Constructing precisely computing networks with biophysical spiking neurons

TL;DR

This paper extends spike-based neural network models to more biologically realistic settings, demonstrating their ability to perform precise computations while exhibiting key cortical features like irregular spiking and excitation-inhibition balance.

Contribution

It introduces a biophysically plausible spike-based network model that maintains computational accuracy and mimics cortical neural dynamics.

Findings

Reproduces irregular, Poisson-like spike times.

Maintains tight excitation-inhibition balance.

Scales effectively with network size.

Abstract

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Den\'eve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output. By postulating that each neuron fires in order to reduce the error in the network's output, it was demonstrated that linear computations can be carried out by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size, or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation.