Flexible sensorimotor computations through rapid reconfiguration of cortical dynamics

By Evan D. Remington, Devika Narain, Eghbal A. Hosseini, Mehrdad Jazayeri. 2018

272 citations recorded by the source corpus.

Sensorimotor computations can be flexibly adjusted according to internal states and contextual inputs. The mechanisms supporting this flexibility are not understood. Here, we tested the utility of a dynamical system perspective to approach this problem. In a dynamical system whose state is determined by interactions among neurons, computations can be rapidly and flexibly reconfigured by controlling the system‘s inputs and initial conditions. To investigate whether the brain employs such control strategies, we recorded from the dorsomedial frontal cortex (DMFC) of monkeys trained to measure time intervals and subsequently produce timed motor responses according to multiple context-specific stimulus-response rules. Analysis of the geometry of neural states revealed a control mechanism that relied on the system‘s inputs and initial conditions. A tonic input specified by the behavioral context adjusted firing rates throughout each trial, while the dynamics in the measurement epoch allowed the system to establish initial conditions for the ensuing production epoch. This initial condition in turn set the speed of neural dynamics in the production epoch allowing the animal to aim for the target interval. These results provide evidence that the language of dynamical systems can be used to parsimoniously link brain activity to sensorimotor computations.

Subfields: Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “In this view, neural circuits must learn to encode distinct stimuli with separated trajectories with minimal overlap due to noise or variability (Friedrich and Laurent, 2001; Stopfer et al., 2003; Broome et al., 2006; Shen et al., 2013; Zylberberg et al., 2016; Remington et al., 2018).”
Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity (2021, background): “b Population activity in the macaque dorsomedial frontal cortex reflecting flexible motor planning in a context-dependent timing task [10].”
Long-term stability of cortical population dynamics underlying consistent behavior (2020, unknown): “Within the motor cortex, recent work indicates that the supplementary motor area, a “higher” motor cortical area whose activity reflects motor timing 48 or sequence production 47 , exhibits more complex dynamics than what is consistently observed in M1 47 .”
If deep learning is the answer, what is the question? (2020, background): “1f) , judge the length of a time interval 47,48 (Fig.”
Organizing recurrent network dynamics by task-computation to enable continual learning (2020, background): “This dynamical systems view has offered insight into computational mechanisms underlying motor control [1], decision making [2–4] and timing tasks [5].”
Complementary roles of dimensionality and population structure in neural computations (2020, background): “A newly emerging paradigm has therefore posited that neural computations need instead to be understood in terms of collective dynamics in the state space of joint activity of all neurons [Buonomano and Maass, 2009; Rigotti et al., 2013; Mante et al., 2013; Gallego et al., 2017; Remington et al., 201”
Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks. (2017, unknown): “The cues for context A and B are represented by additional inputs along random vectors IctxA and IctxB , presented for the full length of the trial (Remington et al., 2018) (Fig.”