A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes

Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016

Two brains in action: joint-action coding in the primate frontal cortex

Daily life often requires the coordination of our actions with those of another partner. After sixty years (1968-2018) of behavioral neurophysiology of motor control, the neural mechanisms which allow such coordination in primates are unknown. We studied this issue by recording cell activity simultaneously from dorsal premotor cortex (PMd) of two male interacting monkeys trained to coordinate their hand forces to achieve a common goal. We found a population of 'joint-action cells' that discharged preferentially when monkeys cooperated in the task. This modulation was predictive in nature, since in most cells neural activity led in time the changes of the "own" and of the "other" behavior. These neurons encoded the joint-performance more accurately than 'canonical action-related cells', activated by the action per se, regardless of the individual vs. interactive context. A decoding of joint-action was obtained by combining the two brains activities, using cells with directional properties distinguished from those associated to the 'solo' behaviors. Action observation-related activity studied when one monkey observed the consequences of the partner's behavior, i.e. the cursor's motion on the screen, did not sharpen the accuracy of 'joint-action cells' representation, suggesting that it plays no major role in encoding joint-action. When monkeys performed with a non-interactive partner, such as a computer, 'joint-action cells' representation of the "other" (non-cooperative) behavior was significantly degraded. These findings provide evidence of how premotor neurons integrate the time-varying representation of the self-action with that of a co-actor, thus offering a neural substrate for successful visuo-motor coordination between individuals.SIGNIFICANT STATEMENTThe neural bases of inter-subject motor coordination were studied by recording cell activity simultaneously from the frontal cortex of two interacting monkeys, trained to coordinate their hand forces to achieve a common goal. We found a new class of cells, preferentially active when the monkeys cooperated, rather than when the same action was performed individually. These 'joint-action neurons' offered a neural representation of joint-behaviors by far more accurate than that provided by the canonical action-related cells, modulated by the action per se regardless of the individual/interactive context. A neural representation of joint-performance was obtained by combining the activity recorded from the two brains. Our findings offer the first evidence concerning neural mechanisms subtending interactive visuo-motor coordination between co-acting agents

Brain computer interface based robotic rehabilitation with online modification of task speed

We present a systematic approach that enables online modification/adaptation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with motor imagery; however, instead of providing a binary classification output, we utilize posterior probabilities extracted from LDA classifier as the continuous-valued outputs to control a rehabilitation robot. Passive velocity field control (PVFC) is used as the underlying robot controller to map instantaneous levels of motor imagery during the movement to the speed of contour following tasks. In other words, PVFC changes the speed of contour following tasks with respect to intention levels of motor imagery. PVFC also allows decoupling of the task and the speed of the task from each other, and ensures coupled stability of the overall robot patient system. The proposed framework is implemented on AssistOn-Mobile - a series elastic actuator based on a holonomic mobile platform, and feasibility studies with healthy volunteers have been conducted test effectiveness of the proposed approach. Giving patients online control over the speed of the task, the proposed approach ensures active involvement of patients throughout exercise routines and has the potential to increase the efficacy of robot assisted therapies

A Neural Circuit Model for Prospective Control of Interceptive Reaching

Two prospective controllers of hand movements in catching -- both based on required velocity control -- were simulated. Under certain conditions, this required velocity controlled to overshoots of the future interception point. These overshoots were absent in pertinent experiments. To remedy this shortcoming, the required velocity model was reformulated in terms of a neural network, the Vector Integration To Endpoint model, to create a Required Velocity Integration To Endpoint modeL Addition of a parallel relative velocity channel, resulting in the Relative and Required Velocity Integration To Endpoint model, provided a better account for the experimentally observed kinematics than the existing, purely behavioral models. Simulations of reaching to intercept decelerating and accelerating objects in the presence of background motion were performed to make distinct predictions for future experiments.Vrije Universiteit (Gerrit-Jan van Jngen-Schenau stipend of the Faculty of Human Movement Sciences); Royal Netherlands Academy of Arts and Sciences; Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

Working memory revived in older adults by synchronizing rhythmic brain circuits

Published in final edited form as: Nat Neurosci. 2019 May ; 22(5): 820–827. doi:10.1038/s41593-019-0371-x.Understanding normal brain aging and developing methods to maintain or improve cognition in older adults are major goals of fundamental and translational neuroscience. Here we show a core feature of cognitive decline—working-memory deficits—emerges from disconnected local and long-range circuits instantiated by theta–gamma phase–amplitude coupling in temporal cortex and theta phase synchronization across frontotemporal cortex. We developed a noninvasive stimulation procedure for modulating long-range theta interactions in adults aged 60–76 years. After 25 min of stimulation, frequency-tuned to individual brain network dynamics, we observed a preferential increase in neural synchronization patterns and the return of sender–receiver relationships of information flow within and between frontotemporal regions. The end result was rapid improvement in working-memory performance that outlasted a 50 min post-stimulation period. The results provide insight into the physiological foundations of age-related cognitive impairment and contribute to groundwork for future non-pharmacological interventions targeting aspects of cognitive decline.Accepted manuscrip

Synchronous communication technologies for language learning: Promise and challenges in research and pedagogy

We propose a definition of synchronous communication based on joint attention, noting that in certain mediated communication settings joint attention is a matter of perception rather than determinable fact. The most salient properties of synchronous computer-mediated communication (SCMC) are real-time pressure to communicate and a greater degree of social presence relative to asynchronous communication. These properties underlie the benefits and challenges of SCMC for language learning, which we discuss under three headings: (1) SCMC as learning tool; (2) SCMC as target competence; and (3) SCMC as setting for learner dialogue, intracultural and intercultural. We survey research themes in SCMC and preview the contributions of the Special Issue. Finally, we identify questions for future research