scholarly journals Mirror neurons are modulated by grip force and reward expectation in the sensorimotor cortices (S1, M1, PMd, PMv)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Md Moin Uddin Atique ◽  
Joseph Thachil Francis
Keyword(s):  
Neural Activity ◽  
Visual Information ◽  
Mirror Neurons ◽  
Grip Force ◽  
Neural Representation ◽  
Virtual Object ◽  
Time Lags ◽  
Subjective Value ◽  
Machine Interface ◽  
Definition Of

AbstractMirror Neurons (MNs) respond similarly when primates make or observe grasping movements. Recent work indicates that reward expectation influences rostral M1 (rM1) during manual, observational, and Brain Machine Interface (BMI) reaching movements. Previous work showed MNs are modulated by subjective value. Here we expand on the above work utilizing two non-human primates (NHPs), one male Macaca Radiata (NHP S) and one female Macaca Mulatta (NHP P), that were trained to perform a cued reward level isometric grip-force task, where the NHPs had to apply visually cued grip-force to move and transport a virtual object. We found a population of (S1 area 1–2, rM1, PMd, PMv) units that significantly represented grip-force during manual and observational trials. We found the neural representation of visually cued force was similar during observational trials and manual trials for the same units; however, the representation was weaker during observational trials. Comparing changes in neural time lags between manual and observational tasks indicated that a subpopulation fit the standard MN definition of observational neural activity lagging the visual information. Neural activity in (S1 areas 1–2, rM1, PMd, PMv) significantly represented force and reward expectation. In summary, we present results indicating that sensorimotor cortices have MNs for visually cued force and value.

scholarly journals Mirror Neurons are Modulated by Reward Expectation and Grip Force in the Sensorimotor Cortices (S1, M1, PMd, PMv)

2021 ◽  
Author(s):  
Md Moin Uddin Atique ◽  
Joseph Thachil Francis
Keyword(s):  
Neural Activity ◽  
Visual Information ◽  
Mirror Neurons ◽  
Grip Force ◽  
Neural Representation ◽  
Virtual Object ◽  
Time Lags ◽  
Subjective Value ◽  
Machine Interface ◽  
Definition Of

AbstractMirror Neurons (MN) respond similarly when primates make, or observe, grasping movements. Recent work indicates that reward expectation influences M1 during manual, observational, and Brain Machine Interface (BMI) reaching movements. Previous work showed MN are modulated by subjective value. Here we expand on the above work utilizing two non-human primates (NHPs), one male Macaca Radiata (NHP S) and one female Macaca Mulatta (NHP P), that were trained to perform a cued reward level isometric grip force task, where the NHPs had to apply visually cued grip force to move and transport a virtual object. We found a population of (S1, M1, PMd, PMv) units that significantly represented grip force during manual and observational trials. We found the neural representation of visually cued force was similar during observational trials and manual trials for the same units, however, the representation was weaker during observational trials. Comparing changes in neural time lags between manual and observational tasks indicated that a subpopulation fit the standard MN definition of observational neural activity lagging the visual information. Neural activity in (S1, M1, PMd, PMv) significantly represented force and reward expectation. In summary, we present results indicating that sensorimotor cortices have MN for visually cued force and value.

scholarly journals Working memory as persistent neural activity

2019 ◽  
Author(s):  
Joshua J. Foster ◽  
Edward K. Vogel ◽  
Ed Awh
Keyword(s):  
Working Memory ◽  
Cognitive Processing ◽  
Neural Activity ◽  
Operational Definition ◽  
Task Demands ◽  
Neural Representation ◽  
Long Term Memory ◽  
Intelligent Behavior ◽  
Dynamic Collaboration ◽  
Definition Of

Working memory (WM) is an online memory system that allows us to hold information “in mind” in service of ongoing cognitive processing. Here, we emphasize that short-term retention of information typically involves an interplay between WM and long-term memory (LTM), especially when task demands or interruptions divert our focus from remembered items. We suggest that active neural representation may distinguish between “online” representations in WM and “offline” representations in LTM. This perspective is at odds with “activity-silent” models of WM, which hold that WM representations can be sustained without persistent neural activity. We suggest that activity-silent representations might be more productively conceptualized as offline representations in LTM because accessing these representations shows multiple signatures of retrieval from LTM. Moreover, active neural traces track WM load, predict individual differences in performance, and respect sharp item limits in WM storage. Thus, we argue that using neural activity as an operational definition of WM may provide strong traction for studying the dynamic collaboration between WM and LTM that is critical for intelligent behavior.

