Reorganization between preparatory and movement population responses in motor cortex

By Gamaleldin F. Elsayed, Antonio H. Lara, Matthew T. Kaufman, Mark M. Churchland, John P. Cunningham. Nature Communications, 2016

409 citations recorded by the source corpus.

Neural populations can change the computation they perform on very short timescales. Although such flexibility is common, the underlying computational strategies at the population level remain unknown. To address this gap, we examined population responses in motor cortex during reach preparation and movement. We found that there exist exclusive and orthogonal population-level subspaces dedicated to preparatory and movement computations. This orthogonality yielded a reorganization in response correlations: the set of neurons with shared response properties changed completely between preparation and movement. Thus, the same neural population acts, at different times, as two separate circuits with very different properties. This finding is not predicted by existing motor cortical models, which predict overlapping preparation-related and movement-related subspaces. Despite orthogonality, responses in the preparatory subspace were lawfully related to subsequent responses in the movement subspace. These results reveal a population-level strategy for performing separate but linked computations. Single neuron responses are highly complex and dynamic yet they are able to flexibly represent behaviour through their collective activity. Here the authors demonstrate that population activity patterns of motor cortex neurons are orthogonal during successive task epochs that are linked through a simple linear function.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/aa7b61e99f23d3ab3ff0fa575b1a39fb7e7d7344

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Citations in the corpus

Similarity of Neural Network Representations Revisited (2019, background): “…brain areas (Haxby et al., 2001; Freiwald & Tsao, 2010), individuals (Connolly et al., 2012), species (Kriegeskorte et al., 2008b), and behaviors (Elsayed et al., 2016), as well as between brains and neural networks (Yamins et al., 2014; Khaligh-Razavi & Kriegeskorte, 2014; Sussillo et al.,…”
Towards the neural population doctrine. (2019, background): “Other motor studies in varied tasks have also succeeded in relating the motor cortex activity to artiﬁcial nodes in an ANN [38 ,36].”
Causes and consequences of representational drift (2019, background): “behavioral contexts, for example motor planning and execution [28,26,29].”
Cortical areas interact through a communication subspace (2019, positive): “Two important recent studies have investigated the relationship between activity in motor cortex and muscles (Kaufman et al., 2014; Elsayed et al., 2016).”
Motor cortex embeds muscle-like commands in an untangled population response (2018, positive): “Tangling across Tasks, Species, and Areas Is low neural- versus muscle-trajectory tangling specific to cycling or a more general property of motor cortex? We leveraged recently collected data (Elsayed et al., 2016) from two monkeys performing a center-out reach task.”
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models (2021, result): “These findings are consistent with previous reports of monkey M1 partitioning preparatory and movement-related activity into distinct subspaces [14, 32], as well as with the analogous finding that a ‘relative target’ subspace is active before a ‘movement subspace’ in previous analyses of this partic”
Computation Through Neural Population Dynamics (2020, background): “Kaufman et al. (2014) together with Elsayed et al. (2016) found evidence for such a mechanism in motor cortex: Outputpotent dimensions presumably relay information to muscles and are preferentially active during movement, and output-null dimensions are preferentially active during preparation.”