Biology subjects

Kao, J. C.

Publications and source records attributed to Kao, J. C..

12 recordsLinked to original sources

bioRxiv2025

Complementary cortical and striatal encoding of locomotor preparation and performance

Cortical and striatal circuits play an important role in motor planning and execution, and encode movement-related information. While neural dynamics in these areas show substantial similarities, possibly reflecting shared information content, studies directly comparing the cortical and striatal encoding of locomotor preparation and performance have been lacking. Here we contrasted the neural coding properties of mouse primary motor and medial prefrontal cortex, as well as dorsolateral and dorsomedial striatum, prior to and during bouts of self-initiated walking. All four areas contained cells active during both the preparatory and performance periods of locomotion. However, the decoding of behaviorally relevant information using population-level activity revealed significant regional variations. Specifically, dorsomedial striatum more accurately encoded the preparatory period prior to walking, while primary motor cortex more accurately encoded rhythmic limb kinematics during walking. Together, this work provides evidence for a complementary neural coding scheme for locomotor preparation and performance in cortical and striatal circuits.

neuroscience↗

bioRxiv2025

Illusions of Alignment Between Large Language Models and Brains Emerge From Fragile Methods and Overlooked Confounds

Emerging research seeks to draw neuroscientific insights from the neural predictivity of large language models (LLMs). However, as results continue to be generated at a rapid pace, there is a growing need for large-scale assessments of their robustness. Here, we analyze a wide range of models, methodological approaches, and neural datasets. We find that some methodological approaches, particularly the use of shuffled train-test splits, have led to many impactful yet unreliable findings, and that the method by which activations are extracted from LLMs can bias results to favor particular model classes. Moreover, we find that confounding variables, particularly positional signals and word rate, perform competitively with trained LLMs and fully account for the neural predictivity of untrained LLMs. In summary, our results suggest that theoretically interesting connections between LLMs and brains on three neural datasets are driven largely by fragile methodologies and overlooked confounds.

neuroscience↗

bioRxiv2024

A reinforcement learning based software simulator for motor brain-computer interfaces

Intracortical motor brain-computer interfaces (BCIs) are expensive and time-consuming to design because accurate evaluation traditionally requires real-time experiments. In a BCI system, a user interacts with an imperfect decoder and continuously changes motor commands in response to unexpected decoded movements. This "closed-loop" nature of BCI leads to emergent interactions between the user and decoder that are challenging to model. The gold standard for BCI evaluation is therefore real-time experiments, which significantly limits the speed and community of BCI research. We present a new BCI simulator that enables researchers to accurately and quickly design BCIs for cursor control entirely in software. Our simulator replaces the BCI user with a deep reinforcement learning (RL) agent that interacts with a simulated BCI system and learns to optimally control it. We demonstrate that our simulator is accurate and versatile, reproducing the published results of three distinct types of BCI decoders: (1) a state-of-the-art linear decoder (FIT-KF), (2) a "two-stage" BCI decoder requiring closed-loop decoder adaptation (ReFIT-KF), and (3) a nonlinear recurrent neural network decoder (FORCE).

bioengineering↗

bioRxiv2024

Non-invasive brain-machine interface control with artificial intelligence copilots

Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the last two decades, BMIs face key obstacles to clinical viability. Invasive BMIs achieve proficient cursor and robotic arm control but require neurosurgery, posing significant risk to patients. Non-invasive BMIs do not have neurosurgical risk, but achieve lower performance, sometimes being prohibitively frustrating to use and preventing widespread adoption. We take a step toward breaking this performance-risk tradeoff by building performant non-invasive BMIs. The critical limitation that bounds decoder performance in non-invasive BMIs is their poor neural signal-to-noise ratio. To overcome this, we contribute (1) a novel EEG decoding approach and (2) artificial intelligence (AI) copilots that infer task goals and aid action completion. We demonstrate that with this "AI-BMI," in tandem with a new adaptive decoding approach using a convolutional neural network (CNN) and ReFIT-like Kalman filter (KF), healthy users and a paralyzed participant can autonomously and proficiently control computer cursors and robotic arms. Using an AI copilot improves goal acquisition speed by up to 4.3x in the standard center-out 8 cursor control task and enables users to control a robotic arm to perform the sequential pick-and-place task, moving 4 randomly placed blocks to 4 randomly chosen locations. As AI copilots improve, this approach may result in clinically viable non-invasive AI-BMIs.

bioengineering↗

bioRxiv2024

Multiregional Representations of Intertemporal Decision Making in Human Single Neurons

Characterization of the neural basis of delay discounting provides insight into the origin of impulsive decision-making, which underlies several psychiatric diseases including substance use disorder. Here, we identify human single unit representations of decisions, and their level of difficulty, in the orbitofrontal cortex, hippocampus and amygdala that are related to preferences for immediate vs. delayed rewards. Results provide an initial account of multiregional intracranial computations related to impulsive behaviors in humans.

neuroscience↗

bioRxiv2024

Spontaneous pain dynamics characterized by stochasticity in awake human LFP with chronic pain

Chronic pain involves persistent fluctuations lasting seconds to minutes, yet there are limited studies on spontaneous pain fluctuations utilizing high-temporal-resolution electrophysiological signals in humans. This study addresses the gap, capturing data during awake deep brain stimulation (DBS) surgery in five chronic pain patients. Patients continuously reported pain levels using the visual analog scale (VAS), and local field potentials (LFP) from key pain-processing structures (ventral parietal medial of the thalamus, VPM; subgenual cingulate cortex, SCC; periaqueductal gray, PVG) were recorded. Our novel AMI analysis revealed that regular spike-like events in the theta/alpha band was associated with higher pain; and regular events in the gamma band was associated with opioid effects. We demonstrate a novel methodology that successfully characterizes spontaneous pain dynamics with human electrophysiological signals, holding potential for advancing closed-loop DBS treatments for chronic pain.

