Elsevier

Biosystems

Volume 48, Issues 1–3, 1 November 1998, Pages 57-65
Biosystems

Rate coding versus temporal order coding: a theoretical approach

https://doi.org/10.1016/S0303-2647(98)00050-1Get rights and content

Abstract

The belief that neurones transmit information in the form of a firing rate code is almost universal. However, we argue that at least in some situations, the efficiency of a coding strategy based on rate coding is surprisingly poor. A simple mathematical analysis reveals that, due to the stochastic nature of spike generation, even transmitting the simplest signals reliably would require either: (1) excessively long observation periods incompatible with the speed of sensory processing or (2) excessively large numbers of redundant neurones, incompatible with the anatomical constraints imposed by sensory pathways. We argue that such problems may be avoided by using alternative temporal codes which rely on the asynchrony of firing across a population of afferent neurones.

Introduction

For decades, neurophysiologists have characterised neural activity by the firing rate: PSTHs, tuning curves and even more recent stimulus-reconstruction methods all rely on stimulus-dependent changes in firing rate. Furthermore, the rate coding hypothesis has undoubtedly shaped the development of the ideas underlying artificial neural networks and PDP models.

However, recent studies that have looked at the speed with which sensory systems can process information (Thorpe et al., 1996) pose serious problems for traditional rate coding schemes. For instance, face selective neurones in primate inferotemporal cortex can respond only 80–100 ms after stimulus onset (Oram and Perrett, 1992). The fact that the first 5 ms of their responses is already selective suggests that the selectivity can be produced by essentially feed-forward propagation of information from the retina to IT via the LGN, V1, V2 and V4, a processing sequence involving roughly ten synaptic stages. On average, this leaves no more than 10 ms between two successive stages of synaptic activation, i.e. 10 ms for synaptic transmission, PSP conduction and integration, spike generation and spike conduction. We have argued that this period is too short to allow rates to be determined accurately, because few neurones will fire more than one spike in this time (Thorpe and Imbert, 1989).

Section snippets

Rate coding of analog values

In this section, we will examine the efficiency of rate coding using a very simple Poisson process for generating spikes. It is clear this is only a very approximate model for the firing of real neurones. However, we believe that the general points that can be drawn will apply to any scheme relying solely on counting the number of spikes.

From the efferent neurone’s point of view, the less time there is to evaluate afferent spike rate, the more inaccurate this evaluation. If we assume that spike

Comparisons between two analog values

Neural processing is often aimed at detecting differences in activation rather than absolute values: contrast rather than pure luminance, edges rather than areas, etc. Would a rate coding strategy be more efficient for such a purpose?

Let us take two populations A and B, each composed of N neurones, which send spikes to a second-level neurone D that has to react on the basis of the difference of activity between the two afferent sub-populations. For instance, the efferent neurone D could test

Asynchrony: another way to code

In this section, we will consider some of the alternatives to rate coding. Typically, these involve some form of temporal coding, i.e. a code in which the timing of spikes plays a crucial role. With very short time scales (10 ms or less), time coding and rate coding tend to become confounded since ultimately, any form of temporal code can be described in terms of very rapid changes in firing rate (Rieke et al., 1997). However, the situation becomes much more interesting if we consider the

Rank order coding

One simple way of using asynchrony is to use the order in which the neurones spike as a code. In this case, the exact latency at which a neurone fires is not critical—only the rank order of each neurone is important (Thorpe and Gautrais, 1997; Thorpe and Gautrais, 1998). Such a scheme offers a number of advantages.

Firstly, a code based on the order will be more robust to noisy temporal jitter of each spike than a pure temporal code that must rely on temporal precision, especially when decoded

Conclusions

The idea that neurones transmit information in the form of a rate code is extremely entrenched. There have been numerous other suggestions over the years (Perkel and Bullock, 1968), but they have done little to overturn the overwhelming popularity of the rate coding hypothesis. Even today, with more and more researchers interested in the possibility that temporal synchrony might play an important role in neural computation (Abeles, 1991 Singer and Gray, 1995), most people still consider that

References (21)

  • W.R. Levick

    Variation in the response latency of cat retinal ganglion cells

    Vision Res.

    (1973)
  • A.K. Sestokas et al.

    Visual latency of X- and Y-cells in the dorsal lateral geniculate nucleus of the cat

    Vision Res.

    (1986)
  • R. van Rullen et al.

    Face processing using one spike per neurone

    Biosystems

    (1998)
  • L.F. Abbott et al.

    Synaptic depression and cortical gain control

    Science

    (1997)
  • Abeles, M., 1991. Corticonics. Neural circuits of the cerebral cortex. Cambridge University Press,...
  • R. Baddeley et al.

    Responses of neurons in primary and inferior temporal visual cortices to natural scenes

    Proc. R. Soc. London

    (1997)
  • P.Y. Burgi et al.

    Asynchrony in visual analysis: Using the luminance-to-response-latency relationship to improve segmentation

    J. Opt. Soc. Am. A

    (1994)
  • S. Celebrini et al.

    Dynamics of orientation coding in area V1 of the awake primate

    Visual Neurosci.

    (1993)
  • T.J. Gawne et al.

    Adjacent visual cortical complex cells share about 20% of their stimulus-related information

    Cereb Cortex

    (1996)
  • Nowak, L.G., Bullier, J., 1997. The timing of information transfer in the visual system. In: J. Kaas, K. Rocklund, A....
There are more references available in the full text version of this article.

Cited by (0)

View full text