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

2018 ◽  
Author(s):  
Diogo Peixoto ◽  
Roozbeh Kiani ◽  
Chandramouli Chandrasekaran ◽  
Stephen I. Ryu ◽  
Krishna V. Shenoy ◽  
...  
Keyword(s):  
Decision Making ◽  
Stimulus Duration ◽  
Primary Motor Cortex ◽  
Population Level ◽  
Motor Preparation ◽  
Neural Population ◽  
Decision Making Process ◽  
Decision Variable ◽  
Single Trial ◽  
Population Activity

SummaryStudies 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.

scholarly journals A deep learning framework for inference of single-trial neural population activity from calcium imaging with sub-frame temporal resolution

2021 ◽  
Author(s):  
Feng Zhu ◽  
Harrison A Grier ◽  
Raghav Tandon ◽  
Changjia Cai ◽  
Andrea Giovannucci ◽  
...  
Keyword(s):  
Deep Learning ◽  
Temporal Resolution ◽  
Calcium Imaging ◽  
Temporal Dynamics ◽  
Population Level ◽  
Neural Population ◽  
Single Trial ◽  
Population Activity ◽  
Temporal Precision ◽  
Network States

In many brain areas, neural populations act as a coordinated network whose state is tied to behavior on a moment-by-moment basis and millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe network-scale computation, as it can measure the activity of many individual neurons, monitor multiple layers simultaneously, and sample from identified cell types. However, estimating network states and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities, and limitations on temporal resolution. Here we describe RADICaL, a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically-recorded spikes. It incorporates a novel network training strategy that exploits the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers network states more accurately than previous methods, particularly for high-frequency components. In real 2p recordings from sensorimotor areas in mice performing a "water grab" task, RADICaL infers network states with close correspondence to single-trial variations in behavior, and maintains high-quality inference even when neuronal populations are substantially reduced.

scholarly journals Neural population dynamics in motor cortex are different for reach and grasp

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Aneesha K Suresh ◽  
James M Goodman ◽  
Elizaveta V Okorokova ◽  
Matthew Kaufman ◽  
Nicholas G Hatsopoulos ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Population Level ◽  
Basic Structure ◽  
Neuronal Population ◽  
Neural Population ◽  
Population Activity ◽  
Muscle Activations ◽  
Low Dimensional ◽  
Analytical Approaches

Low-dimensional linear dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to give rise to movement. In the present study, we examine whether similar dynamics are also observed during grasping movements, which involve fundamentally different patterns of kinematics and muscle activations. Using a variety of analytical approaches, we show that M1 does not exhibit such dynamics during grasping movements. Rather, the grasp-related neuronal dynamics in M1 are similar to their counterparts in somatosensory cortex, whose activity is driven primarily by afferent inputs rather than by intrinsic dynamics. The basic structure of the neuronal activity underlying hand control is thus fundamentally different from that underlying arm control.

scholarly journals NEURAL POPULATION DYNAMICS IN MOTOR CORTEX ARE DIFFERENT FOR REACH AND GRASP

2019 ◽  
Author(s):  
Aneesha K. Suresh ◽  
James M. Goodman ◽  
Elizaveta V. Okorokova ◽  
Matthew T. Kaufman ◽  
Nicholas G. Hatsopoulos ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Population Level ◽  
Pattern Generator ◽  
Neuronal Population ◽  
Rotational Dynamics ◽  
Neural Population ◽  
Population Activity ◽  
Reach And Grasp ◽  
Smooth Dynamics

AbstractRotational dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to produce movement. Here, we show that M1 does not exhibit smooth dynamics during grasping movements, suggesting a more input-driven circuit.

Understanding Cooperation in a Single-trial Prisoner's Dilemma Game: Interactions Among Three Conditions

2013 ◽  
Vol 41 (5) ◽  
pp. 721-729 ◽  
Author(s):  
Yang Xi ◽  
Tingyan Li ◽  
Yong Zheng
Keyword(s):  
Decision Making ◽  
Risk Taking ◽  
Perceived Control ◽  
Cognitive Process ◽  
Prisoner's Dilemma ◽  
Prisoner’S Dilemma ◽  
Decision Making Process ◽  
Single Trial ◽  
Direct Measurements ◽  
Potential Conditions

In the present study we investigated the mechanism inherent in the decision-making process in a single-trial with the game called Prisoner's Dilemma (PD), in which players might simultaneously encounter different conditions that influence cooperation: namely, reciprocity, perceived control, and risk taking. We conducted 2 experiments to examine how, and to what extent, these conditions contribute to cooperation. By incorporating direct measurements of the potential conditions into a paradigm produced in previous studies, we were able to test the reliability of backward inference and to quantify the effect exerted by each condition. Results consistently revealed a complex cognitive process in single-trial PD: each condition had an influence on decision making, but none of them showed a dominating power to prompt cooperation independently; reciprocity served as a moderator.

