About

Nicholas G. “Nicho” Hatsopoulos, Ph.D., is a Professor at the University of Chicago and has served as the Chairman of the Computational Neuroscience graduate program from 2008 to 2015. He leads a laboratory that includes graduate students, postdoctoral fellows, and technicians, with funding partly provided by the National Institutes of Health. His research focuses on the neural basis of motor control and learning, investigating how features of motor behavior are encoded and represented in the collective activity of neuronal ensembles in the motor cortex. Dr. Hatsopoulos studies how these neural representations change during motor learning by recording electrical discharges from many motor cortical neurons using multi-electrode arrays and correlating these signals with motor behavior. His work explores the encoding properties of individual neurons, their relation to higher-order group representations, and the potential changes in functional connectivity among neurons during motor learning. He completed his B.A. in Physics from Williams College in 1984, his M.S. in Psychology in 1991, and his Ph.D. in Cognitive Science from Brown University in 1992. He also co-founded Cyberkinetics Neurotechnology Systems in 2001, a company that developed neural prosthesis technology to assist individuals with severe motor disabilities. Prior to his current position, Dr. Hatsopoulos was an Assistant Professor of Research in the Department of Neuroscience at Brown University from 1998 to 2001, and he completed postdoctoral fellowships at Brown University and the California Institute of Technology.

Research topics

• Computer Science
• Psychology
• Neuroscience
• Physics
• Biology
• Artificial Intelligence
• Telecommunications
• Simulation
• Medicine

Selected publications

• Propagating Motor Cortical Dynamics Facilitate Movement Initiation

  Neuron · 2025-07-15

  erratumOpen accessSenior author
  PublisherOA PDFDOI

• Spatially Extensive LFP Correlations Identify Slow-Wave Sleep in Marmoset Sensorimotor Cortex

  eNeuro · 2025-11-01

  articleOpen accessSenior author

  Identifying neural signatures of slow-wave sleep (SWS) is important for a number of reasons including diagnosing potential sleep disorders and examining its role in memory consolidation ( Diekelmann and Born, 2010; Klinzing et al., 2019; Brodt et al., 2023). Studies of sleep in the common marmoset ( Callithrix jacchus ) have revealed similarities to humans and other nonhuman primates, including distinct sleep stages ( Crofts et al., 2001) and diurnal sleep patterns ( Hoffmann et al., 2012). Advances in applying wireless technology for recording neural activity during natural, unrestrained behaviors ( Walker et al., 2021) position the marmoset as an excellent model for studying sleep-related neural activity associated with learning. Here, we identify putative SWS epochs based on the spatially correlated activity of local field potentials (LFPs) recorded from a multielectrode planar array implanted in the sensorimotor cortex of two marmosets (one female and one male). The average correlation of the LFP signal measured between electrodes decreased gradually with the distance between pairs. We modeled this spatial structure as an exponential decay function, where the spatial decay constant varied significantly over time, reaching its lowest values during epochs where LFP power dynamics were consistent with SWS. These periods of widespread high correlations across the sensorimotor cortex closely matched SWS identification commonly used in rodent models based on the changes in power in the gamma (30–60 Hz) and delta/slow oscillation (0.1–4 Hz) frequency bands. These findings demonstrate that putative SWS epochs can be reliably identified using spatially correlated LFP activity across the sensorimotor cortex.

  PublisherOA PDFDOI

• How different immersive environments affect intracortical brain computer interfaces

  Journal of Neural Engineering · 2025-01-30

  articleOpen access

  Abstract Objective . As brain–computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different virtual environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device. Approach . Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments. Two participants who had intracortical electrodes implanted in their precentral gyrus used a BCI to control a virtual arm, both viewed immersively through virtual reality goggles and at a distance on a flat television monitor. Main results . Each participant performed better with a decoder trained and tested in the environment they had used the most prior to the study, one for each environment type. The neural tuning to the desired movement was minimally influenced by the immersiveness of the environment. Finally, in further testing with one of the participants, we found that decoders trained in one environment generalized well to the other environment, but the order in which the environments were experienced within a session mattered. Significance . Overall, experience with an environment was more influential on performance than the immersiveness of the environment, but BCI performance generalized well after accounting for experience. Clinical Trial: NCT01894802

  PublisherDOI

• Author response: Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex

  2023-01-24

  peer-reviewOpen access

  A recurrent neural network model with parameters constrained by data explains mechanisms for how tuning properties of motor cortical neurons change during movement preparation and execution in a monkey performing a reaching task, and accurately reproduces neural dynamics from recordings.

