Abstract
Cortical circuits generate excitatory currents that must be cancelled by strong inhibition to assure stability. The resulting excitatory-inhibitory (E-I) balance can generate spontaneous irregular activity but, in standard balanced E-I models, this requires that an extremely strong feedforward bias current be included along with the recurrent excitation and inhibition. The absence of experimental evidence for such large bias currents inspired us to examine an alternative regime that exhibits asynchronous activity without requiring unrealistically large feedforward input. In these networks, irregular spontaneous activity is supported by a continually changing sparse set of neurons. To support this activity, synaptic strengths must be drawn from high-variance distributions. Unlike standard balanced networks, these sparse balance networks exhibit robust nonlinear responses to uniform inputs and non-Gaussian input statistics. Interestingly, the speed, not the size, of synaptic fluctuations dictates the degree of sparsity in the model. In addition to simulations, we provide a mean-field analysis to illustrate the properties of these networks.
Highlights
A typical cortical pyramidal cell receives thousands of excitatory inputs [1] that, without the influence of inhibition, would drive extremely high firing rates
We have uncovered a novel regime of E-I networks that exhibits asynchronous irregular activity without the need for unrealistically large external input currents
We have done so by taking advantage of widely distributed synapses that generate fluctuations that would otherwise be minuscule in the absence of large feedforward currents
Summary
A typical cortical pyramidal cell receives thousands of excitatory inputs [1] that, without the influence of inhibition, would drive extremely high firing rates. In the standard balanced models [7, 8], the input to each neuron has three strong components—recurrent excitation, recurrent inhibition and feedforward excitation. These balance automatically as part of the network dynamics, leaving residual fluctuations that drive neuronal firing at reasonable rates. There is no evidence for the strong input components predicted by these models [9], and some evidence against them [10,11,12,13,14]. We examine the consequences of removing strong feedforward input in balanced models
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