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

2017 ◽  
Author(s):  
Chethan Pandarinath ◽  
Daniel J. O’Shea ◽  
Jasmine Collins ◽  
Rafal Jozefowicz ◽  
Sergey D. Stavisky ◽  
...  
Keyword(s):  
Nonlinear Dynamical System ◽  
Simultaneous Recording ◽  
Neural Population ◽  
Single Trial ◽  
Population Activity ◽  
Nonlinear Dynamical ◽  
System A ◽  
Behavioral Choices ◽  
Neural Spiking ◽  
Human Motor

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 population’s 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.

scholarly journals Brain mapping for lesions in primary motor cortex: integrated surgical approach

2019 ◽  
Vol 21 (Supplement_4) ◽  
pp. iv18-iv18
Author(s):  
José Lavrador ◽  
Prajwal Ghimire ◽  
Christian Brogna ◽  
Luciano Furlanetti ◽  
Sabina Patel ◽  
...  
Keyword(s):  
Decision Making ◽  
Process Model ◽  
Primary Motor Cortex ◽  
Treatment Plan ◽  
Extent Of Resection ◽  
Motor Deficit ◽  
Decision Making Process ◽  
Total Resection ◽  
Intraoperative Mapping ◽  
Subcortical Mapping

Abstract Background Lesions within the corticospinal tract (CST) represent significant surgical challenge with a delicate functional trade-off that should be integrated in the overall patient-centred treatment plan. Methods Patients with lesions within the CST with preoperative cortical and subcortical mapping (nTMS and tractography), intraoperative mapping and intraoperative provisional histological information (smear +- 5-ALA) were included. This independently acquired information was integrated in a decision-making process model to determine the intraoperative extent of resection. Results 10 patients (6 patients with metastatic precentral tumour; 1 patient with Grade III and 2 patients with grade IV gliomas; 1 patient with precentral vascular malformation) were included in the study. Majority of the patient had pre-operative motor deficit (60%). 50% patients underwent gross total resection and 40% patients underwent near total resection of the lesions. In only one patient, no surgical resection was possible after both pre-operative and intraoperative mapping. 70% of patients remained stable postoperatively and 20% improved from previous motor weakness. Conclusion The independently acquired anatomical (anatomical MRI) and functional (nTMS and tractography) in patients with CST lesions provide a more accurate guide for resection. The inclusion of the histological information (smear +- 5-ALA) further allows the surgical team to balance the potential functional risks within the global treatment plan. Therefore, the patient is kept at the centre of the informed decision making process.

Pre- and Intraoperative Mapping for Tumors in the Primary Motor Cortex: Decision-Making Process in Surgical Resection

2020 ◽  
Author(s):  
José Pedro Lavrador ◽  
Prajwal Ghimire ◽  
Christian Brogna ◽  
Luciano Furlanetti ◽  
Sabina Patel ◽  
...  
Keyword(s):  
Decision Making ◽  
Motor Cortex ◽  
Surgical Resection ◽  
Process Model ◽  
Primary Motor Cortex ◽  
Treatment Plan ◽  
Motor Deficit ◽  
Decision Making Process ◽  
Total Resection ◽  
Intraoperative Mapping

Abstract Background Lesions within the primary motor cortex (M1) and the corticospinal tract (CST) represent a significant surgical challenge with a delicate functional trade-off that should be integrated in the overall patient-centered treatment plan. Methods Patients with lesions within the M1 and CST with preoperative cortical and subcortical mapping (navigated transcranial magnetic stimulation [nTMS] and tractography), intraoperative mapping, and intraoperative provisional histologic information (smear with and without 5-aminolevulinic acid [5-ALA]) were included. This independently acquired information was integrated in a decision-making process model to determine the intraoperative extent of resection. Results A total of 10 patients (6 patients with metastatic precentral tumor; 1 patient with grade III and 2 patients with grade IV gliomas; 1 patient with precentral cavernoma) were included in the study. Most of the patients (60%) had a preoperative motor deficit. The nTMS documented M1 invasion in all cases, and in eight patients, the lesions were embedded within the CST. Overall, 70% of patients underwent gross total resection; 20% of patients underwent near-total resection of the lesions. In only one patient was no surgical resection possible after both preoperative and intraoperative mapping. Overall, 70% of patients remained stable postoperatively, and previous motor weakness improved in 20%. Conclusion The independently acquired anatomical (anatomical MRI) and functional (nTMS and tractography) tests in patients with CST lesions provide a useful guide for resection. The inclusion of histologic information (smear with or without 5-ALA) further allows the surgical team to balance the potential functional risks within the global treatment plan. Therefore, the patient is kept at the center of the informed decision-making process.

