@article {68938, title = {Learning produces an orthogonalized state machine in the hippocampus.}, journal = {Nature}, year = {2025}, month = {2025 Feb 12}, abstract = {

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, {\textquoteright}state cells{\textquoteright}). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.

}, doi = {10.1038/s41586-024-08548-w}, url = {https://doi.org/10.1038/s41586-024-08548-w}, author = {Sun, Weinan and Winnubst, Johan and Natrajan, Maanasa and Lai, Chongxi and Kajikawa, Koichiro and Michaelos, Michalis and Gattoni, Rachel and Stringer, Carsen and Flickinger, Daniel and Fitzgerald, James E and Spruston, Nelson} } @article {69073, title = {Volitional activation of remote place representations with a hippocampal brain-machine interface.}, journal = {Science}, volume = {382}, year = {2023}, month = {2023 Nov 03}, pages = {566-573}, abstract = {

The hippocampus is critical for recollecting and imagining experiences. This is believed to involve voluntarily drawing from hippocampal memory representations of people, events, and places, including maplike representations of familiar environments. However, whether representations in such "cognitive maps" can be volitionally accessed is unknown. We developed a brain-machine interface to test whether rats can do so by controlling their hippocampal activity in a flexible, goal-directed, and model-based manner. We found that rats can efficiently navigate or direct objects to arbitrary goal locations within a virtual reality arena solely by activating and sustaining appropriate hippocampal representations of remote places. This provides insight into the mechanisms underlying episodic memory recall, mental simulation and planning, and imagination and opens up possibilities for high-level neural prosthetics that use hippocampal representations.

}, keywords = {Animals, Brain-Computer Interfaces, Hippocampus, Humans, Imagination, Memory, Episodic, Mental Recall, Rats}, issn = {1095-9203}, doi = {10.1126/science.adh5206}, author = {Lai, Chongxi and Tanaka, Shinsuke and Harris, Timothy D and Lee, Albert K} } @article {64864, title = {Multimodal in vivo brain electrophysiology with integrated glass microelectrodes.}, journal = {Nature Biomedical Engineering}, volume = {3}, year = {2019}, month = {2019 Apr 01}, pages = {741-53}, abstract = {

Electrophysiology is the most used approach for the collection of functional data in basic and translational neuroscience, but it is typically limited to either intracellular or extracellular recordings. The integration of multiple physiological modalities for the routine acquisition of multimodal data with microelectrodes could be useful for biomedical applications, yet this has been challenging owing to incompatibilities of fabrication methods. Here, we present a suite of glass pipettes with integrated microelectrodes for the simultaneous acquisition of multimodal intracellular and extracellular information in vivo, electrochemistry assessments, and optogenetic perturbations of neural activity. We used the integrated devices to acquire multimodal signals from the CA1 region of the hippocampus in mice and rats, and show that these data can serve as ground-truth validation for the performance of spike-sorting algorithms. The microdevices are applicable for basic and translational neurobiology, and for the development of next-generation brain-machine interfaces.

}, issn = {2157-846X}, doi = {10.1038/s41551-019-0373-8}, author = {Hunt, David L and Lai, Chongxi and Smith, Richard D and Lee, Albert K and Harris, Timothy D and Barbic, Mladen} } @article {48220, title = {Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction.}, journal = {bioRxiv}, year = {2017}, month = {2017 Jan 19}, abstract = {

Electrical recordings from a large array of electrodes give us access to neural population activity with single-cell, single-spike resolution. These recordings contain extracellular spikes which must be correctly detected and assigned to individual neurons. Despite numerous spike-sorting techniques developed in the past, a lack of high-quality ground-truth datasets hinders the validation of spike-sorting approaches. Furthermore, existing approaches requiring manual corrections are not scalable for hours of recordings exceeding 100 channels. To address these issues, we built a comprehensive spike-sorting pipeline that performs reliably under noise and probe drift by incorporating a channel-covariance feature and a clustering based on fast density-peak finding. We validated performance of our workflow using multiple ground-truth datasets that recently became available. Our software scales linearly and processes a 1000-channel recording in real-time using a single workstation. Accurate, real-time spike sorting from large recording arrays will enable more precise control of closed-loop feedback experiments and brain-computer interfaces.

}, doi = {10.1101/101030}, author = {Jun, James Jaeyoon and Mitelut, Catalin and Lai, Chongxi and Gratiy, Sergey and Anastassiou, Costas and Harris, Timothy D} }