Biology subjects

Contreras-Hernandez, E.

Publications and source records attributed to Contreras-Hernandez, E..

5 recordsLinked to original sources

bioRxiv2025

Distinct roles of neuronal phenotypes during neurofeedback adaptation

Learning adaptation allows the brain to refine motor patterns in response to changing environments rapidly. While population-level neural dynamics and single-neuron activity in motor learning have been widely studied, the contributions of individual neuron types remain poorly understood. Here, we employed a brain-machine interface (BMI) task with perturbations of varying difficulty to investigate single-neuron dynamics underlying neurofeedback adaptation in two rhesus macaques. Cortical neurons were classified based on waveform shape into narrow waveform (NW) and broad waveform (BW) categories, representing putative inhibitory interneurons and excitatory pyramidal neurons, respectively. Compared to BW neurons, NW neurons were more active and more strongly involved in the learning process. Moreover, task difficulty modulated neural responsiveness and coordination within both neuron groups, highlighting differential neuron engagement during motor learning. Our findings provide novel insights into single-neuron mechanisms underlying neurofeedback adaptation and emphasize the distinct functional roles of neuronal phenotypes in rapid learning processes. Author SummaryUnderstanding how the brain adapts to changes is crucial for improving treatments for neurological disorders and enhancing brain-machine interface (BMI) technology, which allows control of external devices using neural signals. In our study, we investigated how different types of brain cells contribute to the adaptation process when individuals encounter unexpected challenges during neurofeedback tasks. We discovered that one type of neuron was notably more active and played a more engaged role in rapidly adjusting neural activity compared to another type. These neurons demonstrated stronger coordination in their activity and showed greater responsiveness as the task difficulty increased. Our findings highlight the distinct roles of specific neurons in quickly adapting to neurofeedback tasks, offering insights that could enhance therapies for movement disorders and improve the precision and reliability of brain-controlled prosthetics.

neuroscience↗

bioRxiv2025

Population-level constraints on single neuron tuning changes during behavioral adaptation

In neurofeedback training, select neurons - of the billions that comprise the brain - are able to adapt their activity to produce a desired behavior, even though the feedback signal is low-dimensional. However, the extent that individual neurons are able to alter their activity within the context of the neurofeedback population is unclear. Using a brain-computer interface in monkeys (n=2; Macaca mulatta), we tested how the tuning of individual neurons changed in response to various feedback perturbations. Overall, we found that neurons that co-varied their activity the most with the population and those that contributed most to the behavior prior to the perturbation were most resistant to change- regardless of perturbation type. The degree to which individual neurons counteracted the applied perturbation explained daily differences in how well neurons collectively produced adapted behavior. Our work provides important insight into how population-level constraints impact the ability of individual neurons to adapt and support behavioral flexibility.

bioengineering↗

bioRxiv2025

Long-term neuron tracking reveals balance of stability and plasticity in functional properties

Neural stability is essential for executing learned motor behaviors while plasticity provides the flexibility needed to adapt to new tasks and environments. Although low-dimensional neural population dynamics exhibit long-term stability, the extent to which individual neurons retain their functional properties over time and balance the need for both stability and plasticity remains an open question. Tracking individual neurons across multiple recording sessions is crucial to addressing this question, yet conventional methods face challenges such as electrode drift, waveform variability, and large inter-electrode distances that limit the number of channels a neuron is observed on. Here, we introduce a waveform-based neuron tracking method optimized for standard microelectrode arrays, enabling the identification of the same neurons across sessions without relying on spatial overlap, a strategy commonly leveraged with high-density electrode arrays. We apply this method to assess the longitudinal stability of multiple neural properties, including firing rates, inter-spike intervals, tuning properties, and spike-field interactions. Our findings reveal that while spike waveform properties remain stable, certain functional properties such as ISI and tuning can exhibit gradual shifts, suggesting a balance between neural stability and plasticity. Understanding the persistence of individual neural signals provides insight into learning and adaptation while advancing the study of neural stability and plasticity over extended timescales. Beyond basic neuroscience, this framework has potential to enhance the long-term reliability of brain-machine interfaces and closed-loop deep brain stimulation systems that rely on chronic neural sensing.

neuroscience↗

bioRxiv2024

Neural population variance explains adaptation differences during learning

Variability, a ubiquitous feature of neural activity, plays an integral role in behavior. However, establishing a causal relationship between neural signals and behavior is difficult. By defining a mathematical mapping between neural spiking activity and behavior, we investigate the role of spiking variability in adaptation during a brain-computer interface (BCI) behavior in male rhesus macaques (Macaca mulatta, n=2). Recent BCI evidence demonstrates that creating novel neural patterns is harder than repurposing existing patterns to respond to changes in external input. However, what limits the ability to repurpose, or adapt, patterns under different magnitudes of change is less well-characterized. Here, we present evidence that variance in neural spiking activity reveals differences in learnability between easy and hard adaptation conditions and across sessions. Furthermore, our study illuminates the limitations in neural changes underlying behavior within a neurofeedback paradigm. Significance StatementVariability in neural activity is a major driver of behavioral variability, though it is unclear how variability is balanced with stable neural activity as new behaviors become more practiced. By using a brain-computer interface methodology, we define a mathematical mapping between neural spiking activity and a behavioral control signal. Through thoughtful manipulation of this mapping, we incite the subjects (rhesus macaques) to learn and adapt neural activity to regain behavioral proficiency. We find that metrics of neural population variability are differentially modulated depending on difficulty of the imposed manipulation. Our exciting results provide important implications for brain-computer interface applications as well as our understanding of learning and adaptation more broadly. Our work represents an important step forwards towards understanding population neural dynamics in this critical component of behavior.

bioengineering↗

bioRxiv2023

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes. Author SummaryMulti-modal measurements have become an emerging trend in recent years due to the capability of studying brain dynamics at disparate scales. However, an integrative framework to systematically capture the multi-scale nonlinear dynamics in brain networks is lacking. A major challenge for creating a cohesive model is a mismatch in the timescale and subsequent sampling rate of the dynamics for disparate modalities. In this work, we introduce a deep learning-based approach to characterize brain communications between regions and scales. By modeling the continuous dynamics of hidden states using the neural network-based ordinary differential equations, the requirement of downsampling the faster sampling signals is discarded, thus preventing from losing dynamics information. Another advantageous feature of the proposed method is flexibility. An adaptable framework to bridge the gap between scales is necessary. Depending on the neural recording modalities utilized in the experiment, any suitable pair of well-established models can be plugged into the proposed multi-scale modeling framework. Thus, this method can provide insight into the brain computations of multi-scale brain activity.

neuroscience↗