Einblick - Computational neuroscience - # Efficient Coding in Spiking Neural Networks

Efficient Coding in Excitatory-Inhibitory Spiking Networks: Structural, Dynamical, and Biophysical Properties

Q: How might the efficient coding principles be extended to account for the role of neuromodulators in shaping cortical dynamics and information processing

Efficient coding principles can be extended to account for the role of neuromodulators in shaping cortical dynamics and information processing by considering how neuromodulators influence the parameters and dynamics of the network. Neuromodulators, such as dopamine, acetylcholine, and serotonin, play a crucial role in regulating synaptic plasticity, neuronal excitability, and network connectivity. These neuromodulatory systems can modulate the gain of neural responses, alter the balance between excitation and inhibition, and regulate the flexibility of neural circuits in response to changing environmental demands. In the context of efficient coding, neuromodulators can adjust the balance between encoding accuracy and metabolic cost based on the current behavioral context or motivational state. For example, dopamine release in response to reward signals can enhance the signal-to-noise ratio in neural responses, leading to more precise encoding of rewarding stimuli. Acetylcholine, on the other hand, can enhance attention and sensory processing by modulating the dynamics of cortical circuits, potentially improving the efficiency of information processing. Furthermore, neuromodulators can influence the plasticity of synaptic connections, affecting the structure and strength of recurrent connectivity in the network. By incorporating the effects of neuromodulators into the efficient coding framework, we can better understand how cortical dynamics are dynamically regulated to optimize information processing under different conditions and states.

Q: What are the potential limitations of the efficient coding framework in explaining the full diversity of neural response properties observed in biological networks

While the efficient coding framework provides valuable insights into how sensory information is encoded in neural networks with minimal metabolic cost, there are potential limitations in explaining the full diversity of neural response properties observed in biological networks. Some of these limitations include: Temporal Dynamics: Efficient coding models often focus on static stimuli or simplified temporal dynamics, whereas biological neural networks exhibit complex temporal patterns and dynamics. Incorporating more realistic temporal dynamics, such as adaptation, facilitation, and oscillations, may be necessary to capture the full range of neural responses. Nonlinear Interactions: Biological neural networks exhibit nonlinear interactions between neurons, such as dendritic processing, synaptic plasticity, and network feedback loops. Efficient coding models typically assume linear integration of inputs, which may not fully capture the nonlinearities present in biological systems. Neuromodulation: Efficient coding models often do not explicitly consider the role of neuromodulatory systems in shaping neural responses. Neuromodulators can dynamically modulate network properties, such as synaptic strength, excitability, and plasticity, which can significantly impact information processing in the brain. Sparse Coding: While efficient coding emphasizes the importance of sparse representations for reducing redundancy, biological networks also exhibit distributed and overlapping representations. Exploring the balance between sparse and distributed coding in neural networks is essential for a more comprehensive understanding of information processing. By addressing these limitations and incorporating more biologically realistic features into the efficient coding framework, we can enhance its explanatory power and better capture the complexity of neural response properties observed in biological networks.

Kernkonzepte

Efficient coding principles can directly shape the structural, dynamical, and biophysical properties of excitatory-inhibitory spiking neural networks, leading to key features observed in biological cortical circuits.

Zusammenfassung

The paper systematically analyzes the properties of a spiking neural network model that is analytically derived from the principles of efficient coding. The key findings are:

The optimal network has biologically-plausible features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and structured recurrent connectivity that implements feature-specific competition between excitatory neurons.
The optimal network has a 4:1 ratio of excitatory to inhibitory neurons and a 3:1 ratio of mean inhibitory-to-inhibitory vs. excitatory-to-inhibitory connectivity, closely matching experimental observations in cortical sensory networks.
The efficient network exhibits a tight instantaneous balance between excitation and inhibition, enabling efficient coding of external stimuli varying over multiple timescales.
The structure of recurrent connectivity, particularly the excitatory-to-inhibitory and inhibitory-to-excitatory connections, is crucial for achieving efficient coding and implementing feature-specific lateral inhibition.
The metabolic cost parameter controls the operating regime of the network, modulating firing rates, variability, and the balance between excitation and inhibition.
The model makes quantitative predictions about experimentally measurable structural, coding, and dynamical features of neural activity that emerge from efficient coding principles.

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Statistiken

"The optimal network has a 4 to 1 ratio of excitatory vs inhibitory neurons and a 3 to 1 ratio of mean inhibitory-to-inhibitory vs. excitatory-to-inhibitory connectivity."
"The efficient network exhibits a tight instantaneous balance between excitation and inhibition."

Zitate

"The optimal network has biologically-plausible features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and structured recurrent connectivity that implements feature-specific competition between excitatory neurons."
"The structure of recurrent connectivity, particularly the excitatory-to-inhibitory and inhibitory-to-excitatory connections, is crucial for achieving efficient coding and implementing feature-specific lateral inhibition."

