How thalamic relays might orchestrate supervised deep training and symbolic computation in the brain

Abstract

The thalamus appears to be involved in the flexible routing of information among cortical areas, yet the computational implications of such routing are only beginning to be explored. Here we create a connectionist model of how selectively gated cortico-thalamo-cortical relays could underpin both symbolic and sub-symbolic computations. We first show how gateable relays can be used to create a Dynamically Partitionable Auto-Associative Network (DPAAN) (Hayworth, 2012) consisting of a set of cross-connected cortical memory buffers. All buffers and relays in a DPAAN are trained simultaneously to have a common set of stable attractor states that become the symbol vocabulary of the DPAAN. We show via simulations that such a DPAAN can support operations necessary for syntactic rule-based computation, namely buffer-to-buffer copying and equality detection. We then provide each DPAAN module with a multilayer input network trained to map sensory inputs to the DPAANs symbol vocabulary, and demonstrate how gateable thalamic relays can provide recall and clamping operations to train this input network by Contrastive Hebbian Learning (CHL) (Xie and Seung, 2003). We suggest that many such DPAAN modules may exist at the highest levels of the brains sensory hierarchies and show how a joint snapshot of the contents of multiple DPAAN modules can be stored as a declarative memory in a simple model of the hippocampus. We speculate that such an architecture might first have been discovered by evolution as a means to bootstrap learning of more meaningful cortical representations feeding the striatum, eventually leading to a system that could support symbolic computation. Our model serves as a bridging hypothesis for linking controllable thalamo-cortical information routing with computations that could underlie aspects of both learning and symbolic reasoning in the brain.