@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} } @article {68166, title = {Distributing task-related neural activity across a cortical network through task-independent connections}, journal = {bioRxiv}, year = {2022}, month = {2022 Jun 18}, abstract = {

Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a limited subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. We found that task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.

}, doi = {10.1101/2022.06.17.496618}, url = {https://doi.org/10.1101/2022.06.17.496618}, author = {Christopher M. Kim and Arseny Finkelstein and Carson C. Chow and Karel Svoboda and Ran Darshan} }