Lab Members


Ariel Furstenberg
My current research focuses on human behavioral and electrophysiological (EEG) experiments (in collaboration with Prof. Leon Deouell’s lab) and large scale neural-network modeling of ‘intentions’, ‘intention formation’ and complex processes such as ‘change of intention’. These and other notions and processes are closely related to the concepts of will and agency. I’m also interested in the following general question: in what way can progress in the scientific realm of these concepts lead to deeper understanding in the related philosophical discourse, and vice versa?



Ran Rubin
In most neuronal systems, neural activities are in the form of dynamic time series of spikes, and stimulus representation in some sensory systems is characterized by a small number of precisely timed spikes. More generally, many of the most interesting tasks our brain performs involve the processing of temporally modulated stimuli and the production of temporally accurate output, for example, fast visual processing (reading), auditory processing of time varying signal (speech) and production of fast accurate motor output (speech, singing, writing or playing a musical instrument). 
These findings suggest that the brain possesses a machinery for extracting information embedded in the timings of spikes and for generating series of precisely timed spikes. Thus, understanding the power and limitations of spike-timing based computation and learning is of fundamental importance in computational neuroscience.
In our work we study the way in which neurons may perform the processing and generation of spike-timing based inputs and outputs. Our goal is to develop a complete model for neuronal learning in the temporal domain.

 

 
I am a Postdoctoral Fellow in the lab. My main research interest is in ascertaining the fundamental principles that underlie computation in networks of neurons in the brain. I am also interested in issues of intention and how it is manifested in human large scale neuronal networks, especially as measured using EEG.
 

 
Sam Zibman
 

 
Jonathan Kadmon
I am studying the dynamical and computational properties of large recurrent neuronal networks in the chaotic dynamical regime. The brain, and cortex in particular, are built from very large and highly complex networks of interconnected neurons. Though the dynamics of a single neuron may be complicated by it self, the collective behaviour of large networks create emergent phenomena that origin from only a handful of common properties of the individual neurons. I am using theoretical tools from statistical mechanics to derive such macroscopic dynamics based on models of single neuron. In particular I am interested in the large-scale and long-time patterns that are observed in chaotic large network and in the computational capacity of such systems.

 
Uri Cohen
My main field of interest is theory of learning in neural networks. I'm working on understanding the neural mechanism underlying the development of invariant representations: how low-level sensory signals are transformed into abstract, highly selective and very tolerant representations reported in high-level brain areas.
 

 
Itamar Landau