scholarly journals Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity

2009 ◽  
Vol 102 (1) ◽  
pp. 614-635 ◽  
Author(s):  
Byron M. Yu ◽  
John P. Cunningham ◽  
Gopal Santhanam ◽  
Stephen I. Ryu ◽  
Krishna V. Shenoy ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Gaussian Process ◽  
Predictive Ability ◽  
Neural Population ◽  
Single Trial ◽  
Two Stage ◽  
Population Activity ◽  
Process Factor ◽  
Low Dimensional ◽  
Neural Population Activity

We consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from many neurons on individual experimental trials. Beyond the benefit of visualizing the high-dimensional, noisy spiking activity in a compact form, such trajectories can offer insight into the dynamics of the neural circuitry underlying the recorded activity. Current methods for extracting neural trajectories involve a two-stage process: the spike trains are first smoothed over time, then a static dimensionality-reduction technique is applied. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way and that account for spiking variability, which may vary both across neurons and across time. We then present a novel method for extracting neural trajectories—Gaussian-process factor analysis (GPFA)—which unifies the smoothing and dimensionality-reduction operations in a common probabilistic framework. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that the proposed extensions improved the predictive ability of the two-stage methods. The predictive ability was further improved by going to GPFA. From the extracted trajectories, we directly observed a convergence in neural state during motor planning, an effect that was shown indirectly by previous studies. We then show how such methods can be a powerful tool for relating the spiking activity across a neural population to the subject's behavior on a single-trial basis. Finally, to assess how well the proposed methods characterize neural population activity when the underlying time course is known, we performed simulations that revealed that GPFA performed tens of percent better than the best two-stage method.

scholarly journals Motor cortex signals corresponding to the two arms are shared across hemispheres, mixed among neurons, yet partitioned within the population response

2019 ◽  
Author(s):  
K. Cora Ames ◽  
Mark M. Churchland
Keyword(s):  
Motor Cortex ◽  
Neural Activity ◽  
Muscle Activity ◽  
Primary Motor Cortex ◽  
Population Level ◽  
Response Patterns ◽  
Neural Signals ◽  
Population Activity ◽  
The Individual

AbstractPrimary motor cortex (M1) has lateralized outputs, yet M1 neurons can be active during movements of either arm. What is the nature and role of activity in the two hemispheres? When one arm moves, are the contralateral and ipsilateral cortices performing similar or different computations? When both hemispheres are active, how does the brain avoid moving the “wrong” arm? We recorded muscle and neural activity bilaterally while two male monkeys (Macaca mulatta) performed a cycling task with one or the other arm. Neurons in both hemispheres were active during movements of either arm. Yet response patterns were arm-dependent, raising two possibilities. First, the nature of neural signals may differ (e.g., be high versus low-level) depending on whether the ipsilateral or contralateral arm is used. Second, the same population-level signals may be present regardless of the arm being used, but be reflected differently at the individual-neuron level. The data supported this second hypothesis. Muscle activity could be predicted by neural activity in either hemisphere. More broadly, we failed to find signals unique to the hemisphere contralateral to the moving arm. Yet if the same signals are shared across hemispheres, how do they avoid impacting the wrong arm? We found that activity related to the two arms occupied distinct, orthogonal subspaces of population activity. As a consequence, a linear decode of contralateral muscle activity naturally ignored signals related to the ipsilateral arm. Thus, information regarding the two arms is shared across hemispheres and neurons, but partitioned at the population level.

scholarly journals An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability

2018 ◽  
Author(s):  
Ziqiang Wei ◽  
Hidehiko Inagaki ◽  
Nuo Li ◽  
Karel Svoboda ◽  
Shaul Druckmann
Keyword(s):  
Decision Making ◽  
Brain Function ◽  
Premotor Cortex ◽  
Neural Correlates ◽  
Neural Population ◽  
Behavioral Variability ◽  
Perceptual Decision Making ◽  
Population Activity ◽  
Long Time ◽  
Dynamical Nature

AbstractAnimals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is understanding the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g. decision making), neuronal heterogeneity and signal to noise difficulties. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics was able to reveal previously unrecognized structure in the organization of population activity. This structure was similar on error and correct trials, suggesting what may be the underlying circuit mechanisms, was able to predict multiple aspects of behavioral variability and revealed long time-scale modulation of population activity.

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