Bulletin of the American Physical Society
APS March Meeting 2017
Volume 62, Number 4
Monday–Friday, March 13–17, 2017; New Orleans, Louisiana
Session R5: Physics of Neural Systems |
Hide Abstracts |
Sponsoring Units: DBIO Chair: David Hofmann, Emory University Room: 264 |
Thursday, March 16, 2017 8:00AM - 8:12AM |
R5.00001: The impact of multiple noise sources on maximally informative adaptive dynamics in neural populations Wei-Mien Mendy Hsu, David Kastner, Stephen Baccus, Tatyana Sharpee Sensory neural populations are thought to be optimized to transmit information about the sensory environment either in the course of evolution in the dynamically to adapt neural responses to the current stimulus environment. Recent work has shown that when encoding a particular stimulus feature, the existence of multiple neuronal types with different thresholds increases information transmission when sensory noise drops below a certain level. This prediction across an evolutionary timescale simultaneously explains the existence of adapting and sensitizing Off retinal ganglion cells, which have high and low thresholds for spiking, respectively, as well as the absence of comparable types among On that have higher effective noise level. However, the difference in thresholds between adapting and sensitizing cells is systematically lower than the one that would yield maximal information in an environment of stationary contrast. Here we show how predictions for the optimal threshold difference change when the overall sensory noise is treated as the combination of multiple noise sources separated by nonlinear steps. Analyzing responses of adapting and sensitizing ganglion cells types we identify an additional noise source that affects adapting but not sensitizing ganglion cells. [Preview Abstract] |
Thursday, March 16, 2017 8:12AM - 8:24AM |
R5.00002: Experimental and theoretical cross-species analysis of coupled voltage and calcium dynamics in paced cardiac tissue Conner Herndon, Ilija Uzelac, Flavio Fenton Much theoretical, experimental, and clinical research has been devoted to investigating the initiation of cardiac arrhythmias by alternans, a beat-to-beat variation in the duration of cardiac action potentials produced by a period two bifurcation. Alternans results from a cellular level instability in the bidirectionally coupled dynamics of transmembrane voltage and intracellular calcium concentration. We performed simultaneous recordings of voltage and calcium signals using optical mapping on Langendorff perfused hearts of several species at a spatial resolution of 128x128 pixels and time resolution of 500Hz. In this talk we present experimental results detailing the mechanisms underlying the onset of alternans in zebrafish, rabbit, cat, pig, and alligator. Furthermore we provide a theoretical and computational model of alternans that more generally accounts for cross-species variation. [Preview Abstract] |
Thursday, March 16, 2017 8:24AM - 8:36AM |
R5.00003: Abstract Withdrawn
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Thursday, March 16, 2017 8:36AM - 8:48AM |
R5.00004: NeuroPhysics: Studying how neurons create the perception of space-time using Physics' tools and techniques Shonali Dhingra, Roman Sandler, Rodrigo Rios, Cliff Vuong, Mayank Mehta All animals naturally perceive the abstract concept of space-time. A brain region called the Hippocampus is known to be important in creating these perceptions, but the underlying mechanisms are unknown. In our lab we employ several experimental and computational techniques from Physics to tackle this fundamental puzzle. Experimentally, we use ideas from Nanoscience and Materials Science to develop techniques to measure the activity of hippocampal neurons, in freely-behaving animals. Computationally, we develop models to study neuronal activity patterns, which are point processes that are highly stochastic and multidimensional. We then apply these techniques to collect and analyze neuronal signals from rodents while they're exploring space in Real World or Virtual Reality with various stimuli. Our findings show that under these conditions neuronal activity depends on various parameters, such as sensory cues including visual and auditory, and behavioral cues including, linear and angular, position and velocity. Further, neuronal networks create internally-generated rhythms, which influence perception of space and time. In totality, these results further our understanding of how the brain develops a cognitive map of our surrounding space, and keep track of time. [Preview Abstract] |
Thursday, March 16, 2017 8:48AM - 9:00AM |
R5.00005: Tympanic-response transition in ICE: Dependence upon the interaural cavity's shape J. Leo van Hemmen More than half of the terrestrial vertebrates have internally coupled ears (ICE), where an interaural cavity of some shape acoustically couples the eardrums. Hence what the animal's auditory system perceives is not the outside stimulus but the superposition of outside and internal pressure on the two eardrums, resulting in so-called internal time and level difference, iTD and iLD, which are keys to sound localization. For a cylindrical shape, it is known that on the frequency axis two domains with appreciably increased iTD and iLD values occur, segregated by the eardrum's fundamental frequency. Here we analyze the case where, as in nature, two or more canals couple the eardrums so that, by opening one of the canals, the animal can switch from coupled to two independent ears. We analyze the iTD/iLD transition and its dependence upon the interaural cavity's size and shape. As compared to a single connection, the iTD performance is preserved to a large extent. Nonetheless, the price to pay for freedom of choice is a reduced frequency range with high-iTD plateau. [Preview Abstract] |
