@article {69669, title = {ONIX: a unified open-source platform for multimodal neural recording and perturbation during naturalistic behavior.}, journal = {Nat Methods}, year = {2024}, month = {2024 Nov 11}, abstract = {

Behavioral neuroscience faces two conflicting demands: long-duration recordings from large neural populations and unimpeded animal behavior. To meet this challenge we developed ONIX, an open-source data acquisition system with high data throughput (2 GB s) and low closed-loop latencies (\<1 ms) that uses a 0.3-mm thin tether to minimize behavioral impact. Head position and rotation are tracked in three dimensions and used to drive active commutation without torque measurements. ONIX can acquire data from combinations of passive electrodes, Neuropixels probes, head-mounted microscopes, cameras, three-dimensional trackers and other data sources. We performed uninterrupted, long (~7 h) neural recordings in mice as they traversed complex three-dimensional terrain, and multiday sleep-tracking recordings (~55 h). ONIX enabled exploration with similar mobility as nonimplanted animals, in contrast to conventional tethered systems, which have restricted movement. By combining long recordings with full mobility, our technology will enable progress on questions that require high-quality neural recordings during ethologically grounded behaviors.

}, issn = {1548-7105}, doi = {10.1038/s41592-024-02521-1}, url = {https://doi.org/10.1038/s41592-024-02521-1}, author = {Newman, Jonathan P and Zhang, Jie and Cuevas-L{\'o}pez, Aar{\'o}n and Miller, Nicholas J and Honda, Takato and van der Goes, Marie-Sophie H and Leighton, Alexandra H and Carvalho, Filipe and Lopes, Gon{\c c}alo and Lakunina, Anna and Siegle, Joshua H and Harnett, Mark T and Wilson, Matthew A and Voigts, Jakob} } @article {1341, title = {A combinatorial method for analyzing sequential firing patterns involving an arbitrary number of neurons based on relative time order.}, journal = {Journal of Neurophysiology}, volume = {92}, year = {2004}, month = {2004 Oct}, pages = {2555-73}, abstract = {

Information processing in the brain is believed to require coordinated activity across many neurons. With the recent development of techniques for simultaneously recording the spiking activity of large numbers of individual neurons, the search for complex multicell firing patterns that could help reveal this neural code has become possible. Here we develop a new approach for analyzing sequential firing patterns involving an arbitrary number of neurons based on relative firing order. Specifically, we develop a combinatorial method for quantifying the degree of matching between a "reference sequence" of N distinct "letters" (representing a particular target order of firing by N cells) and an arbitrarily long "word" composed of any subset of those letters including repeats (representing the relative time order of spikes in an arbitrary firing pattern). The method involves computing the probability that a random permutation of the word{\textquoteright}s letters would by chance alone match the reference sequence as well as or better than the actual word does, assuming all permutations were equally likely. Lower probabilities thus indicate better matching. The overall degree and statistical significance of sequence matching across a heterogeneous set of words (such as those produced during the course of an experiment) can be computed from the corresponding set of probabilities. This approach can reduce the sample size problem associated with analyzing complex firing patterns. The approach is general and thus applicable to other types of neural data beyond multiple spike trains, such as EEG events or imaging signals from multiple locations. We have recently applied this method to quantify memory traces of sequential experience in the rodent hippocampus during slow wave sleep.

}, keywords = {Algorithms, Animals, Computer Simulation, Data Interpretation, Electric Stimulation, Electroencephalography, Electrophysiology, Hippocampus, Neural Networks (Computer), Neurons, Probability, Rats, Sample Size, Sleep, Statistical}, issn = {0022-3077}, doi = {10.1152/jn.01030.2003}, author = {Lee, Albert K and Wilson, Matthew A} } @article {1340, title = {Memory of sequential experience in the hippocampus during slow wave sleep.}, journal = {Neuron}, volume = {36}, year = {2002}, month = {2002 Dec 19}, pages = {1183-94}, abstract = {

Rats repeatedly ran through a sequence of spatial receptive fields of hippocampal CA1 place cells in a fixed temporal order. A novel combinatorial decoding method reveals that these neurons repeatedly fired in precisely this order in long sequences involving four or more cells during slow wave sleep (SWS) immediately following, but not preceding, the experience. The SWS sequences occurred intermittently in brief ( approximately 100 ms) bursts, each compressing the behavioral sequence in time by approximately 20-fold. This rapid encoding of sequential experience is consistent with evidence that the hippocampus is crucial for spatial learning in rodents and the formation of long-term memories of events in time in humans.

}, keywords = {Action Potentials, Animals, Computer-Assisted, Hippocampus, Learning, Long-Evans, Male, Memory, Neural Pathways, Neuropsychological Tests, Pyramidal Cells, Rats, Signal Processing, Signal Transduction, Sleep, Synaptic Transmission}, issn = {0896-6273}, author = {Lee, Albert K and Wilson, Matthew A} }