-queer“Why inherently biological? Like due to the level of complexity that would be nearly impossible to build otherwise?”

Yeah this is partially what I mean—the idea that a neural network is meaningfully “similar to the brain” in structure and function is lacking in strong evidence imo. Like, sure, maybe they’re a little similar! Just like the brain is a little similar to a CPU, to an electrical circuit, to a steam engine, all the way back to when people started using modern technology as a metaphor to understand the mind. I also think it’s possible that consciousness is an emergent property of biological brains so that even if we could build a network with the same structure somehow, it wouldn’t necessarily be sentient.

My hottest take about AI is that proponents of AI consciousness have completely refused to engage with the possibility that consciousness is inherently biological. I do believe this but I’m open to being wrong about it. I think it’s the easiest answer to this question but it gets no airtime; no one seriously considers it.

[geometrymatters]

Scientists Sue Yeon Chung and L.F. Abbott from Columbia University developed an approach for understanding neural networks, by analyzing the geometric properties of neural populations and understanding how information is embedded and processed through high-dimensional representations to solve complex tasks. When a group of neurons demonstrates variability in response to stimuli or through internal recurrent dynamics, manifold-like representations emerge.

In their work, the team highlights the important examples of how geometrical techniques and the insights they provide have aided the understanding of biological and artificial neural networks.

They investigate the geometry of these high-dimensional representations, i.e., neuronal population geometry, using mathematical and computational tools. Their review includes a variety of geometrical approaches that provide insight into the function of the networks.

An important aspect of their research is the insight that dividing sets of neural population activities is more difficult when their representation patterns are on a curved surface, instead of a linear one – a concept that plays an important role in the analysis of neural population geometry and goes back to the beginnings of artificial neural networks.

Continue Reading

[compneuropapers]

Interesting Papers for Week 36, 2022

Coding of chromatic spatial contrast by macaque V1 neurons. De, A., & Horwitz, G. D. (2022). eLife, 11, e68133.

Neural Encoding of Active Multi-Sensing Enhances Perceptual Decision-Making via a Synergistic Cross-Modal Interaction. Delis, I., Ince, R. A. A., Sajda, P., & Wang, Q. (2022). Journal of Neuroscience, 42(11), 2344–2355.

Brain-like functional specialization emerges spontaneously in deep neural networks. Dobs, K., Martinez, J., Kell, A. J. E., & Kanwisher, N. (2022). Science Advances, 8(11), eabl8913.

Stimulus dependence of directed information exchange between cortical layers in macaque V1. Gieselmann, M. A., & Thiele, A. (2022). eLife, 11, e62949.

Acceleration of information processing en route to perceptual awareness in infancy. Hochmann, J.-R., & Kouider, S. (2022). Current Biology, 32(5), 1206-1210.e3.

Complementary roles of serotonergic and cholinergic systems in decisions about when to act. Khalighinejad, N., Manohar, S., Husain, M., & Rushworth, M. F. S. (2022). Current Biology, 32(5), 1150-1162.e7.

Cerebellar modulation of memory encoding in the periaqueductal grey and fear behaviour. Lawrenson, C., Paci, E., Pickford, J., Drake, R. A., Lumb, B. M., & Apps, R. (2022). eLife, 11, e76278.

The role of state uncertainty in the dynamics of dopamine. Mikhael, J. G., Kim, H. R., Uchida, N., & Gershman, S. J. (2022). Current Biology, 32(5), 1077-1087.e9.

Neurons in the Monkey’s Subthalamic Nucleus Differentially Encode Motivation and Effort. Nougaret, S., Baunez, C., & Ravel, S. (2022). Journal of Neuroscience, 42(12), 2539–2551.

Compartment-specific tuning of dendritic feature selectivity by intracellular Ca 2+ release. O’Hare, J. K., Gonzalez, K. C., Herrlinger, S. A., Hirabayashi, Y., Hewitt, V. L., Blockus, H., … Losonczy, A. (2022). Science, 375(6586).

Closed-loop neuromodulation for studying spontaneous activity and causality. Ramot, M., & Martin, A. (2022). Trends in Cognitive Sciences, 26(4), 290–299.

Adaptive Learning through Temporal Dynamics of State Representation. Razmi, N., & Nassar, M. R. (2022). Journal of Neuroscience, 42(12), 2524–2538.

Reorganization of CA1 dendritic dynamics by hippocampal sharp-wave ripples during learning. Rolotti, S. V., Blockus, H., Sparks, F. T., Priestley, J. B., & Losonczy, A. (2022). Neuron, 110(6), 977-991.e4.

