The Timing of Vision – How Neural Processing Links to Different Temporal Dynamics

In this review, we describe our recent attempts to model the neural correlates of visual perception with biologically inspired networks of spiking neurons, emphasizing the dynamical aspects. Experimental evidence suggests distinct processing modes depending on the type of task the visual system is engaged in. A first mode, crucial for object recognition, deals with rapidly extracting the glimpse of a visual scene in the first 100 ms after its presentation. The promptness of this process points to mainly feedforward processing, which relies on latency coding, and may be shaped by spike timing-dependent plasticity (STDP). Our simulations confirm the plausibility and efficiency of such a scheme. A second mode can be engaged whenever one needs to perform finer perceptual discrimination through evidence accumulation on the order of 400 ms and above. Here, our simulations, together with theoretical considerations, show how predominantly local recurrent connections and long neural time-constants enable the integration and build-up of firing rates on this timescale. In particular, we review how a non-linear model with attractor states induced by strong recurrent connectivity provides straightforward explanations for several recent experimental observations. A third mode, involving additional top-down attentional signals, is relevant for more complex visual scene processing. In the model, as in the brain, these top-down attentional signals shape visual processing by biasing the competition between different pools of neurons. The winning pools may not only have a higher firing rate, but also more synchronous oscillatory activity. This fourth mode, oscillatory activity, leads to faster reaction times and enhanced information transfers in the model. This has indeed been observed experimentally. Moreover, oscillatory activity can format spike times and encode information in the spike phases with respect to the oscillatory cycle. This phenomenon is referred to as “phase-of-firing coding,” and experimental evidence for it is accumulating in the visual system. Simulations show that this code can again be efficiently decoded by STDP. Future work should focus on continuous natural vision, bio-inspired hardware vision systems, and novel experimental paradigms to further distinguish current modeling approaches

The Role of Rhythmic Neural Synchronization in Rest and Task Conditions

Rhythmic neural synchronization is found throughout the brain during many different tasks and even at rest. Beyond their underlying mechanisms, the question of their role is still controversial. Modeling can bring insight on this difficult question. We review here our recent modeling results concerning this issue in different situations. During rest, we show how local rhythmic synchrony can induce a spatiotemporally organized spontaneous activity at the brain level. Then, we show how rhythmic synchrony decreases reaction time in attention and enhances the strength and speed of information transfer between different groups of neurons. Finally, we show that when rhythmic synchrony creates firing phases, the learning with spike timing-dependent plasticity of repeatedly presented input patterns is greatly enhanced

Neural variability, or lack thereof

We do not claim that the brain is completely deterministic, and we agree that noise may be beneficial in some cases. But we suggest that neuronal variability may be often overestimated, due to uncontrolled internal variables, and/or the use of inappropriate reference times. These ideas are not new, but should be reexamined in the light of recent experimental findings: trial-to-trial variability is often correlated across neurons, across trials, greater for higher-order neurons, and reduced by attention, suggesting that intrinsic sources of noise can only account for a minimal part of it. While it is obviously difficult to control for all internal variables, the problem of reference time can be largely avoided by recording multiple neurons at the same time, and looking at statistical structures in relative latencies. These relative latencies have another major advantage: they are insensitive to the variability that is shared across neurons, which is often a significant part of the total variability. Thus we suggest that signal-to-noise ratios in the brain may be much higher than usually thought, leading to reactive systems, economic in terms of number of neurons, and energy efficient