Ivan Kairatov is a distinguished biopharma expert and neuroscientist with a profound background in research and development, specializing in the intersection of neural mechanics and technological innovation. His recent work challenges the traditional view that the brain becomes more efficient by making neurons more independent; instead, he argues that learning is actually a process of sophisticated synchronization. By observing small networks of neurons in the visual cortex over several weeks, he and his colleagues have uncovered how shared neural activity allows us to navigate a complex world. This shift in perspective has massive implications for how we understand cognitive disorders and how we might build the next generation of artificial intelligence.
In neuroscience, there is a shift from viewing learning as a process of increasing neuronal independence to one of enhancing coordination. How does this shared activity refine our perception, and what specific metrics or patterns indicate that neurons are behaving more like a synchronized team during a task?
For decades, we operated under the assumption that the brain was trying to eliminate “noise” or redundancy by pushing neurons to act as solo agents. Our research at the University of Rochester shows the opposite is true: as you get better at a skill, like recognizing a face or spotting a typo, your neurons actually increase the amount of information they share. We tracked small networks of neurons in the visual cortex over several weeks and found that before learning, these cells worked largely in isolation. As the subjects honed their skills, the neurons began to display increased information redundancy, behaving less like individuals and more like a well-trained sports team. This coordination allows the brain to perform active inference, meaning it isn’t just recording the world but is actively interpreting it based on what it has learned to expect.
Neural coordination appears to fluctuate based on whether a person is actively making decisions or passively observing. Why is the brain’s engagement level so critical for this synchronized behavior, and what happens to these communication loops when we shift between intense problem-solving and idle rest?
The engagement level is the “on” switch for these sophisticated coordination patterns because the brain needs to prioritize relevant information during decision-making. We observed that when subjects were passively looking at images without needing to respond, the coordinated neural effect completely disappeared. It is only when the brain is actively performing a task that these flexible shifts in behavior emerge, particularly at the exact moments when a decision is being made. This suggests that the brain isn’t a simple conveyor belt passing data forward; it is a dynamic system that dials coordination up or down on the fly. When we shift to idle rest, the feedback loops from higher-level brain areas likely relax, allowing the sensory neurons to return to a more independent, baseline state.
The brain often blends incoming sensory data with learned internal expectations rather than just passing information forward. How do feedback signals from higher-level brain regions physically reshape sensory responses, and how does this integration prevent us from being overwhelmed by unfamiliar visual patterns?
Feedback signals act as a sort of internal guide that tells the sensory cortex what is likely to be important based on past experience. Instead of the sensory areas just passively encoding every photon that hits the eye, higher-level regions send signals back down to reshape how those neurons respond to incoming data. This integration allows us to blend what we see with what we expect to see, creating a much richer and more stable picture of the world. By relying on these internal models, the brain can filter out irrelevant visual noise and focus on the patterns that match our learned expectations. This prevents us from being overwhelmed because we aren’t processing every unfamiliar detail from scratch; we are constantly comparing new data against a robust internal library of “knowns.”
Current artificial intelligence systems often rely on mapping inputs directly to outputs, yet biological models suggest generative feedback loops are superior. How could mimicking neural coordination make AI more robust to uncertainty, and what are the primary hurdles in engineering such flexible, human-like systems?
Most current AI is built on discriminative architectures that simply map a sensory input to a specific output, which makes them brittle when faced with unexpected data. If we move toward architectures that incorporate generative feedback loops—mimicking the way the brain blends expectations with input—we could create systems that are much more robust to uncertainty and can learn from significantly less data. The primary hurdle is that our current engineering relies heavily on linear efficiency and minimizing redundancy, which is the exact opposite of the “information redundancy” we see in the brain. Engineering a system that can flexibly adjust its internal coordination patterns on the fly, just as a human does when switching from a resting state to a complex task, requires a fundamental shift in how we design neural networks.
Disruptions in how neurons share information might underlie certain learning and perception disorders. In what ways could mapping these coordination patterns improve diagnostic tools, and what practical steps could researchers take to help recalibrate neural teamwork in patients struggling with sensory processing?
If we can map the standard “teamwork” patterns of neurons in a healthy brain, we can use those as a benchmark to identify where coordination is failing in patients with learning or sensory disorders. Instead of just looking at whether a specific brain region is “active,” we would look at how well that region is sharing information with others during a task. This could lead to diagnostic tools that catch subtle communication breakdowns long before they manifest as severe symptoms. To recalibrate this neural teamwork, researchers could look into targeted therapies or training protocols that specifically emphasize task-based engagement to trigger those higher-level feedback loops. By focusing on the “active” moments of decision-making, we might be able to encourage the brain to re-establish the synchronized communication necessary for effective learning.
What is your forecast for the field of neural coordination research?
I believe the next decade will see a total overhaul of the “conveyor belt” model of the brain in favor of this highly coordinated, generative model. We are going to move away from looking at individual neurons and start treating neural populations as integrated communication networks, which will revolutionize how we treat cognitive decline and sensory processing issues. This research will also likely serve as the blueprint for “Next-Gen AI,” where systems are no longer just calculators but are capable of the same flexible inference that allows a human to navigate an unpredictable crowd. Ultimately, understanding that the brain values coordination over raw independence will unlock new ways to enhance human performance and build truly adaptive technology.