Statistical learning as a reference point for memory distortions: Swap and shift errors. Scotti, P. S., Hong, Y., Golomb, J. D., & Leber, A. B. (2021). Attention, Perception, & Psychophysics, 83(4), 1652–1672.

Humans perseverate on punishment avoidance goals in multigoal reinforcement learning. Sharp, P. B., Russek, E. M., Huys, Q. J., Dolan, R. J., & Eldar, E. (2022). eLife, 11, e74402.

Saccade-related neural communication in the human medial temporal lobe is modulated by the social relevance of stimuli. Staudigl, T., Minxha, J., Mamelak, A. N., Gothard, K. M., & Rutishauser, U. (2022). Science Advances, 8(11), eabl6037.

Fast and slow feedforward inhibitory circuits for cortical odor processing. Suzuki, N., Tantirigama, M. L., Aung, K. P., Huang, H. H., & Bekkers, J. M. (2022). eLife, 11, e73406.

Deep brain stimulation of the thalamus restores signatures of consciousness in a nonhuman primate model. Tasserie, J., Uhrig, L., Sitt, J. D., Manasova, D., Dupont, M., Dehaene, S., & Jarraya, B. (2022). Science Advances, 8(11), eabl5547.

Striatal dopamine signals are region specific and temporally stable across action-sequence habit formation. van Elzelingen, W., Warnaar, P., Matos, J., Bastet, W., Jonkman, R., Smulders, D., … Willuhn, I. (2022). Current Biology, 32(5), 1163-1174.e6.

Directional Tuning of Phase Precession Properties in the Hippocampus. Yiu, Y.-H., Leutgeb, J. K., & Leibold, C. (2022). Journal of Neuroscience, 42(11), 2282–2297.

[luxe-pauvre]

These discoveries have led some researchers to suggest that the neuron doctrine is not adequate for understanding the complexity of brains, and that unknown collective properties that emerge from the activities of groups of neurons - integrative emergents, in the jargon - may turn out to be significant. As a group of leading neuroscientists put it in 2005: 'the complexity of the human brain and likely other regions of the nervous system derive from some organisational features that make use of the permutations of scores of integrative variables and thousands or millions of connectivity variables and perhaps integrative emergents yet to be discovered. The answers extend well beyond explanation by the neuron acting as a single functional unit.' Ten years later, Rafael Yuste of Columbia University similarly argued that the neuron doctrine is now being if not surpassed, at least supplemented. Many parts of the brain appear to be organised in networks, such as the sets of inhibitory neurons that, as Yuste put it, 'are often linked to each other by gap junctions, as though they are designed to work as a unit', and the ability of some inhibitory neurons to broadcast their neurotransmitters into tissues, rather than simply releasing them into a synapse, suggests they 'appear to be designed to extend a "blanket of inhibition" onto excitatory cells'. [...] Although Yuste argued for the need to develop a theory of how neural circuits operated, like his predecessors he was unclear as to what the next steps forward might be, beyond insisting that it would not be enough to simply record from lots of networks and expect an explanation to emerge. Instead, it might be necessary to take account of all levels of the system - from the molecular, through the activity of single cells, to the network behaviour and the behavioural output - in order to fully understand. Although this is probably correct, there is little detail here. This is not Yuste's fault - this inability to go beyond general principles is typical of current thinking about how the brain works: we do not have an appropriate theoretical framework, nor the experimental evidence that could point to an answer. We cannot yet see how to take the next step.

Matthew Cobb, The Idea of the Brain

[wisdomrays]

TAFAKKUR: Part 183

From Mexican Jumping Beans to Cyborg Plants

According to Merriam Webster, cybernetics is the science of communication and control theory that is particularly concerned with the comparative study of automatic control systems, such as the nervous system, brain and mechanical-electrical communication systems). The root of cybernetics comes from Greek word “kybernētēs,” which means pilot or governor (from kybernan, which means to steer or govern). A cyborg is a cybernetic organism with both organic and cybernetic parts. We are very familiar with this term due to captivating stories of cyborgs in science fiction movies and books. Darth Vader, Robocop, Terminator, Inspector Gadget, and The Six Million Dollar Man are some of the most famous fictional cyborgs. However, cyborgs can also be plants and are not as well-known as the fictional characters on television.

