Bulletin of the American Physical Society
APS March Meeting 2017
Volume 62, Number 4
Monday–Friday, March 13–17, 2017; New Orleans, Louisiana
Session V4: Neural Control of BehaviorFocus
|
Hide Abstracts |
Sponsoring Units: DBIO Chair: Gordon Berman, Emory University Room: 263 |
Thursday, March 16, 2017 2:30PM - 2:42PM |
V4.00001: Development of a two photon microscope for tracking Drosophila larvae Doycho Karagyozov, Mirna Mihovilovic Skanata, Marc Gershow Current in vivo methods for measuring neural activity in Drosophila larva require immobilization of the animal. Although we can record neural signals while stimulating the sensory organs, we cannot read the behavioral output because we have prevented the animal from moving. Many research questions cannot be answered without observation of neural activity in behaving (freely-moving) animals. We incorporated a Tunable Acoustic Gradient (TAG) lens into a two-photon microscope to achieve a 70kHz axial scan rate, enabling volumetric imaging at tens of hertz. We then implemented a tracking algorithm based on a Kalman filter to maintain the neurons of interest in the field of view and in focus during the rapid three dimensional motion of a free larva. Preliminary results show successful tracking of a neuron moving at speeds reaching 500$\mu $m/s. [Preview Abstract] |
Thursday, March 16, 2017 2:42PM - 2:54PM |
V4.00002: Adaptation to Variance of Stimuli in Drosophila Larva Navigation Jason Wolk, Ruben Gepner, Marc Gershow In order to respond to stimuli that vary over orders of magnitude while also being capable of sensing very small changes, neural systems must be capable of rapidly adapting to the variance of stimuli.~We study this adaptation in Drosophila larvae responding to varying visual signals and optogenetically induced fictitious odors using an infrared illuminated arena and custom computer vision software. Larval navigational decisions (when to turn) are modeled as the output a linear-nonlinear Poisson process.~The development of the nonlinear turn rate in response to changes in variance is tracked using an adaptive point process filter determining the rate of adaptation to different stimulus profiles. [Preview Abstract] |
Thursday, March 16, 2017 2:54PM - 3:06PM |
V4.00003: Quantification of Behavioral Stereotypy in Flies Jason Manley, Gordon Berman, Joshua Shaevitz A commonly accepted assumption in the study of behavior is that an organism’s behavioral repertoire can be represented by a relatively small set of stereotyped actions. Here, ``stereotypy" is defined as a measure of the similarity of repetitions of a behavior. Our group utilizes data-driven analyses on videos of ground-based \textit{Drosophila} to organize the set of spontaneous behaviors into a two-dimensional map, or behavioral space. We utilize this framework to define a metric for behavioral stereotypy. This measure quantifies the variance in a given behavior’s periodic trajectory through a space representing its postural degrees of freedom. This newly developed behavioral metric has confirmed a high degree of stereotypy among most behaviors and we correlate stereotypy with various physiological effects. [Preview Abstract] |
Thursday, March 16, 2017 3:06PM - 3:42PM |
V4.00004: Probing the neural control of behavior with whole brain imaging in zebrafish Invited Speaker: Misha Ahrens |
Thursday, March 16, 2017 3:42PM - 3:54PM |
V4.00005: Dynamics of Bayesian non-Gaussian sensorimotor learning with multiple time scales Baohua Zhou, David Hofmann, Samuel Sober, Ilya Nemenman Various theoretical and experimental studies have suggested that sensorimotor learning in animals happens on multiple time scales. In such models, animals can respond to perturbations quickly but keep memories for a long period of time. However, those previous models only focus on average learning behaviors. Here, we propose a model with multiple time scales that deals with the dynamics of whole behavior distributions. The model includes multiple memories, each with a non-Gaussian distribution and its own associated time scale. The memories are combined to generate a distribution of the desired motor command. Our model explains simultaneously the dynamics of distributions of the songbird vocal behaviors in various experiments, including adaptations after step changes or ramps in the error signals and dynamics of forgetting during the washout period, where an immediate sharp approach to the baseline is followed by a prolonged decay. [Preview Abstract] |
Thursday, March 16, 2017 3:54PM - 4:06PM |
