Abstract
We propose the use of nonnegative matrix factorization (NMF) as a model-independent methodology to analyze neural activity. We demonstrate that, using this technique, it is possible to identify local spatiotemporal patterns of neural activity in the form of sparse basis vectors. In addition, the sparseness of these bases can help infer correlations between cortical firing patterns and behavior. We demonstrate the utility of this approach using neural recordings collected in a brain-machine interface (BMI) setting. The results indicate that, using the NMF analysis, it is possible to improve the performance of BMI models through appropriate pruning of inputs.
Highlights
Brain-machine interfaces (BMIs) are an emerging field that aims at directly transferring the subject’s intent of movement to an external machine
In BMIs the neural inputs are processed by grouping the firings into bin counts
The experimental results and the analysis presented in this paper showed that we could find repeated patterns in neuronal activity that occurred in synchrony with the reaching behavior and was automatically and efficiently represented in a set of sparse bases
Summary
Brain-machine interfaces (BMIs) are an emerging field that aims at directly transferring the subject’s intent of movement to an external machine. We examined how each neuron contributes to the output of the models, and found consistent relationships between cortical regions and segments of the hand trajectory in a reaching movement. This analysis indicated that, during each reaching action, specific neurons from the posterior parietal, the premotor dorsal, and the primary motor regions sequentially became dominant in controlling the output of the models. This approach relies on determining a suitable model, because it explicitly uses the learned model to infer the dependencies. We will show that the results from this model-independent analysis of the neuronal activity are consistent with the previous observations from the model-based analysis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
