| Resumo: | Modeling biologically realistic neural networks is a challenge for neural theory. While there is increasing evidence that the precise times of spikes play a crucial role in neural computation, building spike neural networks that resemble the spiking variability encountered in vivo while computing some function is not a trivial task. Boerlin et al. suggested a framework of leaky integrate-and-fire networks that, through excitation-inhibition tight balance, can track high-dimensional signals while producing spike trains with Poisson-like statistics. Notwithstanding their biologically plausible features, the spike coding networks rely on the instantaneous propagation of spikes to ensure an optimal function. Given that such an assumption may not fit the slower timescales of the synapses encountered in the brain this is a limitation of the model. Thus, under the goal of deriving a model with biologically plausible postsynaptic potentials, in this work, we take advantage of the spike coding networks’ ability to track high-dimensional signals to transform the problem of predictive tracking into a high-dimensional problem in the temporal domain. By doing so, we were able to get insights about the properties that such networks should have to be functional: no coding for the present time; temporal heterogeneity; prediction of the network’s estimate according to the dynamics of the signal being tracked. Then, by deriving a network from the same assumptions as Boerlin et al. while enforcing these properties it was possible to build a spike coding network that tracks multi-dimensional signals without relying on instantaneous communication of spikes.
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