Wichtige Erkenntnisse aus

Structure, dynamics, coding and optimal biophysical parameters of efficient excitatory-inhibitory spiking networks

by Koren,V., Bl... um www.biorxiv.org 04-27-2024

https://www.biorxiv.org/content/10.1101/2024.04.24.590955v1

Tiefere Fragen

How might the efficient coding principles be extended to account for the role of neuromodulators in shaping cortical dynamics and information processing

Efficient coding principles can be extended to account for the role of neuromodulators in shaping cortical dynamics and information processing by considering how neuromodulators influence the parameters and dynamics of the network. Neuromodulators, such as dopamine, acetylcholine, and serotonin, play a crucial role in regulating synaptic plasticity, neuronal excitability, and network connectivity. These neuromodulatory systems can modulate the gain of neural responses, alter the balance between excitation and inhibition, and regulate the flexibility of neural circuits in response to changing environmental demands.
In the context of efficient coding, neuromodulators can adjust the balance between encoding accuracy and metabolic cost based on the current behavioral context or motivational state. For example, dopamine release in response to reward signals can enhance the signal-to-noise ratio in neural responses, leading to more precise encoding of rewarding stimuli. Acetylcholine, on the other hand, can enhance attention and sensory processing by modulating the dynamics of cortical circuits, potentially improving the efficiency of information processing.
Furthermore, neuromodulators can influence the plasticity of synaptic connections, affecting the structure and strength of recurrent connectivity in the network. By incorporating the effects of neuromodulators into the efficient coding framework, we can better understand how cortical dynamics are dynamically regulated to optimize information processing under different conditions and states.

What are the potential limitations of the efficient coding framework in explaining the full diversity of neural response properties observed in biological networks

While the efficient coding framework provides valuable insights into how sensory information is encoded in neural networks with minimal metabolic cost, there are potential limitations in explaining the full diversity of neural response properties observed in biological networks. Some of these limitations include:

Temporal Dynamics: Efficient coding models often focus on static stimuli or simplified temporal dynamics, whereas biological neural networks exhibit complex temporal patterns and dynamics. Incorporating more realistic temporal dynamics, such as adaptation, facilitation, and oscillations, may be necessary to capture the full range of neural responses.

Nonlinear Interactions: Biological neural networks exhibit nonlinear interactions between neurons, such as dendritic processing, synaptic plasticity, and network feedback loops. Efficient coding models typically assume linear integration of inputs, which may not fully capture the nonlinearities present in biological systems.

Neuromodulation: Efficient coding models often do not explicitly consider the role of neuromodulatory systems in shaping neural responses. Neuromodulators can dynamically modulate network properties, such as synaptic strength, excitability, and plasticity, which can significantly impact information processing in the brain.

Sparse Coding: While efficient coding emphasizes the importance of sparse representations for reducing redundancy, biological networks also exhibit distributed and overlapping representations. Exploring the balance between sparse and distributed coding in neural networks is essential for a more comprehensive understanding of information processing.

By addressing these limitations and incorporating more biologically realistic features into the efficient coding framework, we can enhance its explanatory power and better capture the complexity of neural response properties observed in biological networks.

What insights can be gained by comparing the efficient coding model to alternative normative theories of neural computation, such as predictive coding or information maximization

Comparing the efficient coding model to alternative normative theories of neural computation, such as predictive coding or information maximization, can provide valuable insights into the underlying principles of information processing in the brain. Here are some key insights that can be gained from such comparisons:

Predictive Coding vs. Efficient Coding: Predictive coding posits that the brain generates internal predictions about sensory inputs and minimizes prediction errors, while efficient coding focuses on maximizing information transmission with minimal metabolic cost. By comparing these two frameworks, we can explore how the brain balances prediction-based processing with energy-efficient coding strategies to optimize sensory processing.

Information Maximization vs. Efficient Coding: Information maximization theories suggest that neural networks are designed to maximize the mutual information between sensory inputs and neural responses. Comparing information maximization with efficient coding can reveal how different optimization principles shape the structure and dynamics of neural networks. Understanding the trade-offs between maximizing information content and minimizing metabolic cost can provide insights into the computational strategies employed by the brain.

Robustness and Generalization: Comparing the generalization capabilities of these different models can shed light on how well they adapt to novel stimuli and noisy environments. Efficient coding models may excel in energy-efficient processing of familiar stimuli, while predictive coding models may be more adaptive to unpredictable or changing environments. Understanding the strengths and limitations of each model in terms of robustness and generalization can inform our understanding of neural computation.

By comparing the efficient coding model to alternative normative theories, we can uncover the common principles underlying neural information processing and identify the unique contributions of each framework to our understanding of brain function.