V4.00006: Multi-sensory integration in a small brain Ruben Gepner, Jason Wolk, Marc Gershow Understanding how fluctuating multi-sensory stimuli are integrated and transformed in neural circuits has proved a difficult task. To address this question, we study the sensori-motor transformations happening in the brain of the Drosophila larva, a tractable model system with about 10,000 neurons. Using genetic tools that allow us to manipulate the activity of individual brain cells through their transparent body, we observe the stochastic decisions made by freely-behaving animals as their visual and olfactory environments fluctuate independently. We then use simple linear-nonlinear models to correlate outputs with relevant features in the inputs, and adaptive filtering processes to track changes in these relevant parameters used by the larva's brain to make decisions. We show how these techniques allow us to probe how statistics of stimuli from different sensory modalities combine to affect behavior, and can potentially guide our understanding of how neural circuits are anatomically and functionally integrated. [Preview Abstract] |
Thursday, March 16, 2017 4:06PM - 4:18PM |
V4.00007: Gamma Rhythm Simulations in Alzheimer's Disease Samuel Montgomery, Carlos Perez, Ghanim Ullah The different neural rhythms that occur during the sleep--wake cycle regulate the brain's multiple functions. Memory acquisition occurs during fast gamma rhythms during consciousness, while slow oscillations mediate memory consolidation and erasure during sleep. At the neural network level, these rhythms are generated by the finely timed activity within excitatory and inhibitory neurons. In Alzheimer's Disease (AD) the function of inhibitory neurons is compromised due to an increase in amyloid beta (A$\beta )$ leading to elevated sodium leakage from extracellular space in the hippocampus. Using a Hodgkin-Huxley formalism, heightened sodium leakage current into inhibitory neurons is observed to compromise functionality. Using a simple two neuron system it was observed that as the conductance of the sodium leakage current is increased in inhibitory neurons there is a significant decrease in spiking frequency regarding the membrane potential. This triggers a significant increase in excitatory spiking leading to aberrant network behavior similar to that seen in AD patients. The next step is to extend this model to a larger neuronal system with varying synaptic densities and conductance strengths as well as deterministic and stochastic drives. [Preview Abstract] |
Thursday, March 16, 2017 4:18PM - 4:30PM |
V4.00008: How doing a dynamical analysis of gait movement may provide information about Autism D. Wu, E. Torres, J. Nguyen, S. Mistry, C. Whyatt, V. Kalampratsidou, A. Kolevzon, J. Jose Individuals with Autism Spectrum Disorder (ASD) are known to have deficits in language and social skills. They also have deficits on how they move. Why individuals get ASD? It is not generally known. There is, however, one particular group of children with a SHANK3 gene deficiency (Phelan-McDermid Syndrome (PMDS)) that present symptoms similar to ASD. We have been searching for universal mechanism in ASD going beyond the usual heterogeneous ASD symptoms. We studied motions in gaits for both PMDS patients and idiopathic ASD. We have examined their motions continuously at milliseconds time scale, away from naked eye detection. Gait is a complex process, requiring a complex integration and coordination of different joints' motions (Bernstein, N., 1967). Significant information about the development and/or deficits in the sensory system is hidden in our gaits. We discovered that the speed smoothness in feet motion during gaits is a critical feature that provides a significant distinction between subjects with ASD and typical controls. The differences in appearance of the speed fluctuations suggested a different coordination mechanism in subjects with disorders. Our work provides a very important feature in gait motion that has significant physiological information. [Preview Abstract] |
Thursday, March 16, 2017 4:30PM - 4:42PM |
V4.00009: Adaptive optimal training of animal behavior Ji Hyun Bak, Jung Yoon Choi, Athena Akrami, Ilana Witten, Jonathan Pillow Neuroscience experiments often require training animals to perform tasks designed to elicit various sensory, cognitive, and motor behaviors. Training typically involves a series of gradual adjustments of stimulus conditions and rewards in order to bring about learning. However, training protocols are usually hand-designed, and often require weeks or months to achieve a desired level of task performance. Here we combine ideas from reinforcement learning and adaptive optimal experimental design to formulate methods for efficient training of animal behavior. Our work addresses two intriguing problems at once: first, it seeks to infer the learning rules underlying an animal's behavioral changes during training; second, it seeks to exploit these rules to select stimuli that will maximize the rate of learning toward a desired objective. We develop and test these methods using data collected from rats during training on a two-interval sensory discrimination task. We show that we can accurately infer the parameters of a learning algorithm that describes how the animal's internal model of the task evolves over the course of training. We also demonstrate by simulation that our method can provide a substantial speedup over standard training methods. [Preview Abstract] |