In recent years scientists have taken huge steps towards the bio-hybrid architecture developed for exploring an alternate approach to the control of autonomous robots. The plant-robot interactions through cyborg plants have been investigated in an effort to apply lessons from plants to robots, which provided another role for these organisms other than being a food source or decoration items. There are several joint experimental, numerical and robotic studies conducted in this newly developed area. One of the examples includes a flower robot made by Korean engineers which has the appearance of a common flower with petals, stem and leaves. The flower robot has sensing ability, moving mechanism, and home appliance function. It can recognize environmental conditions such as room temperature, pressure, voice and light intensity and can imitate the blooming of a flower, the bending of the stem and the stirring of the leaves in the wind. Other than these, the flower robot functions as a humidifier, a vision/voice recording system and an illumination device. For example, when flower robot receives light, it senses the intensity of the light and blooms. On the contrary, when it is dark, as the flower robot starts fading away and its illumination device turns on to flash the room.

Plantas nomadas, made by Mexican artist Gilberto Espaza, is another example of cyborg plants. It uses dirty water to live. It is a miniature eco-system consisting of plants and micro-organisms within a robotic shell. Each of the components symbiotically relies on the others: the plant provides the perfect environment for the microbe, and the microbe (in a microbial fuel cell) transforms nutrients in dirty water into energy to power the robotic components, and the robotic components provide mobility.

A team from Switzerland has been working on a project that endows a robot with the ability to react in response to environmental stress of a plant in order to maintain the state of the plant. The robotic devices monitor the changes in morphology and electrical activity of the avocado plants. According to these parameters, it classifies the drought level and triggers irrigation when necessary.

Some of the artists like James Stone, who is a Media Artist specializing in digital technologies and fabrication, are interested in seeing if plants are prone to act in certain ways, show preference and possibly display other traits such as emotion. Artists are specifically curious as to what would happen when a plant is augmented with technology but also given full control over such technology to do with it whatever it chooses. To see the results of such systems that provides a means for the plant to interact with people or things will surely be fascinating. A study in this line of research is done by a group of researchers in mobile robotics at ETH Zürich, whose long-term research goal is also to bestow machines with the ability to gain and employ knowledge from the universe to improve their intelligence, by building a prototype called iRobot Create. This cyborg plant consists of a computer running Linux, a normal plant and additional sensors and lives its own life, following its internal needs of water, sunlight and electrical energy. The cyborg stays away from obstacles using ultrasonic sensors, finds the best light spot using light sensors and goes to a recharge and to a mock-up water station using iRobot's infrared sensor. Moreover, its sensors pick up noise caused by people moving around nearby, allowing cyborg plant to react by moving out of the way, to prevent themselves from getting underfoot.

It is very important to improve the ability of robots to work successfully in a complex and harsh environment, which would increase their usages. In one of those efforts exploring the use of biological systems to control robots under changing environmental conditions, Dr. David Hu and his group from Georgia Institute of Technology used the Mexican jumping bean, Laspeyresia saltitans, which consists of an empty seed housing a moth larva. Heating by the sun stimulates movements by the larva which rolls, jumps and flips by the bean. They explored this unique means of rolling locomotion and recorded bean trajectories across a series of terrain types, including one-dimensional channels and planar surfaces of varying inclination by Time-lapse videography. They found that the shell encumbers the larva's locomotion, decreasing its speed on flat surfaces by three-fold. Interestingly, they also showed that the two-dimensional search algorithm of the bean resembles the run-and-tumble search of bacteria. When they tested this search algorithm using both an agent-based simulation and a wheeled Scribbler robot, they demonstrated that the algorithm succeeds in propelling the robot away from regions of high temperature. It is amazing that from a study that involves a plant seed, a moth larva and a robot, scientists may develop applications in biomimetic micro-scale navigation systems.

The hi-tech devices that have been inspired by biological systems are not limited by the ones stimulated with plants. The insect world also represents a huge and original database for future bio-inspired systems, vehicles, and micro-vehicles . For example, the process of motion detection system in the fly’s eye is a good example of a neural circuit that was used for robot automatic piloting. Recently, a novel bat-like unmanned aerial vehicle inspired by the morphing-wing mechanism of bats has been presented. Other than that, body undulation used by snakes and the physical structure of the body of a snake may offer major advantages over typical legged or wheeled locomotion designs in certain types of scenarios, therefore a large number of research groups have developed snake-inspired robots to make use of these benefits (11). Caenorhabditis elegans, a roundworm which has similar motions with snakes but with a simpler structure, was also selected to develop a small crawling robot with a thermal shape memory alloy, a homogeneous mixture or solid solution of two or more metal, as an actuator (a type of motor for moving or controlling a mechanism or system) due to the similarities of its properties to C. elegans muscles.

