Biology subjects

Shenoy, K. V.

Publications and source records attributed to Shenoy, K. V..

6 recordsLinked to original sources

bioRxiv2018

Macaque dorsal premotor cortex exhibits decision-related activity only when specific stimulus-response associations are known

How deliberation on sensory cues and action selection interact in decision-related brain areas is still not well understood. Here, monkeys reached to one of two targets, whose colors alternated randomly between trials, by discriminating the dominant color of a checkerboard cue composed of different numbers of squares of the two target colors in different trials. In a \"Targets First\" task the colored targets appeared first, followed by the checkerboard; in a \"Checkerboard First\" task, this order was reversed. After both cues appeared in both tasks, responses of dorsal premotor cortex (PMd) neurons covaried with action choices, strength of evidence for action choices, and RTs--- hallmarks of decision-related activity. However, very few neurons were modulated by checkerboard color composition or the color of the chosen target, even in the Checkerboard First task. These findings implicate PMd in the action-selection but not the perceptual components of the decision-making process in these tasks.

neuroscience↗

bioRxiv2018

Frequency shifts and depth dependence of premotor beta band activity during perceptual decision-making

Neural activity in the premotor and motor cortex shows prominent structure in the beta frequency range (13-30 Hz). Currently, the behavioral relevance of beta band activity (BBA) in premotor and motor regions is not well understood. The underlying source of motor BBA and how it changes as a function of cortical depth is also unknown. Here, we addressed these unresolved questions by investigating BBA recorded using laminar electrodes in the dorsal premotor cortex (PMd) of two male rhesus macaques performing a visual reaction time (RT) reach discrimination task. We observed robust BBA before and after the onset of the visual stimulus but not during the arm movement. While post-stimulus BBA was positively correlated with RT throughout the beta frequency range, pre-stimulus correlation varied by frequency. Low beta frequencies (~15 to 20 Hz) were positively correlated with RT and high beta frequencies (~25 to 30 Hz) were negatively correlated with RT. Simulations suggested that these frequency-dependent correlations could be due to a shift in the component frequencies of the pre-stimulus BBA as a function of RT, such that faster RTs are accompanied by greater power in high beta frequencies. We also observed a laminar dependence of BBA, with deeper electrodes demonstrating stronger power in low beta frequencies both pre- and post-stimulus. The heterogeneous nature of BBA and the changing relationship between BBA and RT in different task epochs may be a sign of the differential network dynamics involved in expectation, decision-making, and motor preparation.

neuroscience↗

bioRxiv2018

Population dynamics of choice representation in dorsal premotor and primary motor cortex

Studies in multiple species have revealed the existence of neural signals that lawfully co-vary with different aspects of the decision-making process, including choice, sensory evidence that supports the choice, and reaction time. These signals, often interpreted as the representation of a decision variable (DV), have been identified in several motor preparation circuits and provide insight about mechanisms underlying the decision-making process. However, single-trial dynamics of this process or its representation at the neural population level remain poorly understood. Here, we examine the representation of the DV in simultaneously recorded neural populations of dorsal premotor (PMd) and primary motor (M1) cortices of monkeys performing a random dots direction discrimination task with arm movements as the behavioral report. We show that single-trial DVs covary with stimulus difficulty in both areas but are stronger and appear earlier in PMd compared to M1 when the stimulus duration is fixed and predictable. When temporal uncertainty is introduced by making the stimulus duration variable, single-trial DV dynamics are accelerated across the board and the two areas become largely indistinguishable throughout the entire trial. These effects are not trivially explained by the faster emergence of motor kinematic signals in PMd and M1. All key aspects of the data were replicated by a computational model that relies on progressive recruitment of units with stable choice-related modulation of neural population activity. In contrast with several recent results in rodents, decision signals in PMd and M1 are not carried by short sequences of activity in non-overlapping groups of neurons but are instead distributed across many neurons, which once recruited, represent the decision stably during individual behavioral epochs of the trial.

neuroscience↗

bioRxiv2017

Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis

Perceptions, thoughts and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor components analysis (TCA) can meet this challenge by extracting three interconnected low dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.

neuroscience↗

bioRxiv2017

ERAASR: An algorithm for removing electrical stimulation artifacts from multielectrode array recordings

Electrical stimulation is a widely used and effective tool in systems neuroscience, neural prosthetics, and clinical neurostimulation. However, electrical artifacts evoked by stimulation significantly complicate the detection of spiking activity on nearby recording electrodes. Here, we present ERAASR: an algorithm for Estimation and Removal of Artifacts on Arrays via Sequential principal components Regression. This approach leverages the similar structure of artifact transients, but not spiking activity, across simultaneously recorded channels on the array, across pulses within a train, and across trials. The effectiveness of the algorithm is demonstrated in macaque dorsal premotor cortex using acute linear multielectrode array recordings and single electrode stimulation. Large electrical artifacts appeared on all channels during stimulation. After application of ERAASR, the cleaned signals were quiescent on channels with no spontaneous spiking activity, whereas spontaneously active channels exhibited evoked spikes which closely resembled spontaneously occurring spiking waveforms. The ERAASR algorithm requires no special hardware and comprises sequential application of straightforward linear methods with intuitive parameters. Enabling simultaneous electrical stimulation and multielectrode array recording can help elucidate the causal links between neural activity and cognitive functions and enable the design and implementation of novel sensory protheses.

neuroscience↗

bioRxiv2017

Inferring single-trial neural population dynamics using sequential auto-encoders

Neuroscience is experiencing a data revolution in which simultaneous recording of many hundreds or thousands of neurons is revealing structure in population activity that is not apparent from single-neuron responses. This structure is typically extracted from trial-averaged data. Single-trial analyses are challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. Here we introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer latent dynamics from single-trial neural spiking data. LFADS uses a nonlinear dynamical system (a recurrent neural network) to infer the dynamics underlying observed population activity and to extract de-noised single-trial firing rates from neural spiking data. We apply LFADS to a variety of monkey and human motor cortical datasets, demonstrating its ability to predict observed behavioral variables with unprecedented accuracy, extract precise estimates of neural dynamics on single trials, infer perturbations to those dynamics that correlate with behavioral choices, and combine data from non-overlapping recording sessions (spanning months) to improve inference of underlying dynamics. In summary, LFADS leverages all observations of a neural populations activity to accurately model its dynamics on single trials, opening the door to a detailed understanding of the role of dynamics in performing computation and ultimately driving behavior.

neuroscience↗