Recurrent predictive coding models for associative memory employing covariance learning
Fig 2
We use the single-layer implicit/dendritic covPCN to model the hippocampus, and a hierarchical PCN from [12] to model the sensory cortex and neocortex. Neurons and synapses in the hierarchical layers follow the dynamic rules in [12]. For clarity of demonstration, only one layer of our neocortex model is shown. Expanded boxes show the detailed computations within individual neurons and related synapses specified in Eqs 20 and 22, where denotes the a-th neuron in the lth layer, and Θab and Wab denote the individual weights from the ath to the bth neurons. Dog image in this figure is obtained from Wikimedia Commons under a CC BY 4.0 license.