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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
Published on: June 24, 2015
Population rate coding in recurrent neuronal networks with unreliable synapses
1School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, 610054 People's Republic of China ; Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Okinawa, 904-0411 Japan.
Cognitive Neurodynamics
|February 2, 2013
Summary
Unreliable synapses in neuronal networks can improve information encoding, especially with low noise. However, high noise levels and network randomness can impair encoding performance, contrary to deterministic models.
Area of Science:
- Computational Neuroscience
- Neural Coding
- Network Dynamics
Background:
- Neuronal communication relies on synaptic transmission, which is often experimentally observed as unreliable.
- Most computational models simplify synaptic interactions as deterministic, potentially overlooking the impact of unreliability.
Purpose of the Study:
- Investigate population rate coding in recurrent neuronal networks with unreliable synapses.
- Analyze the influence of synaptic unreliability and network parameters on neural encoding performance.
Main Methods:
- Utilized a stochastic on-off process to model unreliable synaptic transmission.
- Simulated an all-to-all coupled recurrent neuronal network with excitatory and inhibitory neurons.
- Compared results with corresponding random neuronal networks.
Main Results:
- Synapses with optimal transmission probability enhance encoding under weak noise but degrade it under strong noise.
- Excitatory synaptic strength, E-I balance, and synaptic time constants significantly affect population rate coding.
- Network randomness qualitatively mimics synaptic unreliability but differs quantitatively.
Conclusions:
- Synaptic unreliability plays a crucial role in neural encoding, with its effect being noise-dependent.
- Network structure and synaptic properties are critical determinants of coding efficiency, beyond average neurotransmitter levels.
- Stochastic models are essential for accurately representing neuronal communication and its impact on information processing.
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