Not only multicellular organisms but also unicellular (single-celled) organisms are utilized for generating cyborgs; for example, scientists used circuits prepared from Physarum polycephalum, amoeboid plasmodia of the slime mold, to control an omni-directional hexapod robot. Sensory signals from the macro-physical environment of the robot are transduced to cellular scale and processed using the unique micro-physical characteristics of intracellular information processing and the response from the cellular computation is amplified to yield a macroscopic output action in the environment mediated through the robot’s actuators.

In addition, a new biorobotic system using human neuroblastoma cultures was introduced in 2011 by a Spanish engineering group. Multielectrode Arrays Setups have been designed for direct culturing neural cells over silicon or glass substrates. The main objective of this work is to run a robot using this biological neuroprocessor and the final system could be used for many things such as testing how chemicals influence the behavior of the robot.

In summary, manipulation of robots that use living organisms as an interface to perceive the environment and transfer their responses into functions seem to have endless applications as well as challenges. Biologically-inspired technologies represent an emerging and promising field of interdisciplinary areas composed of engineering, computer sciences, chemistry, biology, physics and even art. In nature there are so many living and non-living elements designed by God to help us develop and improve robots to make our lives easier, better and more productive. Even a flower can offer us with something more than color and scent, and that is if we start thinking outside the box like so many people mentioned above have done.

[candyswift-ny]

Current And Future Research Directions of Stem Cells

In recent years, research related to stem cells has created a boom in the biomedical field. Stem cells, with their advantages of easy access, low immunogenicity, and no ethical controversies, have successfully stood out in the era of cell therapy and become one of the key research directions nowadays. The development of technologies such as gene editing and stem cells surface engineering has greatly contributed to the optimization of stem cell therapy.

Up to now, several research results worldwide have confirmed that stem cell technology has immeasurable value in multiple applications such as human disease treatment, tissue engineering, and even anti-aging cosmetic industry.

Stem Cells for Disease Intervention

Among the stem cell-related research, the most anticipated one is the stem cell therapy with stem cells as the core. It operates by replacing diseased or cancerous cells in the patient's body with normal functional stem cells as the donor to achieve effective intervention in human diseases

So far, stem cell therapy has achieved great success in many clinical trials worldwide. Up to now, several clinical studies have confirmed that stem cells can effectively intervene in more than 140 diseases, including autoimmune diseases, inflammatory diseases, neurodegenerative diseases, motor neuron diseases, respiratory diseases, metabolic diseases, and cardiovascular and cerebrovascular diseases.

Stem Cells for Organ Regeneration

Stem cells have multi-directional differentiation potential, and under specific conditions, through induction, they can differentiate and regenerate into neural stem cells, liver cells, pancreatic islet cells, cardiomyocytes, and so on. This property provides a new idea for tissue engineering. The regeneration of tissues and organs using stem cells has become an important direction in tissue engineering research.

And in the past two years, many remarkable results have emerged in the regenerative medicine community. For example, in June 2021, scientists successfully cultured skin organoids using human pluripotent stem cells, forming multiple layers of skin tissue, even containing hair follicles, sebaceous glands and neural circuits, which are expected to possess the complexity and function of natural skin.

Stem Cells for Anti-aging Cosmetics

Research shows that the aging of human body is closely related to the decrease of adult stem cells. Stem cell-related anti-aging technology is a new technology to replenish the body with adult stem cells.

It is based on the mechanism of providing the body with exogenous stem cells with high vitality, allowing these cells to perform the functions of cell renewal, tissue repair and immune regulation in the body, and finally achieving anti-aging effect. In addition, studies have shown that stem cells can activate the function of epidermal cells, thus restoring skin firmness and elasticity.

It’s now been 16 years since the ability to generate iPS cells (induced pluripotent stem cells) was hailed as a game changer for regenerative medicine. Even with the challenges remaining, stem cell therapy is becoming a more tangible reality by the day. Stem cells, as the main force of the era of cell therapy, will bring disruptive changes to the field of human health and play a huge role in many research fields.

