Bulletin of the American Physical Society
APS March Meeting 2010
Volume 55, Number 2
Monday–Friday, March 15–19, 2010; Portland, Oregon
Session V10: Focus Session: Dynamics of Neural Systems |
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Sponsoring Units: DBP Chair: Rhonda Dzakpasu, Georgetown University Room: A106 |
Thursday, March 18, 2010 8:00AM - 8:12AM |
V10.00001: Altering the Balance between Excitation and Inhibition in Cultured Neural Networks Rhonda Dzakpasu How is the network temporal structure altered when the balance between excitation and inhibition is changed? Proper balance is essential for normal brain function, including cognitive processing, the representation of sensory information and motor control. When the balance is compromised, neurological disorders may result. We use a simple reduced experimental system to investigate how manipulating the number of inhibitory neurons in a network of cultured hippocampal neurons affects synchronized bursting activity, the most prominent temporal signature of cultured hippocampal networks. Inhibitory neurons are thought to control spike timing and modulate network excitability and their absence may lead to widespread synchronization. We culture dissociated hippocampal neurons with varying quantities of inhibitory neurons on an 8x8 grid of extracellular electrodes and study how inhibitory neurons modulate network temporal dynamics. We show that as the proportion of inhibitory neurons increase, there is a dramatic transition in the temporal pattern. [Preview Abstract] |
Thursday, March 18, 2010 8:12AM - 8:24AM |
V10.00002: Dynamic Self-Regulation of Neural Impedance Joseph Tranquillo, Dan Medani One computationally important feature of a neuronal network is the ability of each neuron to dynamically adjust its impedance to external signals. We simulated synaptically coupled Hindmarsh-Rose neurons and found that a bursting neuron can switch a neural oscillator into a temporary period of quiescence, before switching it back to native oscillatory behavior. In quiescent mode, the oscillator did not synchronize to its inputs. In oscillation mode, the neuron did synchronize to its inputs, allowing information transfer. Inhibitory synapses were found to be more effective than excitatory synapses and switching was relatively insensitive to delays, driving frequency or Gaussian noise added to the input. In phase space an unstable limit cycle separates the quiescent and oscillatory modes and the switch is achieve through fold limit cycle and sub-critical Andronov-Hopf bifurcations. While the fast subsystem controls the limit cycle, the size of the unstable limit cycle switch is governed by a slow variable. This separation of time scales allows a neuron to self-regulate input impedance on a slow time scale, while at the same time enabling information to propagate through a larger network on a fast time scale. [Preview Abstract] |
Thursday, March 18, 2010 8:24AM - 8:36AM |
V10.00003: Input-dependent Suppression of Chaos in Recurrent Neural Networks K. Rajan, L.F. Abbott, H. Sompolinsky Neuronal responses arise from an interaction between spontaneous activity and responses driven by external inputs. Experiments studying cortical circuits reveal a striking similarity between the magnitude and complexity of intrinsic and input-generated activity. How does a network generating complex activity remain sensitive to external inputs? This seems unlikely for a network in which input-driven responses add linearly to ongoing activity generated by stochastic noise generators. We developed a mean-field theory and used recurrent network models to distinguish between this type of external noise and chaotic background generated by strong coupling within the circuit. As a result of a highly nonlinear relationship between input- and internally generated activity, we show that intrinsic \textbf{\textit{noise}} is sensitive to the amplitude and the spatiotemporal structure of the input. We find that input not only drives responses, it also actively suppresses spontaneous activity, leading to a phase transition in which the chaotic background is absent. Although the power spectrum of the spontaneous activity falls exponentially from zero, the phase transition reveals a \textbf{\textit{resonant}} frequency at which relatively a weak input suppresses chaos. As long as the input drives the system across the phase transition, a spontaneously active network can work with coupling strong enough to allow large signal amplification and selectivity without the complex background interfering with sensory processing. [Preview Abstract] |
Thursday, March 18, 2010 8:36AM - 8:48AM |