neuroscience↗

bioRxiv2021

Dorsal premammillary projection to periaqueductal gray controls escape vigor from innate and conditioned threats

Escape from threats has paramount importance for survival. However, it is unknown if a single circuit controls escape from innate and conditioned threats. The hypothalamic dorsal premammillary nucleus (PMd) may control escape, as it is activated by escape-inducing threats and projects to the region most implicated in flight, the dorsolateral periaqueductal gray (dlPAG). We show that in mice cholecystokinin (cck)-expressing PMd cells are activated during escape, but not other defensive behaviors. PMd-cck ensemble activity can also predict future escape. Furthermore, PMd inhibition decreases escape speed from both innate and conditioned threats. Inhibition of the PMd-cck projection to the dlPAG also decreased escape speed. Lastly, human fMRI data show that a posterior hypothalamic-to-dlPAG pathway increased activity during exposure to aversive images, indicating that a similar pathway may possibly have a related role in humans. Our data identify the PMd as a central node of the escape network.

neuroscience↗

bioRxiv2020

Dorsal Periaqueductal gray ensembles represent approach and avoidance states

Animals must balance needs to approach threats for risk-assessment and to avoid danger. The dorsal periaqueductal gray (dPAG) controls defensive behaviors, but it is unknown how it represents states associated with threat approach and avoidance. We identified a dPAG threat-avoidance ensemble in mice that showed higher activity far from threats such as the open arms of the elevated plus maze and a live predator. These cells were also more active during threat-avoidance behaviors such as escape and freezing, even though these behaviors have antagonistic motor output. Conversely, the threat-approach ensemble was more active during risk-assessment behaviors and near threats. Furthermore, unsupervised methods showed approach/avoidance states were encoded with shared activity patterns across threats. Lastly, the relative number of cells in each ensemble predicted threat-avoidance across mice. Thus, dPAG ensembles dynamically encode threat approach and avoidance states, providing a flexible mechanism to balance risk-assessment and danger avoidance.

neuroscience↗

bioRxiv2019

Recurrent neural network models of multi-area computation underlying decision-making

Cognition emerges from coordinated computations across multiple brain areas. However, elucidating these computations within and across brain regions is challenging because intra- and inter-area connectivity are typically unknown. To study coordinated computation, we trained multi-area recurrent neural networks (RNNs) to discriminate the dominant color of a checker-board and output decision variables reflecting a direction decision, a task previously used to investigate decision-related dynamics in dorsal premotor cortex (PMd) of monkeys. We found that multi-area RNNs, trained with neurophysiological connectivity constraints and Dales law, recapitulated decision-related dynamics observed in PMd. The RNN solved this task by a dynamical mechanism where the direction decision was computed and outputted, via precisely oriented dynamics, on an axis that was nearly orthogonal to checkerboard color inputs. This orthogonal direction information was preferentially propagated through alignment with inter-area connections; in contrast, color information was filtered. These results suggest that cortex uses modular computation to generate minimal sufficient representations of task information. Finally, we used multi-area RNNs to produce experimentally testable hypotheses for computations that occur within and across multiple brain areas, enabling new insights into distributed computation in neural systems.

neuroscience↗

bioRxiv2019

Decoding and perturbing decision states in real time

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment. The process of deliberation on the evidence can be described by a time-varying decision variable (DV), decoded from neural activity, that predicts a subjects decision at the end of a trial. However, within trials, large moment-to-moment fluctuations of the DV are observed. The behavioral significance of these fluctuations and their role in the decision process remain unclear. Here we show that within-trial DV fluctuations decoded in real time from motor cortex are tightly linked to choice behavior, and that robust changes in DV sign have the statistical regularities expected from behavioral studies of changes-of-mind. Furthermore, we find single-trial evidence for absorbing decision bounds. As the DV builds up, heavily favoring one or the other choice, moment-to-moment variability in the DV is reduced, and both neural DV and behavioral decisions become resistant to additional pulses of sensory evidence as predicted by diffusion-to-bound and attractor models of the decision process.

neuroscience↗

bioRxiv2018

Considerations in using recurrent neural networks to probe neural dynamics

Recurrent neural networks (RNNs) are increasingly being used to model complex cognitive and motor tasks performed by behaving animals. Here, RNNs are trained to reproduce animal behavior while also recapitulating key statistics of empirically recorded neural activity. In this manner, the RNN can be viewed as an in silico circuit whose computational elements share similar motifs with the cortical area it is modeling. Further, as the RNN's governing equations and parameters are fully known, they can be analyzed to propose hypotheses for how neural populations compute. In this context, we present important considerations when using RNNs to model motor behavior in a delayed reach task. First, by varying the network's nonlinear activation and rate regularization, we show that RNNs reproducing single neuron firing rate motifs may not adequately capture important population motifs. Second, by visualizing the RNN's dynamics in low-dimensional projections, we demonstrate that even when RNNs recapitulate key neurophysiological features on both the single neuron and population levels, it can do so through distinctly different dynamical mechanisms. To militate between these mechanisms, we show that an RNN consistent with a previously proposed dynamical mechanism is more robust to noise. Finally, we show that these dynamics are sufficient for the RNN to generalize to a target switch task it was not trained on. Together, these results emphasize important considerations when using RNN models to probe neural dynamics.

neuroscience↗

bioRxiv2017

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

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 populations 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.

neuroscience↗