A Radically New Theory of How the Brain Represents and Computes with Probabilities

It is widely believed that the brain implements probabilistic reasoning and that it represents information via some form of population (distributed) code. Most prior probabilistic population coding (PPC) theories share basic properties: 1) continuous-valued units; 2) fully/densely distributed codes; 3) graded synapses; 4) rate coding; 5) units have innate low-complexity, usually unimodal, tuning functions (TFs); and 6) units are intrinsically noisy and noise is generally considered harmful. I describe a radically different theory that assumes: 1) binary units; 2) sparse distributed codes (SDC); 3) functionally binary synapses; 4) a novel, atemporal, combinatorial spike code; 5) units initially have flat TFs (all weights zero); and 6) noise is a controlled resource used to cause similar inputs to be mapped to similar codes. The theory, Sparsey, was introduced 25 + years ago as: a) an explanation of the physical/computational relationship of episodic and semantic memory for the spatiotemporal (sequential) pattern domain; and b) a canonical, mesoscale cortical probabilistic circuit/algorithm possessing fixed-time, unsupervised, single-trial, non-optimization-based, unsupervised learning and fixed-time best-match (approximate) retrieval; but was not described in terms of probabilistic computation. Here, we show that: a) the active SDC in a Sparsey coding field (CF) simultaneously represents not only the likelihood of the single most likely input but the likelihoods of all hypotheses stored in the CF; and b) that entire explicit distribution can be transmitted, e.g., to a downstream CF, via a set of simultaneous single spikes from the neurons comprising the active SDC.