MAR 11, 2020 6:00 AM PDT

PANEL: Sensorimotor Processing, Decision Making, and Internal States: Towards a Realistic Multiscale Circuit Model of the Larval Zebrafish Brain

Presented at: Neuroscience 2020
C.E. Credits: P.A.C.E. CE Florida CE
  • Professor of Molecular and Cellular Biology, Harvard University
      Dr. Engert was originally trained as a physicist at the Ludwigs-Maximilians-Universität in Munich. He transitioned into neuroscience under the mentorship of Tobias Bonhoeffer at the MPI of Neurobiology in Munich and, as a postdoctoral fellow, in the laboratory of Mu-ming Poo at UCSD and UC Berkeley. In early 2001 he moved to Harvard as a junior faculty where he switched his focus to the larval zebrafish as a model system for circuits neuroscience.
      The general goal of Dr. Engert's current research is the study of larval zebrafish in the context of neuroethology and the evolution of adaptive natural behaviors. To that end he has established a series of quantitative behavioral assays that allow the precise dissection of behavioral algorithms. These assays now provide a framework to generate hypotheses for neural circuit implementations that then can be validated and constrained by whole brain monitoring of neuronal activity, correlated brain wide connectomics, and by targeted interrogation of circuits via optogenetic tools.
    • Jeremy R. Knowles Professor of Molecular and Cellular Biology at Harvard
        Jeff Lichtman is Jeremy R. Knowles Professor of Molecular and Cellular Biology at Harvard. He received an AB from Bowdoin (1973), and an M.D. and Ph.D. from Washington University (1980) where he worked for 30 years before moving to Cambridge in 2004. He is a member of the Center for Brain Science. Lichtman's research interest revolves around the question of how mammalian brain circuits are physically altered by experiences, especially in early life. He has focused on the dramatic re-wiring of neural connections that takes place in early postnatal development when animals are doing most of their learning. This work has required the development of techniques such as "Brainbow" transgenic mice to visualize neural connections and monitor how they are altered over time. Recently his efforts have focused on developing new electron microscopy methods to map the entire wiring diagram of the developing and adult brain. This "connectomics" approach has as one of its aims uncovering the ways information is stored in neural networks.
      • Professor of Physics and Neuroscience at Hebrew University and Director of Swartz Program in Theoretical Neuroscience at Harvard University
          Haim Sompolinsky is Professor of Physics and Neuroscience at Hebrew University and Director of Swartz Program in Theoretical Neuroscience at Harvard University. Sompolinsky's research goal is to uncover the fundamental principles that link the structure, dynamics and functions of the brain, focusing on collective dynamical properties and their emergent computation. He has pioneered the field of Computational Neuroscience, using statistical mechanics, dynamical systems theory, information theory and machine learning, to build mathematical models of neuronal circuits and new methods for analyzing their information processing capabilities. His neuroscience research builds on his early work on the statistical mechanics of complex disordered systems.

          His contribution to computational neuroscience include: theories of long-term and short-term memory, dynamic and computational principles in recurrent cortical circuits, attractors and attractor manifolds, the ring attractor, balanced networks, neural noise and chaos, neuronal population codes, statistical mechanics of learning in neuronal networks, spike-time based synaptic plasticity and neural learning algorithms, principles of compressed sensing, sparse coding and dimensionality transformation in sensory representation.

          Among Sompolinsky's recent efforts are: the development of multi-scaling approaches for modeling whole brain structural and functional properties of the zebrafish larvae; elucidating computational principles of neuronal processing across cortical sensory hierarchies; and investigating the interface between Artificial and Biological Intelligence. He also studies neuronal mechanisms of human volition, and explores the impact of physics and neuroscience on the foundations of human freedom and agency.


        We present here a framework to generate a realistic multiscale circuit model of the larval zebrafish brain – the multiscale virtual fish (MVF). The model will be based on algorithms inferred from behavioral assays and it will span spatial ranges across three levels: from the nanoscale at the synaptic level, to the microscale describing local circuits, to the macroscale brain-wide activity patterns distributed across many regions. The model will be constrained and validated by functional imaging and sparse connectomics of identified circuit elements.

        Specifically, we focus on five ethologically relevant behaviors: the opto-motor response, phototaxis, rheotaxis, escape, and hunting. First, we extract the precise algorithms underlying each behavior and develop a version of the circuit model to understand their neural implementation. Second, we refine the model to account for multimodal integration and decision making, events that naturally happen when conflicting stimuli driving different behaviors are presented simultaneously. Third, we will examine how internal brain states, such as hunger or stress, influence and modulate the specific behaviors or behavioral interactions. Implementation of neurochemical modulation into the framework of the MVF will be achieved through simulation of highly conserved neuromodulatory neurotransmitter systems such as serotonin, acetylcholine, epinephrine, dopamine and oxytocin.

        Learning Objectives:

        1. Describe the advantages (and disadvantages) of the larval zebrafish as a model system for Neuroscience.
        2. Discuss what specific questions in neuroscience can be answered by combining brain wide recordings of neural activity with brain wide connectomics in the same animal.

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