A5057741640- Neural Networks and Applications
- Neural dynamics and brain function
- Advanced Memory and Neural Computing
- Evolutionary Algorithms and Applications
- Circadian rhythm and melatonin
- Fractal and DNA sequence analysis
- Retinal Development and Disorders
- Graphene research and applications
- Nanopore and Nanochannel Transport Studies
- Receptor Mechanisms and Signaling
- Sleep and Wakefulness Research
- Quantum and electron transport phenomena
Weizmann Institute of Science
2019-2024
The mammalian retina is considered an autonomous circuit, yet work dating back to Ramon y Cajal indicates that it receives inputs from the brain. How such affect retinal processing has remained unknown. We confirmed brain-to-retina projections of histaminergic neurons mouse hypothalamus. Histamine application ex vivo altered activity various ganglion cells (RGCs), including direction-selective RGCs gained responses high motion velocities. These results were reproduced in with optic tract...
Abstract Electron transport in two-dimensional conducting materials such as graphene, with dominant electron–electron interaction, exhibits unusual vortex flow that leads to a nonlocal current-field relation (negative resistance), distinct from the classical Ohm’s law. The behavior of these is best described by low Reynolds number hydrodynamics, where constitutive pressure–speed Stoke’s Here we report evidence vortices observed viscous Newtonian fluid microfluidic device consisting...
Studying and understanding the code of large neural populations hinge on accurate statistical models population activity. A novel class models, based learning to weigh sparse non-linear Random Projections (RP) population, has demonstrated high accuracy, efficiency, scalability. Importantly, these RP have a clear biologically-plausible implementation as shallow networks. We present new that are learned by optimizing randomly selected projections themselves. This “reshaping” is akin changing...
Summary The mammalian retina is considered an autonomous circuit, yet work dating back to Ramon y Cajal indicates that it receives inputs from the brain. How such affect retinal processing has remained unknown. We identified brain-to-retina projections of histaminergic neurons mouse hypothalamus, which densely innervated dorsal retina. Histamine application, or chemogenetic activation axons, altered spontaneous and light-evoked activity various ganglion cells (RGCs), including...
Studying and understanding the code of large neural populations hinge on accurate statistical models population activity. A novel class models, based learning to weigh sparse nonlinear Random Projections (RP) population, has demonstrated high accuracy, efficiency, scalability. Importantly, these RP have a clear biologically plausible implementation as shallow networks. We present new that are learned by optimizing randomly selected projections themselves. This ‘reshaping’ is akin changing...
Studying and understanding the code of large neural populations hinge on accurate statistical models population activity. A novel class models, based learning to weigh sparse nonlinear Random Projections (RP) population, has demonstrated high accuracy, efficiency, scalability. Importantly, these RP have a clear biologically-plausible implementation as shallow networks. We present new that are learned by optimizing randomly selected projections themselves. This “reshaping” is akin changing...
Studying and understanding the code of large neural populations hinge on accurate statistical models population activity. A novel class models, based learning to weigh sparse nonlinear Random Projections (RP) population, has demonstrated high accuracy, efficiency, scalability. Importantly, these RP have a clear biologically plausible implementation as shallow networks. We present new that are learned by optimizing randomly selected projections themselves. This ‘reshaping’ is akin changing...
Studying and understanding the code of large neural populations hinge on accurate statistical models population activity. A novel class models, based learning to weigh sparse nonlinear Random Projections (RP) population, has demonstrated high accuracy, efficiency, scalability. Importantly, these RP have a clear biologically-plausible implementation as shallow networks. We present new that are learned by optimizing randomly selected projections themselves. This “reshaping” is akin changing...