Recurrent probabilistic dynamics: Applications to face recognition (original) (raw)
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Psychological review, 2018
We present a hierarchical neural network model, in which subpopulations of neurons develop fixed and regularly repeating temporal chains of spikes (polychronization), which respond specifically to randomized Poisson spike trains representing the input training images. The performance is improved by including top-down and lateral synaptic connections, as well as introducing multiple synaptic contacts between each pair of pre- and postsynaptic neurons, with different synaptic contacts having different axonal delays. Spike-timing-dependent plasticity thus allows the model to select the most effective axonal transmission delay between neurons. Furthermore, neurons representing the binding relationship between low-level and high-level visual features emerge through visually guided learning. This begins to provide a way forward to solving the classic feature binding problem in visual neuroscience and leads to a new hypothesis concerning how information about visual features at every spati...
Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots, and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory-inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization, as well as stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset, and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of awake monkey recordings. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function -fast sampling-based inference- and...
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ArXiv, 2021
In this paper, we showed that adding within-layer recurrent connections to feedforward neural network models could improve the performance of neural response prediction in early visual areas by up to 11 percent across different data sets and over tens of thousands of model configurations. To understand why recurrent models perform better, we propose that recurrent computation can be conceptualized as an ensemble of multiple feed-forward pathways of different lengths with shared parameters. By reformulating a recurrent model as a multi-path model and analyzing the recurrent model through its multi-path ensemble, we found that the recurrent model outperformed the corresponding feed-forward one due to the former’s compact and implicit multi-path ensemble that allows approximating the complex function underlying recurrent biological circuits with efficiency. In addition, we found that the performance differences among recurrent models were highly correlated with the differences in their...
PLoS ONE, 2014
Neurons in the primary visual cortex are more or less selective for the orientation of a light bar used for stimulation. A broad distribution of individual grades of orientation selectivity has in fact been reported in all species. A possible reason for emergence of broad distributions is the recurrent network within which the stimulus is being processed. Here we compute the distribution of orientation selectivity in randomly connected model networks that are equipped with different spatial patterns of connectivity. We show that, for a wide variety of connectivity patterns, a linear theory based on firing rates accurately approximates the outcome of direct numerical simulations of networks of spiking neurons. Distance dependent connectivity in networks with a more biologically realistic structure does not compromise our linear analysis, as long as the linearized dynamics, and hence the uniform asynchronous irregular activity state, remain stable. We conclude that linear mechanisms of stimulus processing are indeed responsible for the emergence of orientation selectivity and its distribution in recurrent networks with functionally heterogeneous synaptic connectivity.
A general model of stochastic neural processing
Biological Cybernetics, 1990
A model of neural processing is proposed which is able to incorporate a great deal of neurophysiological detail, including effects associated with the mechanics of postsynaptic summation and cell surface geometry and is capable of hardware realisation as a ‘probabilistic random access memory’ (pRAM). The model is an extension of earlier work by the authors, which by operating at much shorter time scales (of the order of the lifetime of a quantum of neurotransmitter in the synaptic cleft) allows a greater amount of information to be retrieved from the simulated spike train. The mathematical framework for the model appears to be that of an extended Markov process (involving the firing histories of the N neurons); simulation work has yielded results in excellent agreement with theoretical predictions. The extended neural model is expected to be particularly applicable in situations where timing constraints are of special importance (such as the auditory cortex) or where firing thresholds are high, as in the case for the granule and pyramidal cells of the hippocampus.