Bayesian Integration of Information Using Top-Down Modulated WTA Networks
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2022-10-01
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en
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Abstract
Winner-Take-All (WTA) circuits—a type of Spiking
Neural Networks (SNN) that have been identified as ubiquitous
processing components in the brain — have been suggested
as facilitating the brain’s ability to process information in
a Bayesian manner. Research has shown that WTA circuits
are capable of approximating the Expectation-Maximization
(EM) algorithm. Further, it has been shown that WTA circuits
can be connected into hierarchical WTA networks capable of
approximating hierarchical Bayesian models. So far, research
in this direction has focused on bottom-up processes. This is
contrary to neuroscientific evidence that shows that, besides
bottom-up processes, top-down processes too play a key role in
information processing by the human brain. Several functions
ascribed to top-down processes include direction of attention,
adjusting for expectations, facilitation of encoding and recall of
learned information, and imagery. This paper explores whether
WTA circuits are suitable for further integrating information
represented in separate WTA networks. Furthermore, it explores
whether, and under what circumstances, top-down processes can
improve WTA network performance with respect to inference
and learning. The results show that WTA circuits are capable
of integrating the probabilistic information represented by other
WTA networks, and that top-down processes can improve a WTA
network’s inference and learning performance.
Index Terms—Winner-take-all (WTA) circuit, hierarchical
WTA network, Spiking Neural Network (SNN), spike-timingdependent
plasticity (STDP), Bayesian inference, top-down processes.
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Faculteit der Sociale Wetenschappen