betekintés - Neuroscience Computational - # Neuronal Dynamics and Synaptic Plasticity

Real-Time Learning in Cortical Circuits through a Neuronal Least-Action Principle

Q: How can the NLA principle be extended to account for more complex neuronal dynamics, such as spiking activity, dendritic nonlinearities, and neuromodulation

To extend the Neuronal Least-Action (NLA) principle to incorporate more complex neuronal dynamics, several adjustments can be made. Firstly, for spiking activity, the NLA framework can be adapted to include spiking neuron models, such as the integrate-and-fire model or the Hodgkin-Huxley model. By incorporating the dynamics of action potentials and spike timing, the prospective coding in the NLA principle can be modified to account for the discrete nature of spiking activity. This adjustment would involve updating the neuronal dynamics equations to capture the spiking behavior and the propagation of action potentials through the network. Secondly, to address dendritic nonlinearities, the NLA principle can be enhanced by incorporating detailed models of dendritic processing. Dendrites play a crucial role in information integration and processing in neurons, and their nonlinear properties can significantly impact neuronal computations. By including dendritic nonlinearities in the model, such as dendritic spikes or nonlinear synaptic integration, the NLA principle can better capture the complex dendritic processing that occurs in cortical circuits. This enhancement would involve modifying the equations for dendritic processing to reflect the nonlinear behavior of dendrites. Lastly, to account for neuromodulation, the NLA principle can be extended to incorporate the effects of neuromodulatory systems on cortical processing. Neuromodulators, such as dopamine, serotonin, and acetylcholine, play a key role in regulating neuronal activity and synaptic plasticity. By integrating neuromodulatory signals into the NLA framework, the model can capture the influence of these systems on learning and information processing in cortical circuits. This extension would involve including neuromodulatory pathways in the network model and adjusting the learning rules to account for the modulatory effects on synaptic plasticity and neuronal dynamics.

Q: What are the potential limitations of the prospective coding approach, and how could it be combined with other error-correction mechanisms in the brain

While prospective coding in the NLA principle offers a powerful framework for real-time learning and error correction in cortical circuits, there are potential limitations to this approach that need to be considered. One limitation is the reliance on accurate predictions of future states, which may be challenging in dynamic and unpredictable environments. If the predictions are inaccurate or the environment changes rapidly, the prospective coding may lead to suboptimal corrections and learning outcomes. To address this limitation, prospective coding could be combined with other error-correction mechanisms, such as feedback control loops or reinforcement learning, to adapt to changing conditions and uncertainties in the environment. Another limitation of prospective coding is the computational complexity of continuously predicting future states and adjusting neuronal activity in real time. This may require significant computational resources and neural circuitry to implement effectively. To mitigate this limitation, a balance between prospective coding and reactive processing could be established, allowing the system to react quickly to unexpected events while still leveraging prospective predictions for optimal decision-making. By combining prospective coding with other error-correction mechanisms, such as feedback loops, reinforcement learning, or reactive processing, the NLA principle can be enhanced to address a wider range of challenges and uncertainties in cortical processing.

Q: Could the NLA principle offer insights into the role of cortical feedback connections and their interaction with feedforward processing in other cognitive domains beyond sensory-motor control

The NLA principle offers valuable insights into the role of cortical feedback connections and their interaction with feedforward processing in various cognitive domains beyond sensory-motor control. In cognitive tasks such as decision-making, attention, and memory, cortical feedback connections play a crucial role in shaping neural activity and guiding information processing. By applying the NLA framework to these cognitive domains, we can gain a deeper understanding of how cortical feedback connections contribute to cognitive functions. In decision-making tasks, cortical feedback connections may provide top-down signals that bias neural activity towards relevant information or choices. By incorporating the NLA principle, we can investigate how these feedback signals influence decision-making processes and optimize neural computations for better decision outcomes. In attentional tasks, cortical feedback connections may modulate sensory processing and enhance the representation of relevant stimuli. The NLA framework can elucidate how feedback signals dynamically adjust neural activity to prioritize attentional resources and improve task performance. In memory tasks, cortical feedback connections are involved in memory retrieval, consolidation, and updating. By applying the NLA principle to memory processes, we can explore how feedback signals contribute to memory formation and retrieval, and how they interact with feedforward processing to support memory functions. Overall, the NLA principle can provide a unified framework to study the intricate interplay between cortical feedback connections and feedforward processing in diverse cognitive domains, shedding light on the neural mechanisms underlying complex cognitive functions.

Alapfogalmak

The brain prospectively minimizes the local somato-dendritic mismatch error within individual neurons to produce appropriate behavioral outputs in real-time, based on a neuronal least-action principle.

Kivonat

The paper introduces a neuronal least-action (NLA) principle for real-time learning in cortical circuits. The key insights are:

The NLA principle postulates that cortical pyramidal neurons prospectively minimize the local somato-dendritic mismatch error within individual neurons to produce appropriate behavioral outputs. This is achieved through a prospective coding of neuronal firing rates and errors.
The prospective coding enables instantaneous propagation of information through the network, overcoming delays in sensory-motor processing. This is formalized as a "moving equilibrium hypothesis" where sensory inputs, network state, motor commands, and muscle feedback are in a self-consistent equilibrium at any point of the movement.
A local synaptic plasticity rule is derived that performs gradient descent on the global cost function, relating the local dendritic prediction errors to the overall network performance. This "real-time Dendritic Error Propagation" (rt-DeEP) allows the network to learn complex sensory-motor mappings in real-time.
The NLA principle is implemented in a cortical microcircuit architecture where pyramidal neurons and interneurons interact to extract the dendritic prediction errors. This "real-time Dendritic Error Learning" (rt-DeEL) enables biologically plausible error backpropagation.
The framework is demonstrated on examples of reproducing intracortical EEG recordings and learning a sensory-motor mapping for handwritten digit recognition, showing the advantages of the prospective coding for real-time learning.