  PublisherDOI

• Multiple regions of sensorimotor cortex encode bite force and gape

  Frontiers in Systems Neuroscience · 2023-09-22 · 9 citations

  articleOpen accessSenior author

  The precise control of bite force and gape is vital for safe and effective breakdown and manipulation of food inside the oral cavity during feeding. Yet, the role of the orofacial sensorimotor cortex (OSMcx) in the control of bite force and gape is still largely unknown. The aim of this study was to elucidate how individual neurons and populations of neurons in multiple regions of OSMcx differentially encode bite force and static gape when subjects (Macaca mulatta) generated different levels of bite force at varying gapes. We examined neuronal activity recorded simultaneously from three microelectrode arrays implanted chronically in the primary motor (MIo), primary somatosensory (SIo), and cortical masticatory (CMA) areas of OSMcx. We used generalized linear models to evaluate encoding properties of individual neurons and utilized dimensionality reduction techniques to decompose population activity into components related to specific task parameters. Individual neurons encoded bite force more strongly than gape in all three OSMCx areas although bite force was a better predictor of spiking activity in MIo vs. SIo. Population activity differentiated between levels of bite force and gape while preserving task-independent temporal modulation across the behavioral trial. While activation patterns of neuronal populations were comparable across OSMCx areas, the total variance explained by task parameters was context-dependent and differed across areas. These findings suggest that the cortical control of static gape during biting may rely on computations at the population level whereas the strong encoding of bite force at the individual neuron level allows for the precise and rapid control of bite force.

  PublisherOA PDFDOI

• Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex

  eLife · 2023-03-14 · 24 citations

  articleOpen access

  The primary motor cortex has been shown to coordinate movement preparation and execution through computations in approximately orthogonal subspaces. The underlying network mechanisms, and the roles played by external and recurrent connectivity, are central open questions that need to be answered to understand the neural substrates of motor control. We develop a recurrent neural network model that recapitulates the temporal evolution of neuronal activity recorded from the primary motor cortex of a macaque monkey during an instructed delayed-reach task. In particular, it reproduces the observed dynamic patterns of covariation between neural activity and the direction of motion. We explore the hypothesis that the observed dynamics emerges from a synaptic connectivity structure that depends on the preferred directions of neurons in both preparatory and movement-related epochs, and we constrain the strength of both synaptic connectivity and external input parameters from data. While the model can reproduce neural activity for multiple combinations of the feedforward and recurrent connections, the solution that requires minimum external inputs is one where the observed patterns of covariance are shaped by external inputs during movement preparation, while they are dominated by strong direction-specific recurrent connectivity during movement execution. Our model also demonstrates that the way in which single-neuron tuning properties change over time can explain the level of orthogonality of preparatory and movement-related subspaces.

  PublisherDOI

• Propagating motor cortical patterns of excitability are ubiquitous across human and non-human primate movement initiation

  iScience · 2023-03-29 · 5 citations

  articleOpen accessSenior authorCorresponding

  A spatiotemporal pattern of excitability propagates across the primary motor cortex prior to the onset of a reaching movement in non-human primates. If this pattern is a necessary component of voluntary movement initiation, it should be present across a variety of motor tasks, end-effectors, and even species. Here, we show that propagating patterns of excitability occur during the initiation of precision grip force and tongue protrusion in non-human primates, and even isometric wrist extension in a human participant. In all tasks, the directions of propagation across the cortical sheet were bimodally distributed across trials with modes oriented roughly opposite to one another. Propagation speed was unimodally distributed with similar mean speeds across tasks and species. Additionally, propagation direction and speed did not vary systematically with any behavioral measures except response times indicating that this propagating pattern is invariant to kinematic or kinetic details and may be a generic movement initiation signal.

  PublisherOA PDFDOI

• Propagating spatiotemporal activity patterns across macaque motor cortex carry kinematic information

  Proceedings of the National Academy of Sciences · 2023-01-18 · 9 citations

  articleOpen accessSenior author

  Propagating spatiotemporal neural patterns are widely evident across sensory, motor, and association cortical areas. However, it remains unclear whether any characteristics of neural propagation carry information about specific behavioral details. Here, we provide the first evidence for a link between the direction of cortical propagation and specific behavioral features of an upcoming movement on a trial-by-trial basis. We recorded local field potentials (LFPs) from multielectrode arrays implanted in the primary motor cortex of two rhesus macaque monkeys while they performed a 2D reach task. Propagating patterns were extracted from the information-rich high-gamma band (200 to 400 Hz) envelopes in the LFP amplitude. We found that the exact direction of propagating patterns varied systematically according to initial movement direction, enabling kinematic predictions. Furthermore, characteristics of these propagation patterns provided additional predictive capability beyond the LFP amplitude themselves, which suggests the value of including mesoscopic spatiotemporal characteristics in refining brain-machine interfaces.

