Revealing unexpected complex encoding but simple decoding mechanisms in motor cortex via separating behaviorally relevant neural signals

Author:

Li Yangang1234ORCID,Zhu Xinyun1234ORCID,Qi Yu2345,Wang Yueming12345ORCID

Affiliation:

1. Qiushi Academy for Advanced Studies, Zhejiang University

2. Nanhu Brain-Computer Interface Institute

3. College of Computer Science and Technology, Zhejiang University

4. The State Key Lab of Brain-Machine Intelligence, Zhejiang University

5. Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital and the MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of Medicine

Abstract

In motor cortex, behaviorally relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally relevant and irrelevant signals at both single-neuron and single-trial levels, but this approach remains elusive due to the unknown ground truth of behaviorally relevant signals. Therefore, we propose a framework to define, extract, and validate behaviorally relevant signals. Analyzing separated signals in three monkeys performing different reaching tasks, we found neural responses previously considered to contain little information actually encode rich behavioral information in complex nonlinear ways. These responses are critical for neuronal redundancy and reveal movement behaviors occupy a higher-dimensional neural space than previously expected. Surprisingly, when incorporating often-ignored neural dimensions, behaviorally relevant signals can be decoded linearly with comparable performance to nonlinear decoding, suggesting linear readout may be performed in motor cortex. Our findings prompt that separating behaviorally relevant signals may help uncover more hidden cortical mechanisms.

Funder

National Natural Science Foundation of China

Key Research and Development Program of Zhejiang

Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study

Fundamental Research Funds for the Central Universities

Publisher

eLife Sciences Publications, Ltd

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