Thursday, March 16, 2017 9:00AM - 9:12AM |
R5.00006: Interictal to Ictal Phase Transition in a Small-World Network Louis Nemzer, Gary Cravens, Robert Worth Real-time detection and prediction of seizures in patients with epilepsy is essential for rapid intervention. Here, we perform a full Hodgkin-Huxley calculation using n $\approx $ 50 \textit{in silico} neurons configured in a small-world network topology to generate simulated EEG signals. The connectivity matrix, constructed using a Watts--Strogatz algorithm, admits randomized or deterministic entries. We find that situations corresponding to interictal (non-seizure) and ictal (seizure) states are separated by a phase transition that can be influenced by congenital channelopathies, anticonvulsant drugs, and connectome plasticity. The interictal phase exhibits scale-free phenomena, as characterized by a power law form of the spectral power density, while the ictal state suffers from pathological synchronization. We compare the results with intracranial EEG data and show how these findings may be used to detect or even predict seizure onset. Along with the balance of excitatory and inhibitory factors, the network topology plays a large role in determining the overall characteristics of brain activity. We have developed a new platform for testing the conditions that contribute to the phase transition between non-seizure and seizure states. [Preview Abstract] |
Thursday, March 16, 2017 9:12AM - 9:24AM |
R5.00007: A discrete structure of the brain waves. Yuri Dabaghian, Luca Perotti A physiological interpretation of the biological rhythms, e.g., of the local field potentials (LFP) depends on the mathematical approaches used for the analysis. Most existing mathematical methods are based on decomposing the signal into a set of ``primitives,'' e.g., sinusoidal harmonics, and correlating them with different cognitive and behavioral phenomena. A common feature of all these methods is that the decomposition semantics is presumed from the onset, and the goal of the subsequent analysis reduces merely to identifying the combination that best reproduces the original signal. We propose a fundamentally new method in which the decomposition components are discovered empirically, and demonstrate that it is more flexible and more sensitive to the signal's structure than the standard Fourier method. Applying this method to the rodent LFP signals reveals a fundamentally new structure of these ``brain waves.'' In particular, our results suggest that the LFP oscillations consist of a superposition of a small, discrete set of frequency modulated oscillatory processes, which we call ``oscillons''. Since these structures are discovered empirically, we hypothesize that they may capture the signal's actual physical structure, i.e., the pattern of synchronous activity in neuronal ensembles. Proving this hypothesis will help to advance our principal understanding of the neuronal synchronization mechanisms and reveal new structure within the LFPs and other biological oscillations. [Preview Abstract] |
Thursday, March 16, 2017 9:24AM - 9:36AM |
R5.00008: Cortical Network Analysis from Retrograde Tracing Experiments Daniel Barabasi, Melinda Varga, Zoltan Toroczkai Updated efforts using hemisphere-wide retrograde and anterograde tracing have provided large-scale physical connectivity data about the architecture of the cortex in both macaque and the mouse. Recent studies of these data have reported high-density cortico-cortical wiring, which renders earlier methods designed for sparse network analysis, in particular for network community detection, less applicable. Using novel methods better suitable for dense graphs but also more traditional spectral methods, we show the existence of a strong core-periphery structure in the cortical interareal networks of these mammals, and perform a comparative analysis of these networks. We also show that the Exponential Distance Rule (EDR) framework for mammalian connectivity, according to which one expects dense short-range connectivity with sparsely distributed long-range edges, captures the observed core-periphery structure in both species. When compared to appropriate random graph null models, our analysis shows a clear distinction between the null model and brain data, implying the existence of high connection specificity in both brains, but with stronger specificity in the primate, when compared to the rodent brain. [Preview Abstract] |
Thursday, March 16, 2017 9:36AM - 9:48AM |
R5.00009: Bio-wave change photo-voltages of the solar cells at same changed rate by probability effect of spacetime structure Dayong Cao In our experiment, when light (of ``lamp LED'' 3W, 20cm away from the solar cells) simultaneous radiated on four solar cells, they would produce their photo-voltages which are called as background photo-voltages. And then, the author used thought wave to remotely (wireless) act on the four solar cells and increase four background photo-voltages at the same rates which is about 64{\%}. After that, Adding the other light (of ``lamp CFL'') to simultaneous radiate on the four solar cells to changed their background photo-voltages. But there are different changed rates which will appear in the general experiments because the luminous sensitivities of the solar cell are different and the photo-voltages is a nonlinear function. The probability effects of the spacetime structure (of Confined Structural non-Newtonian Fluids) of brain wave (because the wave is spacetime) to change a balance structure between Electron Clouds and electron holes of P-N Junction, and change the background photo-voltages of the solar cells. In the experiments, the consciousness effect, and the relationship between brain wave and consciousness effect will be considered. After the decade of the brain research and the ``BRAIN'' Initiative, a decade of the consciousness need be taken. http://meetings.aps.org/Meeting/APR16/Session/M13.8 [Preview Abstract] |
