Indisputable facts when implementing spiking neuron networks

TL;DR

This paper clarifies fundamental principles of spike-timing coding in spiking neuron networks, emphasizing the importance of respecting biological and computational constraints for effective implementation.

Contribution

It provides a clear, deterministic review with concrete numerical insights on spike coding, time constraints, and parameter adjustments for biologically plausible and efficient spiking networks.

Findings

Respecting time constraints prevents meaningless mechanisms.

Continuous calculations can replace unnecessary spikes.

Implementing simple spiking networks is straightforward without complex codes.

Abstract

In this article, our wish is to demystify some aspects of coding with spike-timing, through a simple review of well-understood technical facts regarding spike coding. The goal is to help better understanding to which extend computing and modelling with spiking neuron networks can be biologically plausible and computationally efficient. We intentionally restrict ourselves to a deterministic dynamics, in this review, and we consider that the dynamics of the network is defined by a non-stochastic mapping. This allows us to stay in a rather simple framework and to propose a review with concrete numerical values, results and formula on (i) general time constraints, (ii) links between continuous signals and spike trains, (iii) spiking networks parameter adjustments. When implementing spiking neuron networks, for computational or biological simulation purposes, it is important to take into account the indisputable facts here reviewed. This precaution could prevent from implementing mechanisms meaningless with regards to obvious time constraints, or from introducing spikes artificially, when continuous calculations would be sufficient and simpler. It is also pointed out that implementing a spiking neuron network is finally a simple task, unless complex neural codes are considered.