Columbia Workshop on Brain Circuits, Memory and Computation


BCMC 2017

Monday and Tuesday, March 13-14, 2017

Center for Neural Engineering and Computation

Columbia University, New York, NY 10027


Overview

The goal of the workshop is to bring together researchers interested in developing executable models of neural computation/processing of the brain of model organisms. Of interest are models of computation that consist of elementary units of processing using brain circuits and memory elements. Elementary units of computation/processing include population encoding/decoding circuits with biophysically-grounded neuron models, non-linear dendritic processors for motion detection/direction selectivity, spike processing and pattern recognition neural circuits, movement control and decision-making circuits, etc. Memory units include models of spatio-temporal memory circuits, circuit models for memory access and storage, etc. A major aim of the workshop is to explore the integration of various sensory and control circuits in higher brain centers.

A Fruit Fly Brain Hackathon is being conducted in conjunction with the workshop. Workshop participants are welcome to attend the hackathon.

Organizer and Program Chair

Aurel A. Lazar, Department of Electrical Engineering, Columbia University.

Registration

Registration is free but all participants have to register. Thank you!

Lodging and Directions to Venue

Please follow this link for lodging details and directions to the hotel and venue.




Program Overview (Confirmed Speakers)

Dinu Florin Albeanu, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY.

Vijay Balasubramanian, Department of Physics, University of Pennsylvania.

Albert Cardona, Janelia Research Campus, Ashburn, VA.


Long-Term Memory Requires Sequential Protein Synthesis in Discrete Mushroom Body Output Neurons in Drosophila

Ann-Shyn Chiang, National Tsing Hua University, Hsinchu, Taiwan.

Creating long-term memory (LTM) requires new proteins to stabilize learning-induced synaptic changes in the brains. In the fruit flies, Drosophila melanogaster, aversive olfactory learning forms several phases of memory to associate an odor with coincident punishment in sparse Kenyon cells within the mushroom body (MB). How brain circuits translate early phases of labile memory into long-lasting stable memory remains unclear. Here, we show that learning-induced new proteins for aversive olfactory LTM occur at discrete MB output neurons (MBONs). Acutely blocking protein synthesis with a temperature-sensitive ribosomal toxin, we found that LTM formation requires sequential new proteins in three subsets of MBONs at different time windows after learning. RNAi-mediated down-regulation of oo18 RNA-binding proteins (ORB) in any of these MBONs impaired LTM. Neurotransmission outputs from these MBONs are required during specific time windows after learning and essential during LTM retrieval. Together, these results suggest a LTM formation model that early labile memory encoded in neural activities of sparse Kenyon cells consolidates into stable LTM at discrete postsynaptic MBONs by sequential ORBregulated local protein synthesis at active synapses.

Joint work with Jie-Kai Wu, Chu-Yi Tai, Kuang-Lin Feng, Shiu-Ling Chen and Chun-Chao Chen.


An Internal Representation of Walking Movements in a Visual Area of the Drosophila Brain

M. Eugenia Chiappe, Champalimaud Centre for the Unknown, Lisbon.

Animals move their body in specific, coordinated, and flexible ways during locomotion, and this high-performance control depends on an internal estimate of self-movement. Self-movement estimation is also critical for the proper interpretation of external sensory information that may guide locomotive behaviors. This internal representation is thought to arise from the distributed activity of sensorimotor circuits; however, it is still unclear what circuits are involved, and how they implement motor-sensory coordination. Here we will discuss our initial attempts to uncover these issues in Drosophila melanogaster by performing simultaneous recordings of the fly’s walking behavior and the activity of a group of optic-flow processing neurons, the HS cells. Our results show that HS cells encode information about the fly’s walking movements in darkness, suggesting the presence of extra-retinal signals in this network. HS cells show direction selective responses driven by the turning direction of the fly, which cooperate with the cells’ canonical (visual) direction selectivity under visual stimulation. We propose that the observed convergence between visual and motor-related information during walking in HS cells creates a non-ambiguous, quantitative central representation of the movement of the body through space. This accurate representation may guide the fly’s forward movements during explorative walking. Ongoing experiments in Virtual Worlds for freely walking flies are testing this functional hypothesis.


Jonathan B. Demb, Yale School of Medicine.

Barry J. Dickson, Janelia Research Campus, Ashburn, VA.

Anmo J. Kim, Rockefeller University, New York.

Konrad P. Kording, Feinberg School of Medicine, Northwestern University.


How Improvements in Neuroanatomy Could Shed Light on Cognitive Architecture

Adam H. Marblestone, Synthetic Neurobiology Group, MIT.

Neuroanatomy would benefit both from improved methods, and from computationally motivated theories that make distinctive predictions about mesoscale neural structures. I will describe an optical approach we are developing for high-throughput molecularly annotated connectomics, which treats long-range and short-range connections on an equal footing (a collaborative project among the Church, Boyden and Zador labs), introduce a theory of symbolic processing in the thalamo-cortico-striatal system (work with Ken Hayworth), and suggest how the former might in the future shed light on the latter. More generally, I will suggest neuroanatomical questions that could illuminate connections between biological brains and the kinds of structured architectures that are now finding use in deep learning.


Neural Circuits Encoding Wind Direction in Drosophila

Katherine I. Nagel, NYU Medical School.


Circuits for Learning and Memory in the Adult Drosophila Mushroom Body

Gerald M. Rubin, Janelia Research Campus, Ashburn, VA.

