Figure 2.

Spiking network for probabilistic inference and online learning. (A) Winner-take-all network architecture with lateral inhibition and synaptic weights W_ki. (B) Network neurons z_k implicitly maintain a Gaussian likelihood function for each input Y_i in their afferent synaptic weights W_ki. The mean μ_ki of the distribution is encoded by the fraction W_ki/W_max = m_ki/M of active switches in the compound memristive synapse, i.e., stronger synaptic weights W_ki correspond to higher mean values μ_ki. Inset: Local implicit graphical model. (C) Illustration of Bayesian inference for two competing network neurons z_k, z_j and one active input Y_i(t) = 1. Different means μ_ki, μ_ji encoded in the weights give rise to different values in the likelihood function and shape the posterior distribution according to Bayes rule.