[evoldir]

Fwd: Graduate position: MaxPlanck.FishBehaviour

Begin forwarded message: > From: evoldir@evol.biology.mcmaster.ca > Subject: Graduate position: MaxPlanck.FishBehaviour > Date: 24 December 2015 at 06:31:58 GMT > To: rdmpage@gmail.com > > > The mechanisms and evolution of social influence > > Jordan Lab > > The production, perception, and cognitive processing of social cues can > have far reaching effects on the social structure and behaviour of > individuals within animal groups. Studying how the nature, frequency, > and fine-scale detail of these interactions leads to emergent > properties at the level of the collective is essential for > understanding social and collective behaviour generally. > > An aspect of social interaction that is commonly overlooked in studies > of collective systems is that interacting nodes within social networks > are not of equal status - a hierarchy exists that affects the nature > and frequency of interactions among individuals, and ultimately the > influence an individual will have in its social network. This hierarchy > may be based on size, sex, familiarity, or reputation, and has the > potential to influence numerous aspects of sociality and collective > behaviour. > > In this project, we seek to understand how relationships among group > members can mediate the flow of information within natural groups of > either Lake Tanganyikan cichlid fish or colonial spiders in Central > America. We aim to characterise social influence at numerous levels - > from behavioural interactions in the lab to studies of massive > populations in the field, examining the neurobiological basis of social > influence and socio-cognitive abilities that facilitate social > interactions. > > We seek students who wish to employ multidisciplinary approaches to > explore their own research questions around this central theme. In our > department, students have access to cutting edge digital tracking of > animal behaviour and leading molecular techniques, which can be > combined with in-depth lab and field experiments examining the adaptive > significance and mechanisms of social influence. > > Collective Animal Behaviour > > Couzin Lab > > Abstract Understanding collective action in biological processes is a > central challenge, essential for achieving progress in a variety of > fields including the coordinated communication among cells, or animals, > to the dynamics of information exchange among sophisticated organisms, > and the emergence of complex societies. Consequently the study of > collective behaviour naturally spans scales, from how neural circuits > control individual behaviour in a social context, to the analogous > issue of determining the structure and function of the communication > network among organisms that gives rise to emergent group, and > population-level, behaviour. > > We seek multiple PhD candidates to join our highly international, > collaborative and interdisciplinary research group to investigating the > behaviour and evolution of collective animal behaviour in the lab > and/or field. We are interested in both invertebrates (e.g. locusts) > and vertebrates (e.g. fish, birds) and those applicants who wish to > apply and/or develop modern technologies (e.g. in automated tracking, > virtual reality, GPS, drone-based imaging, machine learning, > neurobiology, genetics, computational modelling) to understand how > animals sense their world and make decisions in the face of uncertainty > and risk. > > Given the broad nature of this search it will be extremely helpful if > applicants can clearly state what excites them about collective animal > behaviour, and what they may want to work on. Our positions are fully > funded for 4 years to allow students time to develop their own ideas > and to follow ambitious and creative research directions. > > Collective behaviours and social structure in animal populations > > Farine Lab > > How do collective behaviours and social structure emerge in animal > populations? Seemingly simple mechanisms can often be amplified to > produce remarkable group-level behaviours or population-level patterns. > For example, highly cohesive collective movement patterns can emerge > when animals respond to the movement cues of nearby neighbours. > Similarly, groups of animals can solve complex problems, such as > sensing their environment or finding cryptic new food sources, by > eavesdropping on information being generated by nearby individuals. > While natural selection acts on the behavioural phenotypes of (often > selfish) individuals, collective behaviours are a group-level, or > sometimes population-level, property that themselves can shape > selection, and therefore form part of a complex evolutionary process. > To understand how collective behaviours evolve or social structure > emerges, one must understand (1) the mapping between individual > phenotypes and collective behaviour, (2) the link between collective > behaviour, the environment (both social and physical), and individual > fitness, and (3) how selection arising from ecological or social > conditions drives the expression of the phenotypes that are linked with > collective behaviour or particular decisions that lead to consistent > social structure. > > We are seeking one or more PhD students to join an exciting new group > investigating the ecology and evolution of social and collective animal > behaviour. The student(s) will have the opportunity to conduct > pioneering studies on both new and well-established study systems, with > a major focus on conducting observational and experimental studies in > the wild. Our current projects include whole-group high-resolution GPS > tracking, experimental manipulation of social networks, and > simultaneous tracking of predators and prey. Our strength is to > integrate technology and novel analytical techniques in studies of wild > animal groups, with a particular focus on birds. Applicants are invited > to contact Dr. Damien Farine to discuss potential project ideas or > research topics to work on. We seek highly motivated students, > particularly those with an empirical background, a broad interest in > social behaviour, and who wish to work with animals in the wild. > Quantitative skills are not a prerequisite for consideration, but > positive attitude towards doing great science is! > > Alex Jordan > Principal Investigator > Department of Collective Behaviour > Max Planck Institute for Ornithology > www.alexjordan.org > collectivebehaviour.com > > "Jordan, Alex" > via IFTTT