The mushroom body (MB) is the major site of associative learning in insects. In the MB of adult Drosophila, each of ~20 types of dopaminergic neurons (DANs) innervates distinct small compartmental regions of the mushroom body (MB). Different subsets of these DANs signal punishment and reward. We have recently shown (Aso & Rubin, eLife 2016) that these DANs appear to be able to “write” parallel memories in different MB compartments using distinct “learning rules”. I will discuss our ongoing efforts to explore the rules for writing, forgetting and updating memories in each compartment.

I will also present recent work done with the FlyEM Project Team at Janelia to determine the connectome of the three compartments of the vertical (or lobe) of the MB. The unprecedented level of detail of this dataset should enable modeling studies not previously possible and suggests many experiments to explore the physiological and behavioral significance of the circuit motifs we observed. That many of these motifs were not anticipated by over thirty years of extensive anatomical, experimental and theoretical studies on the role of the insect MB argues strongly for the value of electron microscopic connectomics studies.


Parallel Olfactory Coding Mechanisms in the Drosophila Brain

Silke Sachse, Max Planck Institute for Chemical Ecology, Jena.

Animals use sensory systems to navigate the environment in a way that optimizes their survival and reproduction. The olfactory system plays here a major role in encoding chemical information and translating the outside world into a neuronal representation that enables an animal to take odorguided decisions. The vinegar fly represents a premier model system for studying olfactory processing mechanisms since it exhibits a stereotyped architecture which is similar to its mammalian counterpart, but is less complex and highly tractable as well as susceptible to genetic manipulations. By exploiting these genetic techniques and linking them to neurophysiological, molecular and behavioral methods, we are dissecting the neural circuits underlying olfactory coding and processing in the context of odor-guided behavior in Drosophila.

In order to understand higher odor processing we are scrutinizing the lateral horn (LH) of the protocerebrum, a brain region that is assumed to be involved in innate behavior. Two populations of projection neurons – excitatory (ePNs) and inhibitory (iPNs) – convey the odor information from the antennal lobe to the LH. We analyzed how different odor features such as hedonic valence and intensity are functionally integrated in this brain area. We could previously demonstrate that the LH can be classified into three functional odor response domains that decode opposing hedonic valences and odor intensity that derive from iPNs and third-order neurons that further innervate the ventro-lateral protocerebum. To investigate whether ePNs accomplish a comparable categorization of odor features and whether iPNs and ePNs interact, we have elucidated the neuronal circuitry of the two PN populations at a morphological and functional level. The talk will summarize our recent insights into the processing strategies of the two parallel output pathways to the higher brain and their contribution to odor perception.


Motion Vision: From Behavior to Cellular and Circuit Function

Marion Silies, European Neuroscience Institute, Göttingen.

Many animals use visual motion cues to navigate through the environment, find prey or escape predators. Long-standing theoretical models have made predictions about the computations that compare light signals across space and time to detect motion. Over the past years, core circuits that can implement such motion computations in Drosophila have been proposed based on connectomic and physiological studies. In the fly visual system, separate ON and OFF pathway exist that are specialized to detect the movement of light or dark edges. These pathways already split downstream of photoreceptors in the lamina, where L1 and L2 provide the major inputs to the ON and OFF pathway, respectively.

Using forward genetic approaches, we identified neurons of a third visual pathway in which the first order interneurons L3 provides a key input to direction-selective neurons via the medulla neuron Tm9. Neurons of this pathway are behaviorally required for OFF motion detection and form a novel, parallel OFF pathway. Using in vivo two photon calcium imaging, we showed that this pathway carries sustained responses to contrast changes and exhibits receptive field properties that inform elementary motion detectors about wide regions of visual space. Thus, the two OFF pathways differ in their physiological properties and the first order interneurons L2 and L3 already provide fast and slow inputs to OFF edge motion detection. We are currently investigating the mechanisms that shape the distinct properties of the OFF pathway. Our goal is to understand the cellular mechanisms, circuits and computations that implement behavioral responses to visual motion.


The Larval Standard Brain of Drosophila: The Mushroom Body Learning and Memory Network

Andreas S. Thum, Department of Biology, University of Konstanz.

Brains organize behavior. This involves the integration of present sensory input, past experience, and options for future behavior. The insect mushroom body is a paradigmatic case of a central brain structure bringing about such triadic integration. We use larval Drosophila to systematically study these processes at single-cell resolution. Our focus is bipartite as it includes research on the anatomical and behavioral architecture of the larval mushroom body circuit. On the anatomical level we use serial electron microscopy (EM) to reconstruct a synapse-resolution map (connectome) of the complete MB wiring diagram in a Drosophila larva consisting per hemisphere of about 110 KCs and exactly 24 mushroom body output neurons, 7 dopaminergic input neurons, 2 paired and 2 unpaired octopaminergic input neurons, 5 additional mushroom body modulatory input neurons, and an additional GABAergic feedback neuron. In addition, we study these mushroom body function with respect to appetitive olfactory learning at single-cell resolution, focusing on the behavioral architecture of the mushroom body input and output neurons, as well as the mushroom body intrinsic APL neuron. Ultimately, this data is then integrated in a newly established standard atlas for the larval brain, a five-part approach that includes the generation of an image registration framework, the generation of a larval standard brain, the segmentation and denomination of identified brain structures, the registration of several thousand Gal4 stocks onto the standard brain, and the organization of the obtained information in a web-based open access database called brainbase. Taken together this work provides a rich picture on multiple levels of how an insect central brain structure is anatomically and functionally organized.


Tim P. Vogels, Department of Physiology, Anatomy and Genetics, University of Oxford.



More information about BCMC 2017 can be found here.






Tweet this! Share on Facebook Email a friend Share on Delicious Share on StumbleUpon Share on Digg Share on Reddit Share on Technorati