Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks.

By Francesca Mastrogiuseppe, Srdjan Ostojic. 2017

412 citations recorded by the source corpus.

Large-scale neural recordings have established that the transformation of sensory stimuli into motor outputs relies on low-dimensional dynamics at the population level, while individual neurons exhibit complex selectivity. Understanding how low-dimensional computations on mixed, distributed representations emerge from the structure of the recurrent connectivity and inputs to cortical networks is a major challenge. Here, we study a class of recurrent network models in which the connectivity is a sum of a random part and a minimal, low-dimensional structure. We show that, in such networks, the dynamics are low dimensional and can be directly inferred from connectivity using a geometrical approach. We exploit this understanding to determine minimal connectivity required to implement specific computations and find that the dynamical range and computational capacity quickly increase with the dimensionality of the connectivity structure. This framework produces testable experimental predictions for the relationship between connectivity, low-dimensional dynamics, and computational features of recorded neurons.

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

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Citations in the corpus

Organizing recurrent network dynamics by task-computation to enable continual learning (2020, background): “Such low-rank dynamical components have been shown to successfully implement complex cognitive tasks [26, 27].”
Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity (2021, background): “Nonetheless, recent work has begun to make progress in this direction by developing sophisticated nonlinear dimensionality reduction techniques and applying them to large-scale neural data [10,11,14,19,49], and by using artificial neural network models to generate testable hypotheses for how neural ”
Artificial Neural Networks for Neuroscientists: A Primer (2020, background): “For example, low-rank recurrent neural networks utilize recurrent connectivity matrices with low-dimensional structures (Mastrogiuseppe and Ostojic, 2018), allowing for a more straightforward mapping from network connectivity to dynamics and computation.”