Distributed Temporal Coding of Visual Memory Categories in Human Hippocampal Neurons

The hippocampus is crucial for forming new episodic memories. While the encoding of spatial and temporal information (where and when) in the hippocampus is well understood, the encoding of objects (what) remains less clear due to the high dimensions of object space. Rather than encoding each individual object separately, the hippocampus may instead encode categories of objects to reduce this dimensionality. In this study, we developed and applied a combined experimental-modeling approach to investigate how the hippocampus encodes visual memory categories in humans. We recorded spikes from hippocampal CA3 and CA1 neurons in 24 epilepsy patients performing a visual delayed match-to-sample (DMS) task involving five image categories. An ensemble multi-temporal-resolution classification model was employed to decode these visual memory categories from the hippocampal spiking activity with moderate numbers of trials. This model enables the identification of the spatio-temporal characteristics of hippocampal encoding through its interpretable representations. Using this model, we estimated the optimal temporal resolutions for decoding each visual memory category for each neuron in the ensemble. Results indicate that visual memory categories can be decoded from hippocampal spike patterns despite the short data length, supporting the presence of category-specific coding in the human hippocampus. We found that hippocampal neuron ensembles encode visual memory categories in a distributed manner, akin to a population code, while individual neurons use a temporal code. Additionally, CA3 and CA1 neurons exhibit similar and redundant information regarding visual memory categories, likely due to the strong and diffuse feedforward synaptic connections from the CA3 region to the CA1 region.