Neural Mechanism to Simulate a Scale-Invariant Future

Abstract

Predicting future events, and their order, is important for efficient planning. We propose a neural mechanism to non-destructively translate the current state of memory into the future, so as to construct an ordered set of future predictions. This framework applies equally well to translations in time or in one-dimensional position. In a two-layer memory network that encodes the Laplace transform of the external input in real time, translation can be accomplished by modulating the weights between the layers. We propose 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. We operationalize several neurobiological findings into phenomenological equations constraining translation. 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. The resulting expressions are consistent with findings from phase precession experiments in different regions of the hippocampus and reward systems in the ventral striatum. The model makes several experimental predictions that can be tested with existing technology.

Temporal and spatial context in the mind and brain

“…Rodent work demonstrating that entorhinal grid cells have time-cell-like properties in time cell experiments [52], suggesting that spatial and temporal context representations are supported in some cases by the same neurons. Moreover, because of the mathematical properties of the representation, it can be extended further to generate compressed representations of the future [69]. …”

Predicting the past, remembering the future

“…Several lines of evidence suggest that the hippocampus, although traditionally viewed as a repository for episodic memory and spatial representation, may in fact be organized around predictive principles [35, 36]. First, place cells in the hippocampus sweep ahead of an animal's current position when it is at a choice point [37]; these forward sweeps may arise from phase precession, the progressive shift in spike timing relative to the ongoing theta oscillation as an animal moves through a place field [38, 39, 4]. Second, when an animal repeatedly runs on a particular trajectory, place fields tend to expand opposite the direction of travel [40], consistent with the idea that earlier place cells learn to predict upcoming locations.…”

“…[4] A computational model of how hippocampal theta oscillations can be used to predict the future, by translating a representation of stimulus history forward in time.…”

“…At shorter timescales, many events can be anticipated using sensory information from the recent past, giving organisms with memory an adaptive advantage. Thus, memory of the past, spanning vastly different timescales, is relevant for predicting and controlling the future [4, 5, 6, 7]. …”

Hippocampal Mechanisms for the Segmentation of Space by Goals and Boundaries

“…In this scheme, information is communicated by which neurons are co-active and not by their inter-spike intervals (Harris 2005). Segmentation of the environment would still be evidenced by which regions of space were represented by the ensemble at each phase, though these segments may not change within a theta period (for a different perspective see Shankar and Howard 2015).…”