Thursday, March 16, 2017 9:48AM - 10:00AM |
R5.00010: Topological Principles of Control in Dynamical Networks Jason Kim, Fabio Pasqualetti, Danielle Bassett Networked biological systems, such as the brain, feature complex patterns of interactions. To predict and correct the dynamic behavior of such systems, it is imperative to understand how the underlying topological structure affects and limits the function of the system. Here, we use network control theory to extract topological features that favor or prevent network controllability, and to understand the network-wide effect of external stimuli on large-scale brain systems. Specifically, we treat each brain region as a dynamic entity with real-valued state, and model the time evolution of all interconnected regions using linear, time-invariant dynamics. We propose a simplified feed-forward scheme where the effect of upstream regions (drivers) on the connected downstream regions (non-drivers) is characterized in closed-form. Leveraging this characterization of the simplified model, we derive topological features that predict the controllability properties of non-simplified networks. We show analytically and numerically that these predictors are accurate across a large range of parameters. Among other contributions, our analysis shows that heterogeneity in the network weights facilitate controllability, and allows us to implement targeted interventions that profoundly improve controllability. By assuming an underlying dynamical mechanism, we are able to understand the complex topology of networked biological systems in a functionally meaningful way. [Preview Abstract] |
Thursday, March 16, 2017 10:00AM - 10:12AM |
R5.00011: Fitness landscape complexity and the emergence of modularity in neural networks Jessica Lowell Previous research has shown that the shape of the fitness landscape can affect the evolution of modularity. We evolved neural networks to solve different tasks with different fitness landscapes, using NEAT, a popular neuroevolution algorithm that quantifies similarity between genomes in order to divide them into species. We used this speciation mechanism as a means to examine fitness landscape complexity, and to examine connections between fitness landscape complexity and the emergence of modularity. [Preview Abstract] |
Thursday, March 16, 2017 10:12AM - 10:24AM |
R5.00012: Coordination of size-control, reproduction and generational memory in freshwater planarians Xingbo Yang, Kelson Kaj, David Schwab, Eva-Maria Collins Uncovering the mechanisms that control size, growth, and division rates of systems reproducing through binary division means understanding basic principles of their life cycle. Recent work has focused on how division rates are regulated in bacteria and yeast, but this question has not yet been addressed in more complex, multicellular organisms. We have acquired a unique large-scale data set on the growth and asexual reproduction of two freshwater planarian species, Dugesia japonica and Dugesia tigrina, which reproduce by transverse fission and succeeding regeneration of head and tail pieces into new worms. We developed a new additive theoretical model that mixes multiple size control strategies based on worm size, growth, and waiting time. Our model quantifies the proportions of each strategy in the mixed dynamics, revealing the ability of the two planarian species to utilize different strategies in a coordinated manner for size control. Additionally, we found that head and tail offspring of both species employ different mechanisms to monitor and trigger their reproduction cycles. Finally, we show that generation-dependent memory effects in planarians need to be taken into account to accurately capture the experimental data. [Preview Abstract] |
Thursday, March 16, 2017 10:24AM - 10:36AM |
R5.00013: Abstract Withdrawn
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Thursday, March 16, 2017 10:36AM - 10:48AM |
R5.00014: Variance adaptation in navigational decision making Marc Gershow, Ruben Gepner, Jason Wolk, Digvijay Wadekar {\it Drosophila} larvae navigate their environments using a biased random walk strategy. A key component of this strategy is the decision to initiate a turn (change direction) in response to declining conditions. We modeled this decision as the output of a Linear-Nonlinear-Poisson cascade and used reverse correlation with visual and fictive olfactory stimuli to find the parameters of this model\footnote{Gepner, R., et al., eLife Sciences 4, e06229 (2015)}. Because the larva responds to changes in stimulus intensity, we used stimuli with uncorrelated normally distributed intensity derivatives, i.e. Brownian processes, and took the stimulus derivative as the input to our LNP cascade. In this way, we were able to present stimuli with 0 mean and controlled variance. We found that the nonlinear rate function depended on the variance in the stimulus input, allowing larvae to respond more strongly to small changes in low-noise compared to high-noise environments. We measured the rate at which the larva adapted its behavior following changes in stimulus variance, and found that larvae adapted more quickly to increases in variance than to decreases, consistent with the behavior of an optimal Bayes estimator\footnote{DeWeese, M. \& Zador, A. Neural Computation 10, 1179–1202 (1998)}. [Preview Abstract] |
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