Neuronal Plasticity, Learning, and Memory
Who we are as human individuals is determined to a large degree by the sum-total of our memories, be they intimate recollections, precious facts, useful skills or convenient habits. Furthermore, we now know that the brain systems that permit us to mentally reenact selected moments of our personal past also enables us to embark on a private mental time travel to the future, plan, simulate scenarios and fly on the wings of our imagination. How does the brain accomplish all that? And why is it that sometimes our memory tricks us to confound the real with the imaginary, and that sometimes it may even fail us completely? Why is it, for example, that sometimes we are quick to abandon veridical recollections for erroneous information shared with us by others? And can we enhance memory on the one hand, and prevent or ameliorate memory deficits on the other?
These and related questions concerning memory are the focus of research of several research group in our Department. Together, we tackle memory at multiple levels of analyses, ranging from the basic neuronal machinery that implements synaptic and neuronal plasticity - i.e. elementary building blocks of systems that encode and store memory - to the concerted activity of neuronal circuits that encode and maintain experience-dependent mental representations of the world, to the brain systems that store mental images of events in our life and of places that we visited, and those that control the expression of these recollections at any given point in time. Some of us also investigate the processes and mechanisms that shape the memory of individuals in social milieu. Our multidisciplinary, multi-level experimental approach to neuronal plasticity, learning and memory encompasses a spectrum of cutting-edge methods, ranging from state-of-the-art molecular biology and electrophysiology in animal models to sophisticated psychophysics and behavior, modeling, and the most advanced behavioral protocols and functional neuroimaging of the behaving human brain.
Active Sensing and Motor-Sensory Loops
Sensation is usually not passive. Brains acquire information about their environment actively by selecting sensory targets and probing their features. Target selection and feature probing is controlled by the motor components of sensory systems that either move the sensory organ [e.g., eye, hand, tongue or whisker (in rodents)], move the sensed material across it (e.g., sniffing) or emit sensible energy that interacts with the object (e.g., echolocation in bats or electrolocation in electric fish). Thus, during active sensing, motor and sensory components of the same sense modality are intimately related to each other. How these intimate relations are implemented across the multiple neuronal loops connecting motor and sensory stations, how motor-sensory coordination optimizes sensation, and how out of all these perception emerges are exciting open questions.
Our department offers a rich and diverse range of research directions on active sensing and motor-sensory loops in a variety of animal systems: bat echolocation, human smelling, vision and touch, and rodent whisker-touch. Based on accumulated experience in these systems, advanced research approaches are employed across several research groups in our department, which enable accurate tracking of the interactions between sensory organs and their environment, detailed recordings and manipulations of the relevant neuronal components at various levels, and quantification of animal behavior. Combinations of these methods with conceptual theories and mechanistic models allow addressing the challenging, and fascinating questions related to active sensing, aiming at understanding how perception emerges from interactions between brains and environments.
Neural Circuits
The neuron is the basic element of brain function. However, neurons do not act singly – rather, they act through complex yet coherent neural circuits and networks, in order to generate sensory perceptions, behaviors, memories and thoughts. Although the behavioral output of different neural circuits are highly diverse, the principles of their structure and function is often surprisingly similar across different brain regions and animal species. This suggests that understanding the common fundamental building blocks of neural circuits will allow us to decipher the function of the brain as a whole. Such elucidation requires a multi-level approach and benefits greatly from cooperation between experimentalists and theorists.
Understanding the design and function of neural circuits requires the combination of diverse approaches. Integrating researchers from a variety of backgrounds such as biology, physics, mathematics, computer science, engineering, and psychology, we develop and apply novel approaches to study neural circuits. We combine extracellular and intracellular neural recording, imaging techniques spanning all scales from sub-cellular calcium imaging to whole-brain functional magnetic resonance imaging, and state-of-the-art anatomical, molecular and genomic techniques. These experimental approaches are complemented by theoretical studies of microcircuits and large-scale neuronal ensembles. The questions that are currently being addressed range from studies of active-sensing systems, through the neural codes of learning and memory, neural circuit plasticity and the neural basis of individual and group behaviors.
Theoretical and Computational Neuroscience
The brain is acting through the interaction of billions of neurons and myriads of action potentials that are criss-crossing within and between brain areas. To make sense of this complexity, one must use mathematical tools and sophisticated analysis methods in order to extract the important information and create reduced models of brain function. Together, faculty members and students at the Weizmann Institute, coming from diverse quantitative backgrounds such as physics, engineering, mathematics and computer science, are breaking new cutting-edge avenues in computational and theoretical neuroscience. We are using mathematical tools taken from Statistical Physics, Dynamicsl Systems, Machine Learning and Information Theory -- to name just a few -- in order to create new models and theories of brain function. Both analytical approaches and simulations are used heavily. By intense collaborations with experimental laboratories, these new theories and computational tools are put to the test, and then refined further. Our aim is to unravel the basic principles of brain operation and the underlying neural codes.