V10.00004: Improved spike sorting for multi-electrode array data from mammalian retina Jason Prentice, Jan Homann, Kristy Simmons, Gasper Tkacik, Vijay Balasubramanian, Philip Nelson Multi-electrode array technology provides an efficient means of simultaneously recording from many neurons. However, as arrays become larger, a greater computational burden falls on the spike-sorting algorithm. We have developed a new method for sorting multi-electrode signals and applied it to retinal ganglion cells. Our method is explicitly designed to scale well with increasing array size. It can dissect temporally overlapping spikes and accommodate the amplitude variation seen in spike bursts. The broad outline of our method is to (1) identify spikes in the raw data, cluster a subset, generate template waveforms, then (2) fit the templates to all the data using an iterative Bayesian algorithm. Each of these two steps makes use of the 2D spatial arrangement of the ganglion cells and electrodes, and the locality of signals from each individual cell. We demonstrate the method on data recorded from guinea pig retina on a 30-electrode array. [Preview Abstract] |
Thursday, March 18, 2010 8:48AM - 9:00AM |
V10.00005: The emergence of motion-processing circuits in the visual cortex Audrey Sederberg, Matthias Kaschube Direction selectivity in the visual cortex is a paradigm for understanding the dynamics underlying learning in neural circuits. Experimental work has shown that neurons can become selective for a given direction of motion after a few hours of training with a bidirectionally moving stimulus. Here we show that this property naturally arises in models based on Hebbian synaptic plasticity if cortical neurons inhibit each other sufficiently. Specifically, we analyze a model of synaptic dynamics defined by a learning rule based on simple pre- and post-synaptic firing rate correlations. We also adjust the level of inhibitory inputs; these have the same structure as excitatory inputs, but lag by a constant phase. When inhibition is slightly stronger than excitation, we find stable, selective states. Previous work has focused on spike-time dependent plasticity and has needed a learning threshold to prevent a trained cell from reverting to its non-selective state. We find that neither spike-time dependent plasticity nor a learning threshold is required, but inhibition is necessary for strong direction selectivity. [Preview Abstract] |
Thursday, March 18, 2010 9:00AM - 9:12AM |
V10.00006: Enhancement of neural response by diversity Toni Perez, Claudio Mirasso, Raul Toral, James D. Gunton Synchronization between the constituents of an ensemble is common in Nature. This global behavior can originate from a common response to an external stimulus or might appear in au- tonomous systems. Recent studies indicates that diversity among the constituents might play a positive role in setting a common behevior [1]. In this work we focus on the role of diversity in di?erent neurons models such as the Fitzhugh-Nagumo and Morris-Lecar models. We have ob- served that under certain conditions diversity can enhance the response of the system to an external periodic modulation. We have also found that the number of coupled units become fundamental in the enhancement of the response of the system. This results suggest that diversity present in biological systems may have an important role in order to enhance the response of the system to weak signals.\\[4pt] [1] C.J. Tessone, C.R. Mirasso, R. Toral and J.D. Gunton, Phys. Rev. Lett. 97, 194101 (2006) [Preview Abstract] |
Thursday, March 18, 2010 9:12AM - 9:48AM |
V10.00007: Chips of Hope: Neuro-Electronic Hybrids for Brain Repair Invited Speaker: The field of Neuro-Electronic Hybrids kicked off 30 years ago when researchers in the US first tweaked the technology of recording and stimulation of networks of live neurons grown in a Petri dish and interfaced with a computer via an array of electrodes. Since then, many researchers have searched for ways to imprint in neural networks new ``memories'' without erasing old ones. I will describe our new generation of Neuro-Electronic Hybrids and how we succeeded to turn them into the first learning Neurochips - memory and information processing chips made of live neurons. To imprint multiple memories in our new chip we used chemical stimulation at specific locations that were selected by analyzing the networks activity in real time according to our new information encoding principle. Currently we develop new-generation of neuro chips using special carbon nano tubes (CNT). These electrodes enable to engineer the networks topology and efficient electrical interfacing with the neurons. This advance bears the promise to pave the way for building a new experimental platform for testing new drugs and developing new methods for neural networks repair and regeneration. Looking into the future, the development brings us a step closer towards the dream of Brain Repair by implementable Neuro-Electronic hybrid chips. [Preview Abstract] |
Thursday, March 18, 2010 9:48AM - 10:00AM |
