Neural mechanism to simulate a scale-invariant future timeline

TL;DR

This paper introduces a neural mechanism that enables scale-invariant prediction of future events by translating memory states within a two-layer network, aligning with neurobiological findings and supporting efficient planning.

Contribution

It proposes a novel neural framework for translating memory into future predictions using Laplace transforms and scale-invariance principles, consistent with hippocampal and reward system data.

Findings

Predicts future timelines with Weber-Fechner spacing.

Aligns with hippocampal phase precession data.

Proposes testable experimental predictions.

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.