Thursday, March 16, 2017 4:42PM - 4:54PM |
V4.00010: Bayesian Ising approximation for learning dictionaries of multispike timing patterns in premotor neurons Damian Hernandez Lahme, Samuel Sober, Ilya Nemenman Important questions in computational neuroscience are whether, how much, and how information is encoded in the precise timing of neural action potentials. We recently demonstrated that, in the premotor cortex during vocal control in songbirds, spike timing is far more informative about upcoming behavior than is spike rate (Tang et al, 2014). However, identification of complete dictionaries that relate spike timing patterns with the controled behavior remains an elusive problem. Here we present a computational approach to deciphering such codes for individual neurons in the songbird premotor area RA, an analog of mammalian primary motor cortex. Specifically, we analyze which multispike patterns of neural activity predict features of the upcoming vocalization, and hence are important codewords. We use a recently introduced Bayesian Ising Approximation, which properly accounts for the fact that many codewords overlap and hence are not independent. Our results show which complex, temporally precise multispike combinations are used by individual neurons to control acoustic features of the produced song, and that these code words are different across individual neurons and across different acoustic features. [Preview Abstract] |
Thursday, March 16, 2017 4:54PM - 5:06PM |
V4.00011: Brain Modularity Mediates the Relation between Task Complexity and Performance Fengdan Ye, Qiuhai Yue, Randi Martin, Simon Fischer-Baum, Aurora Ramos-Nuñez, Michael Deem Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases, and other tasks showing worse performance. A recent theoretical model suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of behavioral tasks. Complex and simple tasks were defined on the basis of whether they drew on executive attention. Consistent with predictions, we found a negative correlation between individuals’ modularity and their performance on the complex tasks but a positive correlation with performance on the simple tasks. The results presented here provide a framework for linking measures of whole brain organization to cognitive processing. [Preview Abstract] |
Thursday, March 16, 2017 5:06PM - 5:18PM |
V4.00012: Estimation of the neural drive to the muscle from surface electromyograms David Hofmann Muscle force is highly correlated with the standard deviation of the surface electromyogram (sEMG) produced by the active muscle. Correctly estimating this quantity of non-stationary sEMG and understanding its relation to neural drive and muscle force is of paramount importance. The single constituents of the sEMG are called motor unit action potentials whose biphasic amplitude can interfere (named amplitude cancellation), potentially affecting the standard deviation (Keenan etal. 2005). However, when certain conditions are met the Campbell-Hardy theorem suggests that amplitude cancellation does not affect the standard deviation. By simulation of the sEMG, we verify the applicability of this theorem to myoelectric signals and investigate deviations from its conditions to obtain a more realistic setting. We find no difference in estimated standard deviation with and without interference, standing in stark contrast to previous results (Keenan etal. 2008, Farina etal. 2010). Furthermore, since the theorem provides us with the functional relationship between standard deviation and neural drive we conclude that complex methods based on high density electrode arrays and blind source separation might not bear substantial advantages for neural drive estimation (Farina and Holobar 2016). [Preview Abstract] |
Thursday, March 16, 2017 5:18PM - 5:30PM |
V4.00013: Swimming behavior of zebrafish is accurately classified by direct modeling and behavioral space analysis Ruopei Feng, Yann Chemla, Martin Gruebele Larval zebrafish is a popular organism in the search for the correlation between locomotion behavior and neural pathways because of their highly stereotyped and temporally episodic swimming motion. This correlation is usually investigated using electrophysiological recordings of neural activities in partially immobilized fish. Seeking for a way to study animal behavior without constraints or intruding electrodes, which can in turn modify their behavior, our lab has introduced a parameter-free approach which allows automated classification of the locomotion behaviors of freely swimming fish. We looked into several types of swimming bouts including free swimming and two modes of escape responses and established a new classification of these behaviors. Combined with a neurokinematic model, our analysis showed the capability to probe intrinsic properties of the underlying neural pathways of freely swimming larval zebrafish by inspecting swimming movies only. [Preview Abstract] |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700