Bulletin of the American Physical Society
APS March Meeting 2014
Volume 59, Number 1
Monday–Friday, March 3–7, 2014; Denver, Colorado
Session A15: Focus Session: Physics of Sensory Neuroscience |
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Sponsoring Units: DBIO Room: 304 |
Monday, March 3, 2014 8:00AM - 8:36AM |
A15.00001: Space, Time, Neural Oscillations and Memory Invited Speaker: Mayank R. Mehta All animals move in space and keep track of time. Hence, they must have a clear percept of space-time. Unlike many sensory processing, space-time is abstract concepts because they can neither be directly felt nor readily controlled. How does the brain, or the ensemble of neurons, create a perception of space-time? This has puzzled scientists for a long time. Research in the past few decades have revealed that individual neurons in a few key brain regions, especially the hippocampus and entorhinal cortex, fire selectively as a function of the subject’s position in space and as a function of elapsed time. In fact, the spatial activity pattern of specific neurons forms a hexagonal lattice. The biophysical mechanisms governing the neural maps of space and time have remained elusive. A primary difficulty has been that when animals walk in the real world, stimuli from various modalities, e.g. sound, light, smell, texture etc., all change at the same time and these changes are difficult to measure, let alone control, precisely. Hence, we have developed a noninvasive, immersive and multisensory virtual reality system where the hardware and software transform the movements of a rat into the evolution of complex stimuli surrounding him to form an audio-visual space. Using this apparatus we have measured the activities of thousands of individual neurons. We then developed analysis techniques to decipher the spatio-temporal activity patterns buried in these neural ensembles, and related the emergent neural dynamics to spatial behavior in the virtual world. Finally we have developed computational models that can capture the emergent neural dynamics to reveal the biophysical mechanisms governing the emergent neural dynamics. This has revealed surprising findings which I will discuss. Specifically, we find that neural oscillations are crucial for perceiving space-time. Further, just as in physical world, spatial representation is relative not absolute. These findings open up unprecedented experimental and theoretical avenues for understanding how neural ensembles perceive space-time and guide complex behavior. [Preview Abstract] |
Monday, March 3, 2014 8:36AM - 8:48AM |
A15.00002: Transformation of stimulus correlations by the retina Jason Prentice, Kristina Simmons, Gasper Tkacik, Jan Homann, Heather Yee, Stephanie Palmer, Phillip Nelson, Vijay Balasubramanian Correlations in the responses of sensory neurons seem to waste neural resources, but can carry cues about structured stimuli and help the brain correct for response errors. To assess how the retina negotiates this tradeoff, we measured simultaneous responses from many retinal ganglion cells presented with natural and artificial stimuli that varied in correlation structure. Responding to spatio-temporally structured stimuli such as natural movies, pairs of ganglion cells were more correlated than in response to white noise checkerboards, but were much less correlated than predicted by a non-adapting functional model of retinal response. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. We found that in response to spatio- temporally correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the pattern of correlations across stimuli. [Preview Abstract] |
Monday, March 3, 2014 8:48AM - 9:00AM |
A15.00003: Dynamics of modularity of neural activity in the brain during development Michael Deem, Man Chen Theory suggests that more modular systems can have better response functions at short times. This theory suggests that greater cognitive performance may be achieved for more modular neural activity, and that modularity of neural activity may, therefore, likely increase with development in children. We study the relationship between age and modularity of brain neural activity in developing children. The value of modularity calculated from fMRI data is observed to increase during childhood development and peak in young adulthood. We interpret these results as evidence of selection for plasticity in the cognitive function of the human brain. We present a model to illustrate how modularity can provide greater cognitive performance at short times and enhance fast, low-level, automatic cognitive processes. Conversely, high-level, effortful, conscious cognitive processes may not benefit from modularity. We use quasispecies theory to predict how the average modularity evolves with age, given a fitness function extracted from the model. We suggest further experiments exploring the effect of modularity on cognitive performance and suggest that modularity may be a potential biomarker for injury, rehabilitation, or disease. [Preview Abstract] |
Monday, March 3, 2014 9:00AM - 9:36AM |
A15.00004: Sensory Coding in Oscillatory Peripheral Receptors Invited Speaker: Alexander Neiman Rhythmical activity have been observed in several types of peripheral sensory receptors, e.g. in senses of hearing, balance and electroreception. We use two examples of spontaneously oscillating peripheral sensory receptors: bullfrog saccular hair cells and electroreceptors of paddlefish, to discuss how oscillations emerge, how these sensors may utilize oscillations to optimize their sensitivity and information processing. In the hair cell system oscillations occur on two very different levels: first, the mechano-sensory hair bundle itself can undergo spontaneous mechanical oscillations and second, self-sustained voltage oscillations across the membrane of the hair cell have been documented. Modelling show that interaction of these two compartment results in enhanced sensitivity to periodic mechanical stimuli. The second example, a single peripheral electroreceptor, is a complex system comprised of several thousands of sensory epithelial cells innervated by a few primary sensory neurons. It embeds two distinct oscillators: one residing in a population of epithelial cells, synaptically coupled to another oscillator residing in a branched myelinated afferent axon. We show how neuronal oscillations emerge in a complex network of excitable nodes. We further demonstrate that epithelial oscillations results in extended serial correlations of neruonal discharges enhancing coding of external stimuli. [Preview Abstract] |
Monday, March 3, 2014 9:36AM - 9:48AM |
