Indisputable facts when implementing spiking neuron networks

“Spikes are the neural code”: this claim is about 15 years old (Shadlen & Newsome, 1994; Rieke, Warland, Steveninck, & Bialek, 1996), preceded by theoretical studies on the underlying mathematical processes (e.g., (Gerstein & Mandelbrot, 1964)), and followed by many developments regarding biological modelling or computational paradigms, or both (e.g., (Thorpe, Delorme, & VanRullen, 2001)). However the involvement of spikes in neural coding is still an open subject. Several fundamental aspects of dynamics based on spike-timing have been very recently clarified, both at the neuron level (Touboul & Brette, 2008) and the network level (Cessac & Viéville, 2008). Nevertheless, still a non negligible set of received ideas, as, e.g., the “incredible power of spikes” or, e.g., the “mystery of the [spike based] neural code” (sic !) are currently encountered in literature. In this article, our wish is to demystify some aspects of coding with spike-timing, through a simple review of wellunderstood 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 continous calculations would be sufficent and simpler. It is also pointed out that implementing a spiking neuron network is finally a simple task, unless complex neural codes are considered.