P017 Unraveling the neural mechanisms of behavior-related manifolds in a comprehensive model of primary motor cortex circuits
Roman Baravalle1*, Valery Bragin1,5, Nikita Novikov4, Wei Xu2, Eugenio Urdapilleta3, Ian Duguid2, Salvador Dura-Bernal1,4
1 Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, USA
2 Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
3 Centro Atómico Bariloche & Instituto Balseiro, Bariloche, Argentina
4 Center for Biomedical Imaging & Neuromodulation, The Nathan Kline Institute for Psychiatric Research
5 Brain Simulation Section, Charité - Universitätsmedizin Berlin, Berlin, Germany
*Corresponding Author: roman.baravalle@downstate.edu
Introduction
Accumulating evidence suggests that low-dimensional neural manifolds in the primary motor cortex (M1) play a crucial role in generating motor behavior. These latent dynamics, emerging from the collective activity of M1 neurons, are remarkably consistent across animals performing the same task. However, the specific cell types, cortical layers, and biophysical mechanisms underlying these representations remain largely unknown. Understanding these manifolds is essential for characterizing neural computations underlying behavior and has implications for developing stable and easy-to-train brain-machine interfaces (BMIs) for spinal cord injury.
Methods
We previously developed a realistic computational model of M1 circuits on NetPyNE/NEURON [1], incorporating detailed corticospinal neuron models responsible for transmitting motor commands to the spinal cord. This model was validated against in vivo spiking and local field potential data, demonstrating its ability to generate accurate predictions and provide insights into brain diseases. We further showed that M1 activity could be represented in low-dimensional manifolds, which varied according to behavioral states and experimental manipulations. These embeddings revealed clear clustering related to behavior and inactivation experiments (e.g., noradrenergic or thalamic input lesions), with high correlations between low- and high-dimensional representations.
Results
In this work, we extended the M1 model by incorporating two new interneuron types and tuning it to reproduce neural manifolds observed in vivo during a mouse joystick reaching task. Neuropixels probes recorded spiking activity in M1 and the ventrolateral thalamus, allowing us to jointly analyze neural patterns and joystick trajectories. We constructed a decoder using the CEBRA method [2] to predict movement trajectories from spiking activity and LFP and explored different model tuning strategies, including varying long-range inputs and modifying circuit connectivity via global optimization.
Discussion
Reproducing experimental behavior-related neural manifolds in large-scale cortical models enables linking neural dynamics across scales (membrane voltages, spikes, LFPs, EEG) to behavior, experimental manipulations, and disease. This approach helps refine models, characterize the relationship between latent dynamics and specific cell types, and ultimately deepen our understanding of how brain circuits generate motor behavior.
Acknowledgements
This work is supported by NIBIB U24EB028998 and NYS DOH1-C32250GG-3450000 grants
References
[1]https://doi.org/10.1016/j.celrep.2023.112574[2]https://doi.org/10.1038/s41586-023-06031-6