A Multi-Step Neural Control for Motor Brain-Machine Interface by Reinforcement Learning

2013 ◽  
Vol 461 ◽  
pp. 565-569 ◽  
Author(s):  
Fang Wang ◽  
Kai Xu ◽  
Qiao Sheng Zhang ◽  
Yi Wen Wang ◽  
Xiao Xiang Zheng
Keyword(s):  
Reinforcement Learning ◽  
Neural Activity ◽  
Neural Control ◽  
Brain Machine Interface ◽  
Learning Approach ◽  
Complex Task ◽  
Neural Data ◽  
Neural Spikes ◽  
Performance Improvements ◽  
Machine Interface

Brain-machine interfaces (BMIs) decode cortical neural spikes of paralyzed patients to control external devices for the purpose of movement restoration. Neuroplasticity induced by conducting a relatively complex task within multistep, is helpful to performance improvements of BMI system. Reinforcement learning (RL) allows the BMI system to interact with the environment to learn the task adaptively without a teacher signal, which is more appropriate to the case for paralyzed patients. In this work, we proposed to apply Q(λ)-learning to multistep goal-directed tasks using users neural activity. Neural data were recorded from M1 of a monkey manipulating a joystick in a center-out task. Compared with a supervised learning approach, significant BMI control was achieved with correct directional decoding in 84.2% and 81% of the trials from naïve states. The results demonstrate that the BMI system was able to complete a task by interacting with the environment, indicating that RL-based methods have the potential to develop more natural BMI systems.

scholarly journals Intermittent Visuomotor Processing in the Human Cerebellum, Parietal Cortex, and Premotor Cortex

2006 ◽  
Vol 95 (2) ◽  
pp. 922-931 ◽  
Author(s):  
David E. Vaillancourt ◽  
Mary A. Mayka ◽  
Daniel M. Corcos
Keyword(s):  
Parietal Cortex ◽  
Visual Feedback ◽  
Visual Information ◽  
Grip Force ◽  
Premotor Cortex ◽  
Force Variability ◽  
Neural Activation ◽  
Control Force ◽  
Force Condition ◽  
Visuomotor Processing

The cerebellum, parietal cortex, and premotor cortex are integral to visuomotor processing. The parameters of visual information that modulate their role in visuomotor control are less clear. From motor psychophysics, the relation between the frequency of visual feedback and force variability has been identified as nonlinear. Thus we hypothesized that visual feedback frequency will differentially modulate the neural activation in the cerebellum, parietal cortex, and premotor cortex related to visuomotor processing. We used functional magnetic resonance imaging at 3 Tesla to examine visually guided grip force control under frequent and infrequent visual feedback conditions. Control conditions with intermittent visual feedback alone and a control force condition without visual feedback were examined. As expected, force variability was reduced in the frequent compared with the infrequent condition. Three novel findings were identified. First, infrequent (0.4 Hz) visual feedback did not result in visuomotor activation in lateral cerebellum (lobule VI/Crus I), whereas frequent (25 Hz) intermittent visual feedback did. This is in contrast to the anterior intermediate cerebellum (lobule V/VI), which was consistently active across all force conditions compared with rest. Second, confirming previous observations, the parietal and premotor cortices were active during grip force with frequent visual feedback. The novel finding was that the parietal and premotor cortex were also active during grip force with infrequent visual feedback. Third, right inferior parietal lobule, dorsal premotor cortex, and ventral premotor cortex had greater activation in the frequent compared with the infrequent grip force condition. These findings demonstrate that the frequency of visual information reduces motor error and differentially modulates the neural activation related to visuomotor processing in the cerebellum, parietal cortex, and premotor cortex.

scholarly journals Informative neural representations of unseen objects during higher-order processing in human brains and deep artificial networks

2021 ◽  
Author(s):  
Ning Mei ◽  
Roberto Santana ◽  
David Soto
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Model Simulation ◽  
Conscious Experience ◽  
Neural Representation ◽  
Visual Stream ◽  
Order Processing ◽  
High Level ◽  
Unseen Objects ◽  
Human Brains

