Deciphering hippocampal place codes in weak theta rhythms

TL;DR

This paper shows that weak brain rhythms can still carry spatial information using a new neural network approach.

Contribution

The authors developed a novel neural network to decode spatial information from weak theta rhythms in the hippocampus.

Findings

Weak theta rhythms can convey spatial information comparable to population spike codes.

Position-tuned theta rhythms (pThetas) are distinct from dominant theta rhythms and reflect population coordination.

Information-based decoding principles outperform variance-based methods in capturing spatial data from irregular rhythms.

Abstract

Local field potentials (LFPs) reflect coordination among neural populations, yet their exact relationship to neural computation remains unknown. One exception is the theta rhythm of the rodent hippocampus, which organizes sequential firing among place cells, enabling spike timing to track the animal’s path through its environment. But when the animal stops, the theta rhythm becomes irregular, which is assumed to disrupt its ability to carry spatial information. Here we challenge this assumption by developing an artificial neural network that discovers position-tuned theta rhythms (pThetas) from LFPs even in the absence of strong theta oscillations. Using recordings from male rats, we provide evidence that pTheta is distinct from the dominant theta rhythm, while reflecting rhythmic coordination among place cell populations. Our work suggests that weak and intermittent oscillations, as seen in many brain regions and species, can convey information commensurate with population spike codes when decoded using information-based rather than variance-based principles.

Whether weak brain rhythms carry information about ongoing behavior remains unclear. Here, the authors develop a neural network that finds subtle motifs in irregular brain rhythms arising from neural populations to read out where a rat is in its environment.

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