Ph.D. Physics, Bar Ilan University, Israel, 1993

Postdoc Fellow, Dept. of Physiology & Biophysics, University of Connecticut and Case Western Reserve University, 1994-97.

Instructor to Staff, Cleveland Clinic Foundation and University Hospitals of Cleveland, Case Western Reserve University, 1998-2006.

Assistant Professor, Department of Physics, University of Arkansas, 2006 - present

Selected awards and honors:
Rothschild Postdoctoral Fellow.
Merck Young Investigator.
Scholar, American Society of Photobiology.

THE BIOPHYSICS OF NEURAL COMPUTATION

The neuronal cells in our brain are connected via synapses to form neural networks. How particular tasks, or "computations", are implemented by neural networks to generate behavior, and how patterns of activity are stored during learning are two questions that are still un-answered. Today we know that the neuronal dendrites which integrate their synaptic inputs to define the input-output relation of the neuron, are capable of a processing vast amounts of spatio-temporal information rapidly using a variety of linear and non-linear algorithms. Additionally, signaling mechanisms recently discovered in dendrites provided new means by which patterns of network activity could be stored and transmitted. These recent advances have refocused attention on how single neurons contribute to information processing and storage in the brain. In our lab we are utilizing new experimental and theoretical techniques to link single-cell processing with higher levels of neuronal network function as follows:

On the theoretical level we are interested in understanding cooperative dynamics of neural networks and their emergent properties such as phase-transitions, chaos, oscillations and synchrony. In addition, we are working on developing novel supervised and unsupervised learning algorithm by defining the basic laws that govern synaptic electrical signal processing and the efficacy of information transfer from the synapse to the axon through the cell body (soma). Computer simulations are used extensively in these studies to model single neurons as well as networks of interconnected neurons, using statistical mechanics of equilibrium and non-equilibrium systems, mean-field theories, theory of nonlinear dynamics and stochastic processes.

On the experimental level we focus on the role of nano-scale variations in intra-membrane dipolic fields (lipid rafts) in modulation of voltage-gated channels and their role in processing and transmission of synaptic electrical input in cortical layer 5 pyramidal cells. We use patch-clamp and intracellular recordings from neuronal dendrites and spikes, imaging individual ionic channels and lipid rafts in dendrites with near-filed scanning optical microscopy (NSOM), as well as recording from multiple synaptically connected cells. These techniques are applied in parallel to in-vitro and in-vivo preparations in order to investigate the details of cellular mechanisms while placing them in the context of network activity. By measuring dendritic signals that are triggered by sensory processing, our ultimate goal in this project is to link cellular mechanisms to behavior.