AbstractDespite advances in the neuroscience of visual consciousness over the last decades, we still lack a framework for understanding the scope of unconscious processing and how it relates to conscious experience. Previous research observed brain signatures of unconscious contents in visual cortex, but these have not been identified in a reliable manner, with low trial numbers and signal detection theoretic constraints not allowing to decisively discard conscious perception. Critically, the extent to which unconscious content is represented in high-level processing stages along the ventral visual stream and linked prefrontal areas remains unknown. Using a within-subject, high-precision, highly-sampled fMRI approach, we show that unconscious contents, even those associated with null sensitivity, can be reliably decoded from multivoxel patterns that are highly distributed along the ventral visual pathway and also involving prefrontal substrates. Notably, the neural representation in these areas generalised across conscious and unconscious visual processing states, placing constraints on prior findings that fronto-parietal substrates support the representation of conscious contents and suggesting revisions to models of consciousness such as the neuronal global workspace. We then provide a computational model simulation of visual information processing/representation in the absence of perceptual sensitivity by using feedforward convolutional neural networks trained to perform a similar visual task to the human observers. The work provides a novel framework for pinpointing the neural representation of unconscious knowledge across different task domains.

scholarly journals Subjective value and decision entropy are jointly encoded by aligned gradients across the human brain

2020 ◽  
Author(s):  
Sebastian Bobadilla-Suarez ◽  
Olivia Guest ◽  
Bradley C. Love
Keyword(s):  
Prefrontal Cortex ◽  
Human Brain ◽  
Ventromedial Prefrontal Cortex ◽  
Neural Representation ◽  
Retrospective Evaluation ◽  
Systematic Fashion ◽  
Subjective Value ◽  
Fmri Analysis ◽  
The Relationship ◽  
The Brain

AbstractRecent work has considered the relationship between value and confidence in both behavior and neural representation. Here we evaluated whether the brain organizes value and confidence signals in a systematic fashion that reflects the overall desirability of options. If so, regions that respond to either increases or decreases in both value and confidence should be widespread. We strongly confirmed these predictions through a model-based fMRI analysis of a mixed gambles task that assessed subjective value (SV) and inverse decision entropy (iDE), which is related to confidence. Purported value areas more strongly signalled iDE than SV, underscoring how intertwined value and confidence are. A gradient tied to the desirability of actions transitioned from positive SV and iDE in ventromedial prefrontal cortex to negative SV and iDE in dorsal medial prefrontal cortex. This alignment of SV and iDE signals could support retrospective evaluation to guide learning and subsequent decisions.

scholarly journals Robust and distributed neural representation of action values

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Eun Ju Shin ◽  
Yunsil Jang ◽  
Soyoun Kim ◽  
Hoseok Kim ◽  
Xinying Cai ◽  
...  
Keyword(s):  
Neural Activity ◽  
Statistical Tests ◽  
Brain Structures ◽  
Surrogate Data ◽  
Neural Representation ◽  
Decision Variables ◽  
Multiple Regions ◽  
Serial Correlations ◽  
Action Value ◽  
The Brain

Studies in rats, monkeys, and humans have found action-value signals in multiple regions of the brain. These findings suggest that action-value signals encoded in these brain structures bias choices toward higher expected rewards. However, previous estimates of action-value signals might have been inflated by serial correlations in neural activity and also by activity related to other decision variables. Here, we applied several statistical tests based on permutation and surrogate data to analyze neural activity recorded from the striatum, frontal cortex, and hippocampus. The results show that previously identified action-value signals in these brain areas cannot be entirely accounted for by concurrent serial correlations in neural activity and action value. We also found that neural activity related to action value is intermixed with signals related to other decision variables. Our findings provide strong evidence for broadly distributed neural signals related to action value throughout the brain.

Manifold Visual Working Memory

2020 ◽  
pp. 311-332
Author(s):  
Nicole Hakim ◽  
Edward Awh ◽  
Edward K. Vogel
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Neural Activity ◽  
Long Term Memory ◽  
Term Memory ◽  
Memory Representations ◽  
Latent Representations ◽  
Definition Of ◽  
Spatial Locations

Visual working memory allows us to maintain information in mind for use in ongoing cognition. Research on visual working memory often characterizes it within the context of its interaction with long-term memory (LTM). These embedded-processes models describe memory representations as existing in three potential states: inactivated LTM, including all representations stored in LTM; activated LTM, latent representations that can quickly be brought into an active state due to contextual priming or recency; and the focus of attention, an active but sharply limited state in which only a small number of items can be represented simultaneously. This chapter extends the embedded-processes framework of working memory. It proposes that working memory should be defined operationally based on neural activity. By defining working memory in this way, the important theoretical distinction between working memory and LTM is maintained, while still acknowledging that they operate together. It is additionally proposed that active working memory should be further subdivided into at least two subcomponent processes that index item-based storage and currently prioritized spatial locations. This fractionation of working memory is based on recent research that has found that the maintenance of information distinctly relies on item-based representations as well as prioritization of spatial locations. It is hoped that this updated framework of the definition of working memory within the embedded-processes model provides further traction for understanding how we maintain information in mind.

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