@article {69350, title = {Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings}, journal = {bioRxiv}, year = {2024}, month = {2024 Apr 10}, abstract = {

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density ( 10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron{\textquoteright}s electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to 3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75\% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.

}, doi = {10.1101/2023.08.23.554527}, url = {https://www.biorxiv.org/content/early/2024/04/10/2023.08.23.554527}, author = {Zhiwen Ye and Andrew M Shelton and Jordan R Shaker and Julien M Boussard and Jennifer Colonell and Daniel Birman and Sahar Manavi and Susu Chen and Charlie Windolf and Cole Hurwitz and Tomoyuki Namima and Frederico Pedraja and Shahaf Weiss and Bogdan Raducanu and Torbj{\o}rn Ness and Xiaoxuan Jia and Giulia Mastroberardino and L. Federico Rossi and Matteo Carandini and Michael Hausser and Gaute T Einevoll and Gilles Laurent and Nathaniel B Sawtell and Wyeth Bair and Anitha Pasupathy and Carolina Mora-Lopez and Barun Dutta and Liam Paninski and Joshua H Siegle and Christof Koch and Shawn R Olsen and Timothy D Harris and Nicholas A Steinmetz} } @article {68647, title = {Brain-wide neural activity underlying memory-guided movement}, journal = {bioRxiv}, year = {2023}, month = {2023 Mar 02}, abstract = {

Behavior requires neural activity across the brain, but most experiments probe neurons in a single area at a time. Here we used multiple Neuropixels probes to record neural activity simultaneously in brain-wide circuits, in mice performing a memory-guided directional licking task. We targeted brain areas that form multi-regional loops with anterior lateral motor cortex (ALM), a key circuit node mediating the behavior. Neurons encoding sensory stimuli, choice, and actions were distributed across the brain. However, in addition to ALM, coding of choice was concentrated in subcortical areas receiving input from ALM, in an ALM-dependent manner. Choice signals were first detected in ALM and the midbrain, followed by the thalamus, and other brain areas. At the time of movement initiation, choice-selective activity collapsed across the brain, followed by new activity patterns driving specific actions. Our experiments provide the foundation for neural circuit models of decision-making and movement initiation.

}, doi = {10.1101/2023.03.01.530520}, url = {https://doi.org/10.1101/2023.03.01.530520}, author = {Susu Chen and Yi Liu and Ziyue Wang and Jennifer Colonell and Liu D. Liu and Han Hou and Nai-Wen Tien and Tim Wang and Timothy Harris and Shaul Druckmann and Nuo Li and Karel Svoboda} } @article {68829, title = {Not everything, not everywhere, not all at once: a study of brain-wide encoding of movement}, journal = {bioRxiv}, year = {2023}, month = {2023 Jun 09}, abstract = {

Activity related to movement is found throughout sensory and motor regions of the brain. However, it remains unclear how movement-related activity is distributed across the brain and whether systematic differences exist between brain areas. Here, we analyzed movement related activity in brain-wide recordings containing more than 50,000 neurons in mice performing a decision-making task. Using multiple techniques, from markers to deep neural networks, we find that movement-related signals were pervasive across the brain, but systematically differed across areas. Movement-related activity was stronger in areas closer to the motor or sensory periphery. Delineating activity in terms of sensory- and motor-related components revealed finer scale structures of their encodings within brain areas. We further identified activity modulation that correlates with decision-making and uninstructed movement. Our work charts out a largescale map of movement encoding and provides a roadmap for dissecting different forms of movement and decision-making related encoding across multi-regional neural circuits.

}, doi = {10.1101/2023.06.08.544257}, url = {https://doi.org/10.1101/2023.06.08.544257}, author = {Ziyue Aiden Wang and Susu Chen and Yi Liu and Dave Liu and Karel Svoboda and Nuo Li and Shaul Druckmann} } @article {68466, title = {A scalable implementation of the recursive least-squares algorithm for training spiking neural networks}, journal = {Frontiers in Neuroinformatics}, year = {2023}, month = {2023 Jun 27}, abstract = {

Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a prominent tool to study computations in the brain. With an increasing size and complexity of neural recordings, there is a need for fast algorithms that can scale to large datasets. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation allows training networks to reproduce neural activity of an order of millions neurons at order of magnitude times faster than the CPU implementation. We demonstrate this by applying our algorithm to reproduce the activity of\ \>\ 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables efficient training of large-scale spiking models, thus allowing for\ in-silico\ study of the dynamics and connectivity underlying multi-area computations.

}, doi = {10.3389/fninf.2023.1099510}, url = {https://doi.org/10.3389/fninf.2023.1099510}, author = {Benjamin J. Arthur and Christopher M. Kim and Susu Chen and Stephan Preibisch and Ran Darshan} }