A5049098198- Neural dynamics and brain function
- Advanced Memory and Neural Computing
- Photoreceptor and optogenetics research
- Neural Networks and Reservoir Computing
- Plant and Biological Electrophysiology Studies
- Neuroscience and Neuropharmacology Research
- Satellite Image Processing and Photogrammetry
- Stress Responses and Cortisol
- Birth, Development, and Health
- Neuroendocrine regulation and behavior
- Receptor Mechanisms and Signaling
- Neuroscience and Music Perception
University of California, Los Angeles
2021-2024
ETH Zurich
2019
SIB Swiss Institute of Bioinformatics
2019
University of Zurich
2019
École Polytechnique Fédérale de Lausanne
2014
Unlike synaptic strength, intrinsic excitability is assumed to be a stable property of neurons. For example, learning somatic conductances generally not incorporated into computational models, and the discharge pattern neurons in response test stimuli frequently used as basis for phenotypic classification. However, it increasingly evident that signal processing properties are more plastic on timescale minutes. Here we demonstrate firing patterns CA3 rat hippocampus vitro undergo rapid...
Self-sustained neural activity maintained through local recurrent connections is of fundamental importance to cortical function. Converging theoretical and experimental evidence indicates that circuits generating self-sustained dynamics operate in an inhibition-stabilized regime. Theoretical work has established four sets weights ( W E←E , E←I I←E I←I ) must obey specific relationships produce dynamics, but it not known how the brain can appropriately set values all weight classes...
ABSTRACT Self-sustaining neural activity maintained through local recurrent connections is of fundamental importance to cortical function. We show that Up-states—an example self-sustained, inhibition-stabilized network dynamics—emerge in circuits across three weeks ex vivo development, establishing the presence unsupervised learning rules capable generating self-sustained dynamics. Previous computational models have established four sets weights ( W E ← , I ) must interact an orchestrated...
Abstract Many neural computations emerge from self-sustained patterns of activity in recurrent circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits that use the physics silicon to emulate neuronal dynamics represent a promising approach for implementing brain’s computational primitives, including activity. However, achieving same robustness biological networks neuromorphic computing systems remains challenge, due high degree heterogeneity variability...
Abstract Many neural computations emerge from self-sustained patterns of activity in recurrent circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits that use the physics silicon to emulate neuronal dynamics represent a promising approach for implementing brain's computational primitives, including activity. However, achieving same robustness biological networks neuromorphic computing systems remains challenge, due high degree heterogeneity variability...