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© Research

About

Synaptic and neural basis of behavior and disease

A paramount challenge of Neuroscience research in the 21st century is to understand the neural mechanisms underlying behavior.  The advent of two-photon microscopy, optogenetics, and state-of-the-art tracing methods has shed new light on the connectivity of neural circuits associated with behavior. However, the specific functional transformations used by synapses, neurons and their networks to process information within neural circuits are still only speculative. The research program of the Unit of Dynamic Neuronal Imaging (UIDN) spans the nanoscopic to the organism scales using multidisciplinary approaches (neurophysiology, optics and computational neuroscience) to address the challenges of experimental observation and interpretation of the molecular and cellular basis of information processing underlying brain function. Top down only approaches lack the mechanistic detail to describe the molecular influence on behavior, whereas bottom up only approaches lack the relevant context to relate molecular and cellular mechanisms to their transformation of specific information.

UIDN is committed to identification and characterization of the biophysical mechanisms of synapses and neurons, and how those mechanisms act as computation building blocks for neural circuit function. We believe these computational modules are likely to represent canonical transformations of information (i.e. rules) used by other brain regions. Moreover, studies spanning across spatial scales are essential for understanding the genetic and molecular basis of mental disorders and critical for identifying new therapeutic strategies. Our general strategy will be to identify and localize molecules influencing the strength, time course and plasticity of synaptic transmission, examine the neuronal mechanisms defining dendritic integration many synapses, and finally to perform in vivo recordings of the activity of each neuron type in response to different sensory stimuli and compare to network model predictions that incorporate the identified synaptic and neuronal mechanisms.

The crystalline cytoarchitecture of cerebellar cortex (CC) is comprised of very few neuron types and shares wiring features with microcircuits in other brain regions. But the CC circuit is notably simpler and more regularly structured than circuits in other brain regions, making it an ideal model system to explore the molecular and cellular mechanisms of synaptic, neuronal and network computations. This simple circuitry inspired one of the first theoretical models of neural circuits (Marr, 1968 and Albus, 1971), thus providing a rich framework for hypothesis generation of putative circuit computations. Our goal is not to provide a detailed model of the cerebellum, which is intractable if we include molecular signaling, but to strategically examine computational consequences of the molecular and cellular mechanisms using theoretical models, which can then be tested in awake animals. Thus the ability to perform detailed mechanistic studies in a relatively simple neural circuit, will provide a detailed examination of the neural basis of behavior unrivaled by studies in other brain regions.

Our present and future research is dedicated to identifying and characterizing the synaptic, neuronal and circuit mechanisms underlying circuit computations in cerebellar cortex (CC). The CC receives sensory and cortical information via mossy-fibers that form excitatory synapses onto granule cells (GCs). GCs in turn excite Purkinje cells (PCs), the principal output of the CC, dendritic (stellate cells, SCs) and somatic targeting (basket cells, BCs). The cerebellum uses sensory information about the body and the surrounding world to enable the precise execution of learned movements. Cerebellar deficits have been implicated in developmental disorders involving altered multi-sensory integration such as dyslexia, autism spectrum disorders and schizophrenia. Thus critical to cerebellar function is the necessity of the CC to integrate multiple sensory and non-sensory modalities in order to encode combinations of sensory features (context). In recent years the UIDN and others have embraced the notion that functional synaptic diversity is critical for encoding sensory information within networks of neurons in CC. Using a team of biologists, physicists, and computational neuroscientists, the UIDN uses a multi-technique approach to studying the synaptic and neural basis of behavior: 1) State-of-the-art in situ and in vivo electrophysiological techniques, 2) State-of-the-art optical techniques: conventional confocal and 2-photon imaging, holographic photolysis, smart scanning, and super-resolution imaging, and 3) mathematical approaches (numerical and analytical) for predicting nanoscale signaling, understanding voltage transformations by complex dendritic trees, and finally for interpreting network computations.

 

 

 

 

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Former members

Alexandra Tran-Van-Minh
François Chabrol
Emilienne Repak
Yukihiro Nakamura
Thérèse Abrahamsson
Ann Fink
Jonathan Bradley

Marla Feller (Berkeley, sabbatical)
Graeme Davis (UCSF, sabbatical)
Thomas Otis (UCLA, sabbatical)

Group Picture

IDN_Lab_Photo_Sept2015

Unit of Dynamic Neuronal Imaging, Institut Pasteur, September 2015