Bulletin of the American Physical Society
APS March Meeting 2015
Volume 60, Number 1
Monday–Friday, March 2–6, 2015; San Antonio, Texas
Session G50: Focus Session: Physics of Neural Systems I |
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Sponsoring Units: DBIO Chair: Ilya Nemenman, Emory University Room: 218 |
Tuesday, March 3, 2015 11:15AM - 11:27AM |
G50.00001: Human movement stochastic variability leads to diagnostic biomarkers In Autism Spectrum Disorders (ASD) Di Wu, Elizabeth B. Torres, Jorge V. Jose ASD is a spectrum of neurodevelopmental disorders. The high heterogeneity of the symptoms associated with the disorder impedes efficient diagnoses based on human observations. Recent advances with high-resolution MEM wearable sensors enable accurate movement measurements that may escape the naked eye. It calls for objective metrics to extract physiological relevant information from the rapidly accumulating data. In this talk we'll discuss the statistical analysis of movement data continuously collected with high-resolution sensors at 240Hz. We calculated statistical properties of speed fluctuations within the millisecond time range that closely correlate with the subjects' cognitive abilities. We computed the periodicity and synchronicity of the speed fluctuations' from their power spectrum and ensemble averaged two-point cross-correlation function. We built a two-parameter phase space from the temporal statistical analyses of the nearest neighbor fluctuations that provided a quantitative biomarker for ASD and adult normal subjects and further classified ASD severity. We also found age related developmental statistical signatures and potential ASD parental links in our movement dynamical studies. Our results may have direct clinical applications. [Preview Abstract] |
Tuesday, March 3, 2015 11:27AM - 11:39AM |
G50.00002: Adaptation tunes cortical dynamics to a critical regime during vision Woodrow Shew, Wesley Clawson, Jeff Pobst, Yahya Karimipanah, Nathaniel Wright, Ralf Wessel A long-standing hypothesis at the interface of physics and neuroscience is that neural networks self-organize to the critical point of a phase transition, thereby optimizing aspects of sensory information processing. This idea is partially supported by strong evidence for critical dynamics observed in the cerebral cortex, but has not been tested in systems with significant sensory input. Thus, the foundations of this hypothesis -- the self-organization process and how it manifests during strong sensory input -- remain unstudied experimentally. Here we report microelectrode array measurements from visual cortex of turtles during visual stimulation of the retina. We show experimentally and in a computational model that strong sensory input initially elicits cortical network dynamics that are not critical, but adaptive changes in the network rapidly tune the system to criticality. This conclusion is based on observations of multifaceted scaling laws predicted to occur at criticality. Our findings establish sensory adaptation as a self-organizing mechanism which maintains criticality in visual cortex during sensory information processing. [Preview Abstract] |
Tuesday, March 3, 2015 11:39AM - 11:51AM |
G50.00003: Rich club neurons dominate Information Transfer in local cortical networks Sunny Nigam, Masanori Shimono, Olaf Sporns, John Beggs The performance of complex networks depends on how they route their traffic. It is unknown how information is transferred in local cortical networks of hundreds of closely-spaced neurons. To address this, it is necessary to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512 electrode array (60 $\mu$m spacing) to record spontaneous activity at 20 kHz, simultaneously from up to 700 neurons in slice cultures of mouse somatosensory cortex for 1 hr at a time. We used transfer entropy to quantify directed information transfer (IT) between pairs of neurons. We found an approximately lognormal distribution of firing rates as reported in in-vivo. Pairwise information transfer strengths also were nearly lognormally distributed, similar to synaptic strengths. 20\% of the neurons accounted for 70\% of the total IT coming into, and going out of the network and were defined as rich nodes. These rich nodes were more densely and strongly connected to each other expected by chance, forming a rich club. This highly uneven distribution of IT has implications for the efficiency and robustness of local cortical networks, and gives clues to the plastic processes that shape them. [Preview Abstract] |
Tuesday, March 3, 2015 11:51AM - 12:03PM |
G50.00004: Clique of functional hubs orchestrates population bursts in developmentally regulated neural networks Alessandro Torcini, Stefano Luccioli, Paolo Bonifazi, Eshel Ben-Jacob, Ari Barzilai It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in developing neuronal circuits, typically composed of only excitatory cells, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally regulated constraints on single neuron excitability and connectivity leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate population activity. [Preview Abstract] |
Tuesday, March 3, 2015 12:03PM - 12:15PM |
