Stealey, H. M. | bioRxiv Science

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↗