Variable synaptic strengths controls the firing rate distribution in feedforward neural networks

Area of Science:

• Computational neuroscience
• Neural coding and information processing
• Systems neuroscience

Background:

• Firing rate heterogeneity in neural populations can impair neural coding.
• Experimental data from weakly electric fish show stimulus-dependent firing rate distributions in the electrosensory lateral line lobe (ELL).
• Network inputs can modulate population firing rate distributions.

Purpose of the Study:

• To extend theoretical models of neural heterogeneity to a delayed feedforward spiking network.
• To investigate how synaptic and intrinsic neural attributes interact to alter firing rate heterogeneity.
• To capture in-vivo observations of firing rate heterogeneity changes in response to stimuli.

Main Methods:

• Developed a theoretical model of a delayed feedforward spiking network with random recurrent coupling.
• Incorporated synaptic and intrinsic heterogeneity into the model.
• Analyzed the relationship between neural attributes, network connectivity, and firing rate heterogeneity across different sensory stimuli.

Main Results:

• Demonstrated that heterogeneous neural attributes alter firing rate heterogeneity in a stimulus-dependent manner.
• Predicted a correlation between feedforward input strength and excitability, which varies with firing rate heterogeneity.
• Showed that neural attribute interactions are complex and stimulus-dependent, not simple.

Conclusions:

• Neural heterogeneity is a critical factor in shaping population codes.
• The developed model qualitatively captures experimental observations of firing rate heterogeneity.
• The study provides a framework for predicting effective neural architecture based on neural attributes and stimulus conditions.