Research Interest: I am interested in understanding the computational principles underlying complex behaviors and cognitive processes, and how these emerge from the (relatively) simple microscopic physical processes in the brain. In particular, I am interested in the neural basis and computational principles underlying operant learning. To that goal, I study the behavior of humans and animals in controlled and natural conditions, and develop computational and mechanistic neural networks models to explain these behaviors. Additional areas of research in my laboratory include unsupervised learning in perception, and the dynamics of synapses in the cortex and its relation to learning. Publications: Raviv, O., Ahissar, M. and Loewenstein Y., (2012) How Recent History Affects Perception: The Normative Approach and Its Heuristic Approximation PLoS Comput Biol 8(10): e1002731.Neiman, T. and Loewenstein Y., (2013), Covariance-based synaptic plasticity in an attractor network model accounts for fast adaptation in free operant learning, J. Neurosci., 33(4) 1521-1534 6.908.Shteingart, H., Neiman, T. and Loewenstein Y., (2013) The role of first impression in operant learning, J. Exp. Psychol.: General,142(2):476-488.Sorek, M., Balaban, N.Q. and Loewenstein Y., (2013) Stochasticity, Bistability and the Wisdom of Crowds: a Model for Associative Learning in GeneticRegulatory Networks, PLoS Comput Biol, 9(8): e1003179.Laquitaine, S., Piron C., Abellanas, D., Loewenstein Y.*, Boraud T.* (2013), Early population response of Dorso-Lateral Putamen neurons predicts the ability to learn, PLoS ONE, 8(11) e80683.. *Equal contribution.Neiman, T. and Loewenstein Y. (2014), Spatial Generalization in Operant Learning: Lessons from Professional Basketball, PLoS Comput Biol 10(5): e1003623.