A Radically New Theory of how the Brain Represents and Computes with Probabilities
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The brain is believed to implement probabilistic reasoning and to represent information via population, or distributed, coding. Most previous population-based probabilistic (PPC) theories share several basic properties: 1) continuous-valued neurons; 2) fully(densely)-distributed codes, i.e., all(most) units participate in every code; 3) graded synapses; 4) rate coding; 5) units have innate unimodal tuning functions (TFs); 6) intrinsically noisy units; and 7) noise/correlation is considered harmful. We present a radically different theory that assumes: 1) binary units; 2) only a small subset of units, i.e., a sparse distributed code (SDC) (cell assembly, ensemble), comprises any individual code; 3) binary synapses; 4) signaling formally requires only single (first) spikes; 5) units initially have completely flat TFs (all weights zero); 6) units are not inherently noisy; but rather 7) noise is a resource generated/used to cause similar inputs to map to similar codes, controlling a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over stored codes, indirectly yielding correlation patterns. The theory, Sparsey, was introduced 20 years ago as a canonical cortical circuit/algorithm model, but not elaborated as an alternative to PPC theories. Here, we show that the active SDC simultaneously represents both the most similar/likely input and the coarsely-ranked distribution over all stored inputs (hypotheses). Crucially, Sparsey's code selection algorithm (CSA), used for both learning and inference, achieves this with a single pass over the weights for each successive item of a sequence, thus performing spatiotemporal pattern learning/inference with a number of steps that remains constant as the number of stored items increases. We also discuss our approach as a radically new implementation of graphical probability modeling.
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