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a forrásanyagból

Forrás megtekintése

biorxiv.org

Statisztikák

The paper does not contain explicit numerical data or statistics to support the key claims. It focuses on the theoretical development of the NLA principle and its implementation in cortical microcircuits.

Idézetek

"The neuronal least-action principle offers an axiomatic framework to derive local neuronal and synaptic dynamics for global real-time computation and learning in the brain and in physical substrates in general."
"The fundamental novelty of our NLA principle is the way it deals with time. In physics, bodies interact based on where they are now, irrespective of what happens in the future. Living systems, instead, interact based on what could happen in the near future, and react early to stay alive."
"The insight into the time structure of biological information processing allows us to express a simple form of a total 'mismatch energy' for our cortical neuronal networks, from which we derive the dynamic neuronal and synaptic laws."

Főbb Kivonatok

A neuronal least-action principle for real-time learning in cortical circuits

by Senn,W., Dol... : www.biorxiv.org 03-25-2023

https://www.biorxiv.org/content/10.1101/2023.03.25.534198v3

Mélyebb kérdések

How can the NLA principle be extended to account for more complex neuronal dynamics, such as spiking activity, dendritic nonlinearities, and neuromodulation

To extend the Neuronal Least-Action (NLA) principle to incorporate more complex neuronal dynamics, several adjustments can be made. Firstly, for spiking activity, the NLA framework can be adapted to include spiking neuron models, such as the integrate-and-fire model or the Hodgkin-Huxley model. By incorporating the dynamics of action potentials and spike timing, the prospective coding in the NLA principle can be modified to account for the discrete nature of spiking activity. This adjustment would involve updating the neuronal dynamics equations to capture the spiking behavior and the propagation of action potentials through the network.
Secondly, to address dendritic nonlinearities, the NLA principle can be enhanced by incorporating detailed models of dendritic processing. Dendrites play a crucial role in information integration and processing in neurons, and their nonlinear properties can significantly impact neuronal computations. By including dendritic nonlinearities in the model, such as dendritic spikes or nonlinear synaptic integration, the NLA principle can better capture the complex dendritic processing that occurs in cortical circuits. This enhancement would involve modifying the equations for dendritic processing to reflect the nonlinear behavior of dendrites.
Lastly, to account for neuromodulation, the NLA principle can be extended to incorporate the effects of neuromodulatory systems on cortical processing. Neuromodulators, such as dopamine, serotonin, and acetylcholine, play a key role in regulating neuronal activity and synaptic plasticity. By integrating neuromodulatory signals into the NLA framework, the model can capture the influence of these systems on learning and information processing in cortical circuits. This extension would involve including neuromodulatory pathways in the network model and adjusting the learning rules to account for the modulatory effects on synaptic plasticity and neuronal dynamics.

What are the potential limitations of the prospective coding approach, and how could it be combined with other error-correction mechanisms in the brain

While prospective coding in the NLA principle offers a powerful framework for real-time learning and error correction in cortical circuits, there are potential limitations to this approach that need to be considered. One limitation is the reliance on accurate predictions of future states, which may be challenging in dynamic and unpredictable environments. If the predictions are inaccurate or the environment changes rapidly, the prospective coding may lead to suboptimal corrections and learning outcomes. To address this limitation, prospective coding could be combined with other error-correction mechanisms, such as feedback control loops or reinforcement learning, to adapt to changing conditions and uncertainties in the environment.
Another limitation of prospective coding is the computational complexity of continuously predicting future states and adjusting neuronal activity in real time. This may require significant computational resources and neural circuitry to implement effectively. To mitigate this limitation, a balance between prospective coding and reactive processing could be established, allowing the system to react quickly to unexpected events while still leveraging prospective predictions for optimal decision-making.
By combining prospective coding with other error-correction mechanisms, such as feedback loops, reinforcement learning, or reactive processing, the NLA principle can be enhanced to address a wider range of challenges and uncertainties in cortical processing.

Could the NLA principle offer insights into the role of cortical feedback connections and their interaction with feedforward processing in other cognitive domains beyond sensory-motor control

The NLA principle offers valuable insights into the role of cortical feedback connections and their interaction with feedforward processing in various cognitive domains beyond sensory-motor control. In cognitive tasks such as decision-making, attention, and memory, cortical feedback connections play a crucial role in shaping neural activity and guiding information processing. By applying the NLA framework to these cognitive domains, we can gain a deeper understanding of how cortical feedback connections contribute to cognitive functions.
In decision-making tasks, cortical feedback connections may provide top-down signals that bias neural activity towards relevant information or choices. By incorporating the NLA principle, we can investigate how these feedback signals influence decision-making processes and optimize neural computations for better decision outcomes.
In attentional tasks, cortical feedback connections may modulate sensory processing and enhance the representation of relevant stimuli. The NLA framework can elucidate how feedback signals dynamically adjust neural activity to prioritize attentional resources and improve task performance.
In memory tasks, cortical feedback connections are involved in memory retrieval, consolidation, and updating. By applying the NLA principle to memory processes, we can explore how feedback signals contribute to memory formation and retrieval, and how they interact with feedforward processing to support memory functions.
Overall, the NLA principle can provide a unified framework to study the intricate interplay between cortical feedback connections and feedforward processing in diverse cognitive domains, shedding light on the neural mechanisms underlying complex cognitive functions.