Biology subjects

Dominey, P. F.

Publications and source records attributed to Dominey, P. F..

5 recordsLinked to original sources

bioRxiv2025

Following the Robot's Lead: Predicting Human and Robot Movement from EEG in a Motor Learning HRI Task

Human-robot interaction (HRI) offers unique opportunities to study the neuroscience of human motor control through controlled and reproducible sensory stimuli. In this study, we introduce an innovative neuroscience-HRI framework inspired by the Serial Reaction Time (SRT) task, that combines EEG with a task where a humanoid robot performs preprogrammed movement sequences that are mirrored by a human participant in real time. The use of a humanoid robot ensures precise and repeatable sensory-motor stimuli in the 3D peripersonal space of the participant, providing experimental conditions that may be challenging to replicate with traditional methods. Behavioral performance is assessed by measuring the temporal lag between human and robot movements, which decreases with training, reflecting motor sequence learning. Concurrently, EEG data from the human participant is analyzed to reveal neural correlates of learning and movement dynamics. Event-Related Spectral Perturbations (ERSP) in theta, mu, and beta frequency bands demonstrate distinct patterns associated with rest, fixation, and movement. Furthermore, the ERSP changes over successive trials reflect the progression of sequence learning, highlighting the relationship between neural oscillations and motor learning. A Markov-Switching Linear Regression model further decodes EEG signals to predict movement parameters including both human and robot position and velocity in a time-resolved manner. Our findings highlight the potential of HRI as a robust platform for neuroscience research and underscore the value of EEG-based neural decoding in understanding motor sequence learning. This work suggests further advances for integrating robotics into neuroscience and rehabilitation research.

neuroscience↗

bioRxiv2022

Effects of Connectivity on Narrative Temporal Processing in Structured Reservoir Computing

Computational models of language are having an increasing impact in understanding the neural bases of language processing in humans. A recent model of cortical dynamics based on reservoir computing was able to account for temporal aspects of human narrative processing as revealed by fMRI. In this context the current research introduces a form of structured reservoir computing, where network dynamics are further constrained by the connectivity architecture in order to begin to explain large scale hierarchical network properties of human cortical activity during narrative comprehension. Cortical processing takes place at different time scales depending on the position in a "hierarchy" from posterior sensory input areas to higher level associative frontal cortical areas. This phenomena is likely related to the cortical connectivity architecture. Recent studies have identified heterogeneity in this posterior-anterior hierarchy, with certain frontal associative areas displaying a faster narrative integration response than much more posterior areas. We hypothesize that these discontinuities can be due to white matter connectivity that would create shortcuts from fast sensory areas to distant frontal areas. To test this hypothesis, we analysed the white matter connectivity of these areas and discovered clear connectivity patterns in accord with our hypotheses. Based on these observations we performed simulations using reservoir networks with connectivity patterns structured with an exponential distance rule, yielding the sensory-associative hierarchy. We then introduce connectivity short-cuts corresponding to those observed in human anatomy, resulting in frontal areas with unusually fast narrative processing. Using structured reservoir computing we confirmed the hypothesis that topographic position in a cortical hierarchy can be dominated by long distance connections that can bring frontal areas closer to the sensory periphery.

neuroscience↗

bioRxiv2021

Narrative Event Segmentation in the Cortical Reservoir

During continuous perception of movies or stories, awake humans display cortical activity patterns that reveal hierarchical segmentation of event structure. Sensory areas like auditory cortex display high frequency segmentation related to the stimulus, while semantic areas like posterior middle cortex display a lower frequency segmentation related to transitions between events (Baldassano et al. 2017). These hierarchical levels of segmentation are associated with different time constants for processing. Chien and Honey (2020) observed that when two groups of participants heard the same sentence in a narrative, preceded by different contexts, neural responses for the groups were initially different and then gradually aligned. The time constant for alignment followed the segmentation hierarchy: sensory cortices aligned most quickly, followed by mid-level regions, while some higher-order cortical regions took more than 10 seconds to align. These hierarchical segmentation phenomena can be considered in the context of processing related to comprehension. Uchida et al. (2021) recently described a model of discourse comprehension where word meanings are modeled by a language model pre-trained on a billion word corpus (Yamada et al 2020). During discourse comprehension, word meanings are continuously integrated in a recurrent cortical network. The model demonstrates novel discourse and inference processing, in part because of two fundamental characteristics: real-world event semantics are represented in the word embeddings, and these are integrated in a reservoir network which has an inherent gradient of functional time constants due to the recurrent connections. Here we demonstrate how this model displays hierarchical narrative event segmentation properties. The reservoir produces activation patterns that are segmented by the HMM of Baldassano et al (2017) in a manner that is comparable to that of humans. Context construction displays a continuum of time constants across reservoir neuron subset, while context forgetting has a fixed time constant across these subsets. Virtual areas formed by subgroups of reservoir neurons with faster time constants segmented with shorter events, while those with longer time constants preferred longer events. This neurocomputational recurrent neural network simulates narrative event processing as revealed by the fMRI event segmentation algorithm of Baldassano et al (2017), and provides a novel explanation of the asymmetry in narrative forgetting and construction observed by Chien and Honey (2020). The model extends the characterization of online integration processes in discourse to more extended narrative, and demonstrates how reservoir computing provides a useful model of cortical processing of narrative structure.

