Measuring neuronal tuning curves has been instrumental for many discoveries in neuroscience but requires a-priori assumptions regarding the identity of the encoded variables. We applied unsupervised learning to large-scale neuronal recordings in behaving mice from circuits involved in spatial cognition, and uncovered a highly-organized internal structure of ensemble activity patterns. This emergent structure allowed defining for each neuron an ‘internal tuning-curve’ that characterizes its activity relative to the network activity, rather than relative to any pre-defined external variable – revealing place-tuning and head-direction tuning without relying on measurements of place or head-direction. Similar investigation in prefrontal cortex revealed schematic representations of distances and actions, and exposed a previously unknown variable, the ‘trajectory-phase’. The internal structure was conserved across mice, allowing using one animal’s data to decode another animal’s behavior. Thus, the internal structure of neuronal activity itself enables reconstructing internal representations and discovering new behavioral variables hidden within a neural code.
I did my undergraduate studies at the Hebrew University of Jerusalem in physics and cognitive science. I then studied at the Weizmann Institute of Science, where I completed my MSc under the supervision of Prof. Misha Tsodyks and PhD under the joint supervision of Prof. Misha Tsodyks and Prof. Nachum Ulavovsky. Throughout my research I applied both theoretical modeling and electrophysiological experiments in behaving bats, to study the neuronal code within the hippocampal formation. I then joined the new laboratory of Dr. Yaniv Ziv, where we use novel optical imaging methods which allow longitudinal recording of neuronal activity from large neuronal populations in freely behaving mice. I am particularly interested in advanced analytical paradigms that are now applicable due to the constant up-scaling in numbers of simultaneously recorded neurons.
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