Biology subjects

Oweiss, K.

Publications and source records attributed to Oweiss, K..

4 recordsLinked to original sources

bioRxiv2025

Meta plasticity and Continual Learning: Mechanisms sub serving Brain Computer Interface Proficiency

ObjectiveBrain Computer Interfaces (BCIs) require substantial cognitive flexibility to optimize control performance across diverse settings. Remarkably, learning this control is rapid, suggesting it might be mediated by neuroplasticity mechanisms operating on very short time scales. However, these mechanisms remain far from understood. Here, we propose a meta plasticity model of BCI learning and skill consolidation at the single cell and population levels comprised of three elements: a) behavioral time scale synaptic plasticity (BTSP), b) intrinsic plasticity (IP) and c) synaptic scaling (SS) operating at time scales from seconds to minutes to hours and days. Notably, the model is able to explain representational drift - a frequent and widespread phenomenon observed in multiple brain areas that adversely affects BCI control and continued use. ApproachWe developed a closed loop, all optical approach to characterize IP, BTSP and SS with single cell resolution in cortical L2/3 of awake mice using fluorescent two photon (2P) GCaMP7s imaging and optogenetic stimulation of the soma targeted ChRmineKv2.1. We further trained mice on a one-dimensional (1D) BCI control task and systematically characterized within session (seconds to minutes) learning as well as across sessions (days and weeks) with different neural ensembles. Main resultsWe found that on the time scale of seconds, substantial BTSP could be induced and was associated with significant IP over minutes. Over the time scale of days and weeks, these changes could predict BCI control proficiency, suggesting that BTSP and IP might be complemented by SS to stabilize and consolidate BCI control. SignificanceOur results provide theoretical and early experimental support for an integrated meta plasticity model of continual BCI learning and skill consolidation. The model predictions may be used to design and calibrate neural decoders with complete autonomy while considering the temporal and spatial scales of plasticity mechanisms and their anticipated order of occurrence. With the power of modern-day machine learning (ML) and artificial Intelligence (AI), fully autonomous neural decoding and adaptation in BCIs might be achieved with minimal to no human intervention.

neuroscience↗

bioRxiv2024

Chemogenetic stimulation of phrenic motor output and diaphragm activity

Impaired respiratory motor output contributes to morbidity and mortality in many neurodegenerative diseases and neurologic injuries. We investigated if expressing designer receptors exclusively activated by designer drugs (DREADDs) in the mid-cervical spinal cord could effectively stimulate phrenic motor output to increase diaphragm activation. Two primary questions were addressed: 1) does effective DREADD-mediated diaphragm activation require focal expression in phrenic motoneurons (vs. non-specific mid-cervical expression), and 2) can this method produce a sustained increase in inspiratory tidal volume? Wild type (C57/bl6) and ChAT-Cre mice received bilateral intraspinal (C4) injections of an adeno-associated virus (AAV) encoding the hM3D(Gq) excitatory DREADD. In wild-type mice, this produced non-specific DREADD expression throughout the mid-cervical ventral horn. In ChAT-Cre mice, a Cre-dependent viral construct was used to drive neuronal DREADD expression in the C4 ventral horn, targeting phrenic motoneurons. Diaphragm EMG was recorded in isoflurane-anesthetized spontaneously breathing mice at 4-9 weeks post-AAV delivery. The DREADD ligand JHU37160 (J60) caused a bilateral, sustained (>1 hour) increase in inspiratory EMG bursting in both groups; the relative increase was greater in ChAT-Cre mice. Additional experiments in ChAT-Cre rats were conducted to determine if spinal DREADD activation could increase inspiratory tidal volume (VT) during spontaneous breathing, assessed using whole-body plethysmography without anesthesia. Three-to-four months after intraspinal (C4) injection of AAV driving Cre-dependent hM3D(Gq) expression, intravenous J60 resulted in a sustained (>30 min) increase in VT. Subsequently, phrenic nerve recordings performed under urethane anesthesia confirmed that J60 evoked a > 200% increase in inspiratory output. We conclude that targeting mid-cervical spinal DREADD expression to the phrenic motoneuron pool enables ligand-induced, sustained increases in phrenic motor output and VT. Further development of this technology may enable application to clinical conditions associated with impaired diaphragm activation and hypoventilation.

neuroscience↗

bioRxiv2023

High-throughput in vivo synaptic connectivity mapping of neuronal micro-circuits using two-photon holographic optogenetics and compressive sensing

Understanding the intricate synaptic connectivity in living neural circuits is crucial for unraveling the relationship between network structure and function, as well as its evolution during development, learning, and recovery from injury. However, current methodologies for identifying connected neurons in vivo suffer from limitations, particularly with regards to their throughput. In this study, we introduce a groundbreaking framework for in vivo connectivity mapping that combines two-photon holographic optogenetics for activating single or multiple potential presynaptic neurons, whole-cell recording of postsynaptic responses, and a compressive sensing strategy for efficiently retrieving individual postsynaptic neurons responses when multiple potential presynaptic neurons are simultaneously activated. The approach was validated in the layer 2/3 of the visual cortex in anesthetized mice, enabling rapid probing of up to 100 cells in approximately 5 minutes. By identifying tens of synaptic pairs, including their connection strength, kinetics, and spatial distribution, this method showcases its potential to significantly advance circuit reconstruction in large neuronal networks with minimal invasiveness. Moreover, through simultaneous multi-cell stimulation and compressive sensing, we demonstrate up to a three-fold reduction in the number of required measurements to infer connectivity with limited loss in accuracy, thereby enabling high-throughput connectivity mapping in vivo. These results pave the way for a more efficient and rapid investigation of neuronal circuits, leading to deeper insights into brain function and plasticity.

neuroscience↗

bioRxiv2022

Compressive sensing of neuronal connectivity maps from subsampled, cell-targeted optogenetic stimulation of a network model

Mapping functional connectivity between neurons is an essential step towards probing the neural computations mediating behavior. The ability to consistently and robustly determine synaptic connectivity maps in large populations of interconnected neurons is a significant challenge in terms of yield, accuracy and experimental time. Here we developed a compressive sensing approach to reconstruct synaptic connectivity maps based on random two photon (2p) cell-targeted optogenetic stimulation and membrane voltage readout of many putative postsynaptic neurons. Using a biophysical network model of interconnected populations of excitatory and inhibitory neurons, we found that the mapping can be achieved with far fewer measurements than the standard pairwise sequential approach. We characterized the recall and precision probabilities as a function of network observability, sparsity, number of neurons stimulated per trial, off-target stimulation, synaptic reliability, propagation latency and network topology. We found that that network sparsity and synaptic reliability were primary determinants of the performance. In particular, in a network with 10% probability of neuronal connectivity, functional connections were recovered with >85% recall and >80% precision in half the trials that would be required for single cell stimulation. Our results suggest a rapid and efficient method to reconstruct functional connectivity of brain networks where sparsity is predominantly present.

neuroscience↗