V10.00008: Lateral-Line Detection of Underwater Objects: From Goldfish to Submarines J. Leo van Hemmen Fish and some aquatic amphibians use their mechanosensory lateral-line system to navigate by means of hydrodynamic cues. How a fish determines an object's position and shape only through the lateral-line system and the ensuing neuronal processing is still a challenging problem. Our studies have shown that both stimulus position and stimulus form can be determined within the range of about one fish length and are encoded through the response of the afferent nerves originating from the detectors. A minimal detection model of a vibrating sphere (a dipole) has now been extended to other stimuli such as translating spheres, ellipsoids, or even wakes (vortex rings). The theoretical model is fully verified by experimental data. We have also constructed an underwater robot with an artificial lateral-line system designed to detect e.g.\ the presence of walls by measuring the change of water flow around the body. We will show how a simple model fits experimental results obtained from trout and goldfish and how a submarine may well be able to detect underwater objects by using an artificial lateral-line system. [Preview Abstract] |
Thursday, March 18, 2010 10:00AM - 10:12AM |
V10.00009: Increased Accuracy of Ligand Sensing by Receptor Internalization and Lateral Receptor Diffusion Gerardo Aquino, Robert Endres Many types of cells can sense external ligand concentrations with cell-surface receptors at extremely high accuracy. Interestingly, ligand-bound receptors are often internalized, a process also known as receptor-mediated endocytosis. While internalization is involved in a vast number of important functions for the life of a cell, it was recently also suggested to increase the accuracy of sensing ligand as overcounting of the same ligand molecules is reduced. A similar role may be played by receptor diffusion om the cell membrane. Fast, lateral receptor diffusion is known to be relevant in neurotransmission initiated by release of neurotransmitter glutamate in the synaptic cleft between neurons. By binding ligand and removal by diffusion from the region of release of the neurotransmitter, diffusing receptors can be reasonably expected to reduce the local overcounting of the same ligand molecules in the region of signaling. By extending simple ligand-receptor models to out-of-equilibrium thermodynamics, we show that both receptor internalization and lateral diffusion increase the accuracy with which cells can measure ligand concentrations in the external environment. We confirm this with our model and give quantitative predictions for experimental parameters values. We give quantitative predictions, which compare favorably to experimental data of real receptors. [Preview Abstract] |
Thursday, March 18, 2010 10:12AM - 10:24AM |
V10.00010: Molecular kinetics and axonal Morphodynamics Yinyun Li, Chen Ying, Peter Jung, Anthony Brown Caliber is an important feature of axons which is exquisitely tuned to its electrophysiologic function. Although there is a large variety of axonal calibers (even within one cell), the regulatory mechanisms giving rise to these shapes are not understood. Mechanical integrity of the mature neuronal axon is provided by neurofilaments (NF). The local number of NFs determines axonal caliber. Their net transport at the average slow rate of about 0.5 mm/day is characterized by bursts of movement and extended pauses. Our main hypothesis is that the influx and kinetics of NFs are the determinants of axonal caliber and overall morpholology. We use a well-tested mathematical model for the molecular kinetics of NFs to generate hypotheses how these kinetics are modified along the axon in order to generate observed distributions of NFs. Our experimental model system is the mouse optic nerve as the axons therein are unbranched, and detailed experimental data for the NF distribution along the axon and their overall kinetics obtained through radio-isotopic pulse labeling are available. [Preview Abstract] |
Thursday, March 18, 2010 10:24AM - 11:00AM |
V10.00011: Exploring neural code in natural environments Invited Speaker: Neurons communicate by means of stereotyped pulses, called action potentials or spikes, and a central issue in systems neuroscience is to understand this neural coding. We study how sensory information is encoded in sequences of spikes, using motion detection in the blowfly as a model system. To emphasize the importance of the environment, and specifically the statistics of its dynamics, on shaping the animal's response, we perform experiments in an environment maximally similar to the natural one. This results in a number of unexpected, striking observations about the structure of the neural code in this system, typically unseen in simpler, more traditional experimental setups. First, the timing of spikes is important with a precision roughly two orders of magnitude greater than the temporal dynamics of the stimulus, which is behaviorally controlled in the natural settings. Second, the fly goes a long way to utilize the redundancy in the stimulus in order to optimize the neural code and encode efficiently more refined features than would be possible otherwise, providing sufficient information about the stimulus in time for behavioral decision making. This implies that the neural code, even in low-level vision, may be significantly context (that is, environment and behavior) dependent. The presentation is based on: I Nemenman, GD Lewen, W Bialek, RR de Ruyter van Steveninck. Neural Coding of Natural Stimuli: Information at Sub-Millisecond Resolution. \textit{PLoS Comput Biol} \textbf{4} (3): e1000025, 2008. [Preview Abstract] |
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