Pattern separation of spiketrains in hippocampal neurons

Antoine D Madar,Laura A Ewell,Mathew V Jones

doi:10.1038/s41598-019-41503-8

Antoine D Madar, Laura A Ewell + Show 1 more

Open Access

https://doi.org/10.1038/s41598-019-41503-8

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Abstract

Pattern separation is a process that minimizes overlap between patterns of neuronal activity representing similar experiences. Theoretical work suggests that the dentate gyrus (DG) performs this role for memory processing but a direct demonstration is lacking. One limitation is the difficulty to measure DG inputs and outputs simultaneously. To rigorously assess pattern separation by DG circuitry, we used mouse brain slices to stimulate DG afferents and simultaneously record DG granule cells (GCs) and interneurons. Output spiketrains of GCs are more dissimilar than their input spiketrains, demonstrating for the first time temporal pattern separation at the level of single neurons in the DG. Pattern separation is larger in GCs than in fast-spiking interneurons and hilar mossy cells, and is amplified in CA3 pyramidal cells. Analysis of the neural noise and computational modelling suggest that this form of pattern separation is not explained by simple randomness and arises from specific presynaptic dynamics. Overall, by reframing the concept of pattern separation in dynamic terms and by connecting it to the physiology of different types of neurons, our study offers a new window of understanding in how hippocampal networks might support episodic memory.

Highlights

Pattern separation is a process that minimizes overlap between patterns of neuronal activity representing similar experiences
A direct test of pattern separation in single granule cells (GCs) requires knowledge of the similarity between input patterns arriving via the perforant path (PP) and comparison with the similarity between GC output patterns
We report that similar cortical input spiketrains are transformed in the dentate gyrus (DG) network, leading to less similar output spiketrains in GCs

Summary

Introduction

Pattern separation is a process that minimizes overlap between patterns of neuronal activity representing similar experiences. Some electrophysiological studies suggest that EC spatial representations are on average more correlated than in DG13,14,18,19, but the recorded EC neurons were unlikely to contact the recorded DG neurons, and were not recorded at the same time: a direct test of whether DG itself performs pattern separation on EC inputs is still lacking. Another difficulty in studying pattern separation is in defining the nature of “activity patterns”. Www.nature.com/scientificreports are represented by the same population of active neurons, but differences are encoded by different spatially tuned firing patterns[13,14,17]

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