A theory of multineuronal dimensionality, dynamics and measurement

By Peiran Gao, Eric M. Trautmann, Byron M. Yu, G. Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Surya Ganguli. 2017

358 citations recorded by the source corpus.

In many experiments, neuroscientists tightly control behavior, record many trials, and obtain trial-averaged firing rates from hundreds of neurons in circuits containing billions of behaviorally relevant neurons. Di-mensionality reduction methods reveal a striking simplicity underlying such multi-neuronal data: they can be reduced to a low-dimensional space, and the resulting neural trajectories in this space yield a remarkably insightful dynamical portrait of circuit computation. This simplicity raises profound and timely conceptual questions. What are its origins and its implications for the complexity of neural dynamics? How would the situation change if we recorded more neurons? When, if at all, can we trust dynamical portraits obtained from measuring an infinitesimal fraction of task relevant neurons? We present a theory that answers these questions, and test it using physiological recordings from reaching monkeys. This theory reveals conceptual insights into how task complexity governs both neural dimensionality and accurate recovery of dynamic portraits, thereby providing quantitative guidelines for future large-scale experimental design.

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

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Citations in the corpus

Orthogonal representations for robust context-dependent task performance in brains and neural networks (2022, neutral): “0) the similarity is mostly driven by the structure of the input space (including the task context) (Figure 3A); this is expected because the input weights remain close to their initial values and random high-dimensional projections approximately preserve distances between inputs (Gao et al., 2017).”
Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “Moreover, recent theoretical work has also demonstrated that the dimensionality of neural representations is necessarily limited by the complexity of the task involved (Gao et al., 2017).”
High-performance brain-to-text communication via handwriting (2021, methodology): “Spatial and temporal dimensionality were estimated using the ‘participation ratio’ of the principal component analysis (PCA) eigenvalue spectrum, which quantifies approximately how many spatial or temporal dimensions are required to explain 80% of the variance in the patterns of neural activity 23 .”
Long-term stability of cortical population dynamics underlying consistent behavior (2020, neutral): “Then, the manifold embedded within the empirical neural space is found; this transformation involves a dimensionality reduction from approximately 10 2 to 10 1 for the relatively simple reaching behaviors we and others have studied 8 .”
Direct-fit to nature: an evolutionary perspective on biological (and artificial) neural networks (2019, methodology): “In fact, when interrogating the over-parameterized model under these conditions, we simply recovered the task dimensions by which we constructed the experimental paradigm or training set (Gao et al., 2017; Stringer et al., 2019).”
Machine Learning for Neural Decoding (2017, background): “Moreover, the fact that the tasks we used had a low-dimensional structure, and therefore the neural data were also likely low dimensional (Gao et al., 2017), might allow high decoding performance with limited data.”
A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings (2023, unknown): “The PR has been shown to be a reasonable measure of neural dimensionality in PCA 42 .”