  PublisherOA PDFDOI

• Biomimetic multi-channel microstimulation of somatosensory cortex conveys high resolution force feedback for bionic hands

  bioRxiv (Cold Spring Harbor Laboratory) · 2023 · 35 citations

  • Computer Science
  • Computer Science
  • Neuroscience

  Manual interactions with objects are supported by tactile signals from the hand. This tactile feedback can be restored in brain-controlled bionic hands via intracortical microstimulation (ICMS) of somatosensory cortex (S1). In ICMS-based tactile feedback, contact force can be signaled by modulating the stimulation intensity based on the output of force sensors on the bionic hand, which in turn modulates the perceived magnitude of the sensation. In the present study, we gauged the dynamic range and precision of ICMS-based force feedback in three human participants implanted with arrays of microelectrodes in S1. To this end, we measured the increases in sensation magnitude resulting from increases in ICMS amplitude and participant's ability to distinguish between different intensity levels. We then assessed whether we could improve the fidelity of this feedback by implementing "biomimetic" ICMS-trains, designed to evoke patterns of neuronal activity that more closely mimic those in natural touch, and by delivering ICMS through multiple channels at once. We found that multi-channel biomimetic ICMS gives rise to stronger and more distinguishable sensations than does its single-channel counterpart. Finally, we implemented biomimetic multi-channel feedback in a bionic hand and had the participant perform a compliance discrimination task. We found that biomimetic multi-channel tactile feedback yielded improved discrimination over its single-channel linear counterpart. We conclude that multi-channel biomimetic ICMS conveys finely graded force feedback that more closely approximates the sensitivity conferred by natural touch.

  PublisherOA PDFDOI

• Loss of oral sensation impairs feeding performance and consistency of tongue–jaw coordination

  Journal of Oral Rehabilitation · 2022-05-06 · 21 citations

  articleOpen access

  BACKGROUND: Individuals with impaired oral sensation report difficulty chewing, but little is known about the underlying changes to tongue and jaw kinematics. Methodological challenges impede the measurement of 3D tongue movement and its relationship to the gape cycle. OBJECTIVE: The aim of this study was to quantify the impact of loss of oral somatosensation on feeding performance, 3D tongue kinematics and tongue-jaw coordination. METHODOLOGY: XROMM (X-ray Reconstruction of Moving Morphology) was used to quantify 3D tongue and jaw kinematics during feeding in three rhesus macaques (Macaca mulatta) before and after an oral tactile nerve block. Feeding performance was measured using feeding sequence duration, number of manipulation cycles and swallow frequency. Coordination was measured using event- and correlation-based metrics of jaw pitch, anterior tongue length, width and roll. RESULTS: In the absence of tactile sensation to the tongue and other oral structures, feeding performance decreased, and the fast open phase of the gape cycle became significantly longer, relative to the other phases (p < .05). The tongue made similar shapes in both the control and nerve block conditions, but the pattern of tongue-jaw coordination became significantly more variable after the block (p < .05). CONCLUSION: Disruption of oral somatosensation impacts feeding performance by introducing variability into the typically tight pattern of tongue-jaw coordination.

  PublisherOA PDFDOI

Recent grants

• NIH Grant K01MH001671

  NIH · $323k · 2001

• NIH Grant R01NS045853

  NIH · $4.5M · 2018

• Coding of Action by Motor & Premotor Cortical Ensembles

  NIH · $2.2M · 2019–2025

• Large-scale, neuronal ensemble recordings in motor cortex of the behaving marmoset

  NIH · $3.0M · 2018–2022

• NIH Grant R56NS045853

  NIH · $439k · 2009

Frequent coauthors

• Adam S. Dickey

  Emory University

  51 shared

• Pascal Wallisch

  New York University

  48 shared

• Marc Benayoun

  Wake Forest University

  48 shared

• Tanya I. Baker

  48 shared

• Michael Lusignan

  48 shared

• John P. Donoghue

  Rehabilitation Research and Development Service

  30 shared

• Kazutaka Takahashi

  University of Missouri

  29 shared

• Aaron J. Suminski

  University of Wisconsin–Madison

  22 shared

Labs

• Hatsopoulos LabPI