The effect of correlated variability on the accuracy of a population code
Neural Computation
Stable propagation of activity pulses in populations of spiking neurons
Neural Computation
Population coding and decoding in a neural field: a computational study
Neural Computation
Synchronous firing and higher-order interactions in neuron pool
Neural Computation
On embedding synfire chains in a balanced network
Neural Computation
Rate models for conductance-based cortical neuronal networks
Neural Computation
Stationary Bumps in Networks of Spiking Neurons
Neural Computation
Signal propagation in feedforward neuronal networks with unreliable synapses
Journal of Computational Neuroscience
Transmission of population-coded information
Neural Computation
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We report on deterministic and stochastic evolutions of firing states through a feedforward neural network with Mexican-hat-type connectivity. The prevalence of columnar structures in a cortex implies spatially localized connectivity between neural pools. Although feedforward neural network models with homogeneous connectivity have been intensively studied within the context of the synfire chain, the effect of local connectivity has not yet been studied so thoroughly. When a neuron fires independently, the dynamics of macroscopic state variables (a firing rate and spatial eccentricity of a firing pattern) is deterministic from the law of large numbers. Possible stable firing states, which are derived from deterministic evolution equations, are uniform, localized, and nonfiring. The multistability of these three states is obtained where the excitatory and inhibitory interactions among neurons are balanced. When the presynapse-dependent variance in connection efficacies is incorporated into the network, the variance generates common noise. Then the evolution of the macroscopic state variables becomes stochastic, and neurons begin to fire in a correlated manner due to the common noise. The correlation structure that is generated by common noise exhibits a nontrivial bimodal distribution. The development of a firing state through neural layers does not converge to a certain fixed point but keeps on fluctuating.