BI 205 Dmitri Chklovskii: Neurons Are Smarter Than You Think

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Feb 12, 2025

In a captivating discussion, Dmitri Chklovskii, head of the Neural Circuits and Algorithms lab at the Flatiron Institute, reveals how neurons are far more than just passive processors. He argues that these 'smart' cells actively influence their inputs, reshaping our understanding of brain function. The conversation dives into the complexities of the C. elegans connectome, uncovering its significance for neural communication. Chklovskii also challenges traditional neuroscience by exploring how feedback mechanisms and spike timing shape learning and behavior.

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ANECDOTE

Completing the C. elegans Connectome

Chklovskii's lab completed the C. elegans connectome, initially for wiring economy research.
His student, Beth Chen, discovered the published connectome was incomplete and spent over a year completing it using archived micrographs.

INSIGHT

Connectome's Limitations

Despite having the complete connectome, understanding C. elegans' computation and behavior remains challenging.
Statistical discoveries were made, but they didn't reveal how the worm computes or generates behavior.

INSIGHT

Smart Neurons

McCulloch-Pitts neurons, while influential, oversimplify real neurons.
Real neurons possess temporal dynamics and memory effects, unlike the instantaneous McCulloch-Pitts model.

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Since the 1940s and 50s, back at the origins of what we now think of as artificial intelligence, there have been lots of ways of conceiving what it is that brains do, or what the function of the brain is. One of those conceptions, going to back to cybernetics, is that the brain is a controller that operates under the principles of feedback control. This view has been carried down in various forms to us in present day. Also since that same time period, when McCulloch and Pitts suggested that single neurons are logical devices, there have been lots of ways of conceiving what it is that single neurons do. Are they logical operators, do they each represent something special, are they trying to maximize efficiency, for example?

Dmitri Chklovskii, who goes by Mitya, runs the Neural Circuits and Algorithms lab at the Flatiron Institute. Mitya believes that single neurons themselves are each individual controllers. They're smart agents, each trying to predict their inputs, like in predictive processing, but also functioning as an optimal feedback controller. We talk about historical conceptions of the function of single neurons and how this differs, we talk about how to think of single neurons versus populations of neurons, some of the neuroscience findings that seem to support Mitya's account, the control algorithm that simplifies the neuron's otherwise impossible control task, and other various topics.

We also discuss Mitya's early interests, coming from a physics and engineering background, in how to wire up our brains efficiently, given the limited amount of space in our craniums. Obviously evolution produced its own solutions for this problem. This pursuit led Mitya to study the C. elegans worm, because its connectome was nearly complete- actually, Mitya and his team helped complete the connectome so he'd have the whole wiring diagram to study it. So we talk about that work, and what knowing the whole connectome of C. elegans has and has not taught us about how brains work.

Chklovskii Lab.
Twitter: @chklovskii.
Related papers
- The Neuron as a Direct Data-Driven Controller.
- Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction.
Related episodes
- BI 143 Rodolphe Sepulchre: Mixed Feedback Control
- BI 119 Henry Yin: The Crisis in Neuroscience

Read the transcript.

0:00 - Intro 7:34 - Physicists approach for neuroscience 12:39 - What's missing in AI and neuroscience? 16:36 - Connectomes 31:51 - Understanding complex systems 33:17 - Earliest models of neurons 39:08 - Smart neurons 42:56 - Neuron theories that influenced Mitya 46:50 - Neuron as a controller 55:03 - How to test the neuron as controller hypothesis 1:00:29 - Direct data-driven control 1:11:09 - Experimental evidence 1:22:25 - Single neuron doctrine and population doctrine 1:25:30 - Neurons as agents 1:28:52 - Implications for AI 1:30:02 - Limits to control perspective