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Neural Computation

December 2016, Vol. 28, No. 12, Pages 2594-2627
(doi: 10.1162/NECO_a_00891)
© 2016 Massachusetts Institute of Technology
Neural Mechanism to Simulate a Scale-Invariant Future
Article PDF (772.01 KB)

Predicting the timing and order of future events is an essential feature of cognition in higher life forms. We propose a neural mechanism to nondestructively translate the current state of spatiotemporal memory into the future, so as to construct an ordered set of future predictions almost instantaneously. We hypothesize that within each cycle of hippocampal theta oscillations, the memory state is swept through a range of translations to yield an ordered set of future predictions through modulations in synaptic connections. Theoretically, we operationalize critical neurobiological findings from hippocampal physiology in terms of neural network equations representing spatiotemporal memory. Combined with constraints based on physical principles requiring scale invariance and coherence in translation across memory nodes, the proposition results in Weber-Fechner spacing for the representation of both past (memory) and future (prediction) timelines. We show that the phenomenon of phase precession of neurons in the hippocampus and ventral striatum correspond to the cognitive act of future prediction.