G50.00005: Neural mechanism to construct a future timeline Karthik Shankar Computing the set of possible future states is an important cognitive feature that aids in planning toward a goal. The brain must perform this computation swiftly, and more importantly without destroying the current state of memory. Here we propose a neural mechanism that periodically modifies the synaptic weights in a mathematically principled way to achieve the construction of the future timeline. Preliminary evidence of synaptic modifications in synchrony with the theta rhythm suggests that this mechanism could take place in the Hippocampus. The hypothesis also predicts that the time cells observed in the Hippocampus should exhibit phase precession with respect to the theta rhythm as the future timeline is cognitively constructed. [Preview Abstract] |
Tuesday, March 3, 2015 12:15PM - 12:27PM |
G50.00006: Local structure of subcellular input retinotopy in an identified visual interneuron Ying Zhu, Fabrizio Gabbiani How does the spatial layout of the projections that a neuron receives impact its synaptic integration and computation? What is the mapping topography of subcellular wiring at the single neuron level? The LGMD (lobula giant movement detector) neuron in the locust is an identified neuron that responds preferentially to objects approaching on a collision course. It receives excitatory inputs from the entire visual hemifield through calcium-permeable nicotinic acetylcholine receptors. Previous work showed that the projection from the locust compound eye to the LGMD preserved retinotopy down to the level of a single ommatidium (facet) by employing in vivo widefield calcium imaging. Because widefield imaging relies on global excitation of the preparation and has a relatively low resolution, previous work could not investigate this retinotopic mapping at the level of individual thin dendritic branches. Our current work employs a custom-built two-photon microscope with sub-micron resolution in conjunction with a single-facet stimulation setup that provides visual stimuli to the single ommatidium of locust adequate to explore the local structure of this retinotopy at a finer level. [Preview Abstract] |
Tuesday, March 3, 2015 12:27PM - 12:39PM |
G50.00007: Nonlinear Dynamic Theory of Acute Cell Injuries and Brain Ischemia Doaa Taha, Fika Anggraini, Donald DeGracia, Zhi-Feng Huang Cerebral ischemia in the form of stroke and cardiac arrest brain damage affect over 1 million people per year in the USA alone. In spite of close to 200 clinical trials and decades of research, there are no treatments to stop post-ischemic neuron death. We have argued that a major weakness of current brain ischemia research is lack of a deductive theoretical framework of acute cell injury to guide empirical studies. A previously published autonomous model based on the concept of nonlinear dynamic network was shown to capture important facets of cell injury, linking the concept of therapeutic to bistable dynamics. Here we present an improved, non-autonomous formulation of the nonlinear dynamic model of cell injury that allows multiple acute injuries over time, thereby allowing simulations of both therapeutic treatment and preconditioning. Our results are connected to the experimental data of gene expression and proteomics of neuron cells. Importantly, this new model may be construed as a novel approach to pharmacodynamics of acute cell injury. The model makes explicit that any pro-survival therapy is always a form of sub-lethal injury. This insight is expected to widely influence treatment of acute injury conditions that have defied successful treatment to date. [Preview Abstract] |
Tuesday, March 3, 2015 12:39PM - 12:51PM |
G50.00008: ABSTRACT WITHDRAWN |
Tuesday, March 3, 2015 12:51PM - 1:03PM |
G50.00009: Interpreting collective neural activity underlying spatial navigation in virtual reality Leenoy Meshulam, Jeff Gauthier, David Tank, William Bialek Traditionally, cognitive- demanding processes like spatial navigation were studied by recording the activity of single neurons. However, recent technological progress allows imaging the simultaneous activity of large neuronal populations in awake behaving animals. This progress in experimental work calls for a similar adjustments of the modeling frameworks. To achieve a description of the ``real thermodynamics'' of the neural system, we construct maximum entropy models for optical imaging data taken \textit{in vivo}, from the hippocampus of mice navigating in a virtual reality environment. This provides a natural extension of statistical mechanics applicable to brain activity, by focusing on the interactions between cells rather than on single cell's activity. We aim to determine how the topology of the energy landscape predicted by the model corresponds to the location of the animal in the environment. Since large subpopulations of the neurons in this area are spatially modulated, we expect the landscape to exhibit a large ``valley'' structure of local minima, corresponding to the animal's position along the environment. Such a finding is especially of interest because the location information emerges solely from the activity patterns that are accessible to the brain. [Preview Abstract] |