neuroscience↗

bioRxiv2019

Beyond the word and image: III. Neurodynamic properties of the semantic network

Understanding the neural process underlying the comprehension of visual images and sentences remains a major open challenge in cognitive neuroscience. We previously demonstrated with fMRI and DTI that comprehension of visual images and sentences describing human activities recruits a common semantic system. The current research tests the hypothesis that this common semantic system will display similar neural dynamics during processing in these two modalities. To investigate these neural dynamics we recorded EEG from naive subjects as they saw simple narratives made up of a first visual image depicting a human event, followed by a second that was either a sequentially coherent narrative follow-up, or not, of the first image. In separate blocks of trials the same protocol was presented using sentences. Analysis of the EEG signal revealed common neural dynamics for semantic processing across image and sentence modalities. Late positive ERPs were observed in response to sequential incoherence for sentences and images, consistent with previous studies that examined coherence in these two modalities separately. Analysis of oscillatory power revealed increased gamma-band activity for sequential coherence, again consistent with previous studies showing gamma increases for coherence and matching in sentence and image processing. Multivariate analysis demonstrated that training a classifier on data from one modality (images or sentences) allowed reliable decoding of the sequential coherence of data from trials in the untrained modality, providing further support for a common underlying semantic system for images and sentences. Processing sequential coherence of successive stimuli is associated with neural dynamics that are common to sentence and visual image modalities and that can be decoded across modalities. These results are discussed in the context of EEG signatures of narrative processing and meaning, and more general neural mechanisms for structure processing.

neuroscience↗

bioRxiv2018

Prefrontal cortex creates novel navigation sequences from hippocampal place-cell replay with spatial reward propagation

As rats learn to search for multiple sources of food or water in a complex environment, they generate increasingly efficient trajectories between reward sites, across multiple trials. This optimization capacity has been characterized in the Traveling Salesrat Problem (TSP) (de Jong et al (2011). Such spatial navigation capacity involves the replay of hippocampal place-cells during awake states, generating small sequences of spatially related place-cell activity that we call \"snippets\". These snippets occur primarily during sharp-wave-ripple (SWR) events. Here we focus on the role of replay during the awake state, as the animal is learning across multiple trials. We hypothesize that snippet replay generates synthetic data that can substantially expand and restructure the experience available to make PFC learning more optimal. We developed a model of snippet generation that is modulated by reward, propagated in the forward and reverse directions. This implements a form of spatial credit assignment for reinforcement learning. We use a biologically motivated computational framework known as reservoir computing to model PFC in sequence learning, in which large pools of prewired neural elements process information dynamically through reverberations. This PFC model is ideal to consolidate snippets into larger spatial sequences that may be later recalled by subsets of the original sequences. Our simulation experiments provide neurophysiological explanations for two pertinent observations related to navigation. Reward modulation allows the system to reject non-optimal segments of experienced trajectories, and reverse replay allows the system to \"learn\" trajectories that is has not physically experienced, both of which significantly contribute to the TSP behavior.\n\nAuthor SummaryAs rats search for multiple sources of food in a complex environment, they generate increasingly efficient trajectories between reward sites, across multiple trials, characterized in the Traveling Salesrat Problem (TSP). This likely involves the coordinated replay of place-cell \"snippets\" between successive trials. We hypothesize that \"snippets\" can be used by the prefrontal cortex (PFC) to implement a form of reward-modulated reinforcement learning. Our simulation experiments provide neurophysiological explanations for two pertinent observations related to navigation. Reward modulation allows the system to reject non-optimal segments of experienced trajectories, and reverse replay allows the system to \"learn\" trajectories that it has not physically experienced, both of which significantly contribute to the TSP behavior.

neuroscience↗