A15.00005: Chimera States in Neural Oscillators Sonya Bahar, Tera Glaze Chimera states have recently been explored both theoretically and experimentally, in various coupled nonlinear oscillators, ranging from phase-oscillator models to coupled chemical reactions. In a chimera state, both coherent and incoherent (or synchronized and desynchronized) states occur simultaneously in populations of identical oscillators. We investigate chimera behavior in a population of neural oscillators using the Huber-Braun model, a Hodgkin-Huxley-like model originally developed to characterize the temperature-dependent bursting behavior of mammalian cold receptors. One population of neurons is allowed to synchronize, with each neuron receiving input from all the others in its group (global within-group coupling). Subsequently, a second population of identical neurons is placed under an identical global within-group coupling, and the two populations are also coupled to each other (between-group coupling). For certain values of the coupling constants, the neurons in the two populations exhibit radically different synchronization behavior. We will discuss the range of chimera activity in the model, and discuss its implications for actual neural activity, such as unihemispheric sleep. [Preview Abstract] |
Monday, March 3, 2014 9:48AM - 10:00AM |
A15.00006: Why Internally Coupled Ears (ICE) Work Well J. Leo van Hemmen Many vertebrates, such as frogs and lizards, have an air-filled cavity between left and right eardrum, i.e., internally coupled ears (ICE). Depending on source direction, internal time (iTD) and level (iLD) difference as experienced by the animal's auditory system may greatly exceed [C. Vossen et al., JASA 128 (2010) 909--918] the external, or interaural, time and level difference (ITD and ILD). Sensory processing only encodes iTD and iLD. We present an extension of ICE theory so as to elucidate the underlying physics. First, the membrane properties of the eardrum explain why for low frequencies iTD dominates whereas iLD does so for higher frequencies. Second, the plateau of iTD $=\gamma$ ITD for constant $1 < \gamma < 5$ and variable input frequency $< \nu_{\circ}$ follows; e.g., for the Tockay gecko $\nu_{\circ} \approx 1.5$ kHz. Third, we use a sectorial instead of circular membrane to quantify the effect of the extracolumella embedded in the tympanum and connecting with the cochlea. The main parameters can be adjusted so that the model is species independent. [Preview Abstract] |
Monday, March 3, 2014 10:00AM - 10:36AM |
A15.00007: Using the structure of natural scenes and sounds to predict neural response properties in the brain Invited Speaker: Michael DeWeese The natural scenes and sounds we encounter in the world are highly structured. The fact that animals and humans are so efficient at processing these sensory signals compared with the latest algorithms running on the fastest modern computers suggests that our brains can exploit this structure. We have developed a sparse mathematical representation of speech that minimizes the number of active model neurons needed to represent typical speech sounds. The model learns several well-known acoustic features of speech such as harmonic stacks, formants, onsets and terminations, but we also find more exotic structures in the spectrogra representation of sound such as localized checkerboard patterns and frequency-modulated excitatory subregions flanked by suppressive sidebands. Moreover, several of these novel features resemble neuronal receptive fields reported in the Inferior Colliculus (IC), as well as auditory thalamus (MGBv) and primary auditory cortex (A1), and our model neurons exhibit the same tradeoff in spectrotemporal resolution as has been observed in IC. To our knowledge, this is the first demonstration that receptive fields of neurons in the ascending mammalian auditory pathway beyond the auditory nerve can be predicted based on coding principles and the statistical properties of recorded sounds. We have also developed a biologically-inspired neural network model of primary visual cortex (V1) that can learn a sparse representation of natural scenes using spiking neurons and strictly local plasticity rules. The representation learned by our model is in good agreement with measured receptive fields in V1, demonstrating that sparse sensory coding can be achieved in a realistic biological setting. [Preview Abstract] |
Monday, March 3, 2014 10:36AM - 10:48AM |
A15.00008: Microstate description of stable chaos in networks of spiking neurons Maximilian Puelma Touzel, Monteforte Michael, Fred Wolf Dynamic instabilities have been proposed to explain the decorrelation of stimulus-driven activity observed in sensory areas such as the olfactory bulb, but are sensitive to noise. Simple neuron models coupled through inhibition can nevertheless exhibit a negative maximum Lyapunov exponent, despite displaying irregular and asynchronous (AI) activity and having an exponential instability to finite-sized perturbations above a critical strength that scales with the size, density and activity of the circuit [1]. This stable chaos, a phenomenon first found in coupled-map lattices, produces a large, finite set of locally-attracting, yet mutually-repelling AI spike sequences ideally suited for discrete, high-dimensional coding. We analyze the effects of finite-sized perturbations on the spiking microstate and reveal the mechanism underlying the stable chaos. From this, we can analytically derive the aforementioned scaling relations and estimate the critical value of previously observed transitions to conventional chaos. This work highlights the features of intra-neuron dynamics and inter-neuron coupling that generate this phase space structure, which might serve as an attractor reservoir that downstream networks can use to decode sensory input.\\[4pt] [1] Monteforte, M. \& Wolf, F., PRX 2, 1(2012). [Preview Abstract] |
Monday, March 3, 2014 10:48AM - 11:00AM |
A15.00009: ERP Energy and Cognitive Activity Correlates Michael Jay Schillaci, Jennifer M.C. Vendemia We propose a novel analysis approach for high-density event related scalp potential (ERP) data where the integrated channel-power is used to attain an energy density functional state for channel-clusters of neurophysiological significance. The method is applied to data recorded during a two-stimulus, directed lie paradigm and shows that deceptive responses emit between 8\% and 10\% less power. A time course analysis of these cognitive activity measures over posterior and anterior regions of the cortex suggests that neocortical interactions, reflecting the differing workload demands during executive and semantic processes, take about 50\% longer for the case of deception. These results suggest that the proposed method may provide a useful tool for the analysis of ERP correlates of high-order cognitive functioning. We also report on a possible equivalence between the energy functional distribution and near-infrared signatures that have been measured with other modalities. [Preview Abstract] |
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