Tuesday, March 3, 2015 1:03PM - 1:39PM |
G50.00010: How Worms Eat Invited Speaker: Christopher Fang-Yen The nematode C. elegans feeds by rhythmic contractions of the pharynx, a neuromuscular tube which traps bacteria and transports them to the intestine. The pharynx is innervated by an almost independent nervous system composed of 20 neurons, most of unclear function. First, I will review previous studies using high speed video microscopy to understand how the pharynx filters and transports bacteria. Second, I will describe our efforts to understand the neural basis of feeding behavior using a novel method for optogenetic perturbation of single neurons in an intact, behaving animal. [Preview Abstract] |
Tuesday, March 3, 2015 1:39PM - 1:51PM |
G50.00011: On basins of attraction in attractor neural networks Suchitra Sampath, Vipin Srivastava We present an in-depth study of basin of attraction for patterns of $ \pm 1$ inscribed following Hebbian hypothesis {[1]} in a spin-glass like neural network. The aim is to investigate if basin of attraction being {\it non-zero} is a sufficient condition for the stability of an inscribed state when the necessary condition is that the inscribed state should be retrieved without any error. While this is true for Hopfield model {[1]}, we find that the following model is an exception in that as many as {\it p=N-1} stored patterns ({\it N} being the number of neurons in a fully connected network) can be retrieved without error while their basins of attraction consistently reduce in size as {\it p} increases and become zero around {\it p=0.8N}. The model proposes that the information that comes to be recorded in the brain is first orthogonalized (as in Gram-Schmidt orthogonalization) and then inscribed in synaptic weights. While the orthogonalized versions of input vectors with $ \pm 1$ components are stored in the model brain, the original vectors/patterns are retrieved exactly when checked for retrieval. Simulations are presented that give insight into the energy landscape in the space spanned by the network states.\\[4pt] [1] J.J. Hopfield,{\it PNAS} {\bf 79}, 2554(1982) [Preview Abstract] |
Tuesday, March 3, 2015 1:51PM - 2:03PM |
G50.00012: How to generate a sound-localization map in fish J. Leo van Hemmen How sound localization is represented in the fish brain is a research field largely unbiased by theoretical analysis and computational modeling. Yet, there is experimental evidence that the axes of particle acceleration due to underwater sound are represented through a map in the midbrain of fish, e.g., in the \emph{torus semicircularis} of the rainbow trout (Wubbels et al. 1997). How does such a map arise? Fish perceive pressure gradients by their three otolithic organs, each of which comprises a dense, calcareous, stone that is bathed in endolymph and attached to a sensory epithelium. In rainbow trout, the sensory epithelia of left and right utricle lie in the horizontal plane and consist of hair cells with equally distributed preferred orientations. We model the neuronal response of this system on the basis of Schuijf's vector detection hypothesis (Schuijf et al. 1975) and introduce a temporal spike code of sound direction, where optimality of hair cell orientation $\theta_j$ with respect to the acceleration direction $\theta_s$ is mapped onto spike phases via a von-Mises distribution. By learning to tune in to the earliest synchronized activity, nerve cells in the midbrain generate a map under the supervision of a locally excitatory, yet globally inhibitory visual teacher. [Preview Abstract] |
Tuesday, March 3, 2015 2:03PM - 2:15PM |
G50.00013: The neural circuit and synaptic dynamics underlying perceptual decision-making Feng Liu Decision-making with several choice options is central to cognition. To elucidate the neural mechanisms of multiple-choice motion discrimination, we built a continuous recurrent network model to represent a local circuit in the lateral intraparietal area (LIP). The network is composed of pyramidal cells and interneurons, which are directionally tuned. All neurons are reciprocally connected, and the synaptic connectivity strength is heterogeneous. Specifically, we assume two types of inhibitory connectivity to pyramidal cells: opposite-feature and similar-feature inhibition. The model accounted for both physiological and behavioral data from monkey experiments. The network is endowed with slow excitatory reverberation, which subserves the buildup and maintenance of persistent neural activity, and predominant feedback inhibition, which underlies the winner-take-all competition and attractor dynamics. The opposite-feature and opposite-feature inhibition have different effects on decision-making, and only their combination allows for a categorical choice among 12 alternatives. Together, our work highlights the importance of structured synaptic inhibition in multiple-choice decision-making processes. Reference: Cheng Xue and Feng Liu. J. Neurosci. 34, 13444-13457 (2014). [Preview Abstract] |
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