Kavli Affiliate: Loren Frank
| Authors: Joshua P Chu, Michael E Coulter, Eric L. Denovellis, Trevor TK Nguyen, Daniel F Liu, Xinyi Deng, Uri T Eden, Caleb T Kemere and Loren M Frank
| Summary:
Decoding algorithms provide a powerful tool for understanding the firing patterns that underlie cognitive processes such as motor control, learning, and recall. When implemented in the context of a real-time system, decoders also make it possible to deliver feedback based on the representational content of ongoing neural activity. That in turn allows experimenters to test hypotheses about the role of that content in driving downstream activity patterns and behaviors. While multiple real-time systems have been developed, they are typically implemented in C++ and are locked to a specific data acquisition system, making them difficult to adapt to new experiments. Here we present a Python software system that implements online clusterless decoding using state space models in a manner independent of data acquisition systems. The parallelized system processes neural data with temporal resolution of 6 ms and median computational latency <50 ms for medium- to large-scale (32+ tetrodes) rodent hippocampus recordings without the need for spike sorting. It also executes auxiliary functions such as detecting sharp wave ripples from local field potential (LFP) data. Performance is similar to state-of-the-art solutions which use compiled programming languages. We demonstrate this system use in a rat behavior experiment in which the decoder allowed closed loop neurofeedback based on decoded hippocampal spatial representations . This system provides a powerful and easy-to-modify tool for real-time feedback experiments.