Volume 14 Supplement 1
Abstracts from the Twenty Second Annual Computational Neuroscience Meeting: CNS*2013
Meeting abstracts
Edited by Gennady S Cymbalyuk and Astrid A Prinz
Publication of this supplement has been funded by the Organization for Computational Neurosciences. The Supplement Editors declare that they have no competing interests.
Twenty Second Annual Computational Neuroscience Meeting: CNS*2013. Go to conference site.
Paris, France13-18 July 2013
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Citation: BMC Neuroscience 2013 14(Suppl 1):P222
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Change point detection for neuronal data with CUSUM and classification methods
Citation: BMC Neuroscience 2013 14(Suppl 1):P223 -
Coding of touch properties by three types of mechanosensory cells of the leech hirudo medicinalis
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SPIKY: a graphical user interface for monitoring spike train synchrony
Citation: BMC Neuroscience 2013 14(Suppl 1):P225 -
Synfire chains and gamma oscillations: two complementary modes of information transmission in cortical networks
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Effects of single neuron firing patterns on network dynamics
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Controlling the Go / No-Go decision threshold in the striatum
Citation: BMC Neuroscience 2013 14(Suppl 1):P228 -
Investigating dynamical properties of the Caenorhabditis elegans connectome through full-network simulations
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A reaction-diffusion model of cholinergic retinal waves and self-organized criticality
Citation: BMC Neuroscience 2013 14(Suppl 1):P230 -
Axonal injuries and consequences to neuronal computation
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Towards a dynamic clamp for neuro-chemical modalities
Citation: BMC Neuroscience 2013 14(Suppl 1):P232 -
Hopfield neural network model for explaining double dissociation in semantic memory impairment
Citation: BMC Neuroscience 2013 14(Suppl 1):P233 -
Periodically stimulated piecewise linear adaptive exponential integrate-and-fire neuron
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Latency of inhibitory response
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Probabilistic computation underlying sequence learning in a spiking attractor memory network
Citation: BMC Neuroscience 2013 14(Suppl 1):P236 -
Neuronal avalanches change from wakefulness to deep sleep - a study of intracranial depth recordings in humans
Citation: BMC Neuroscience 2013 14(Suppl 1):P237 -
Towards a computational model of learning and social interactions of mice in IntelliCage
Citation: BMC Neuroscience 2013 14(Suppl 1):P238 -
Interspike intervals under the constraint of linear synaptic integration and background synaptic activity
Citation: BMC Neuroscience 2013 14(Suppl 1):P239 -
Where are the most informative neurons?
Citation: BMC Neuroscience 2013 14(Suppl 1):P240 -
The influence of multiple firing events on the formation and stability of activity patterns in continuous attractor networks
Citation: BMC Neuroscience 2013 14(Suppl 1):P241 -
Invariance of visual operations at the level of receptive fields
Citation: BMC Neuroscience 2013 14(Suppl 1):P242 -
Analytical results for integrate-and-fire neurons driven by dichotomous noise
Citation: BMC Neuroscience 2013 14(Suppl 1):P243 -
Generalized synchronous output of neural populations - does it only encode fast stimulus components?
Citation: BMC Neuroscience 2013 14(Suppl 1):P244 -
Mutual information density of stochastic integrate-and-fire models
Citation: BMC Neuroscience 2013 14(Suppl 1):P245 -
Does noise shift or delete spikes?
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On the effect of network structure and synaptic mechanisms on sustained bursting activity
Citation: BMC Neuroscience 2013 14(Suppl 1):P247 -
Divisive and non-monotonic gain control in open-loop neural circuits
Citation: BMC Neuroscience 2013 14(Suppl 1):P248 -
Learning to perform contrast-invariant cancellation of redundant stimuli
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Positive sparse coding of natural images: a theory for simple cell tuning
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Network oscillations in a neural mass model induced by metabolic modulation are consistent with EEG data of neocortical epileptic seizure onset
Citation: BMC Neuroscience 2013 14(Suppl 1):P251 -
A large deviation principle for networks of rate neurons with correlated synaptic weights
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Abstract withdrawn
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Motion control of thumb and index finger of an artificial hand for precision grip using asynchronous decoding of CM cell activity
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Inferring network model from local field potentials
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Neuronal chains as processing units of long scale wave information
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The Hopfield-like neural network with governed ground state
Citation: BMC Neuroscience 2013 14(Suppl 1):P257 -
How conductance distributions are shaped by activity-dependent regulation rules
Citation: BMC Neuroscience 2013 14(Suppl 1):P258 -
The rectification of an electrical synapse can change the functional output of a pattern-generating circuit
Citation: BMC Neuroscience 2013 14(Suppl 1):P259 -
Recovering directed networks in neuroimaging datasets using partially conditioned Granger causality
Citation: BMC Neuroscience 2013 14(Suppl 1):P260 -
Maximizing the efficiency of decoding by selecting heterogeneous inputs from a population of electrosensory neurons responding to communication signals
Citation: BMC Neuroscience 2013 14(Suppl 1):P261 -
V1 neurons can distinguish between motion in the world and visual displacements due to eye movements: a microsaccade study
Citation: BMC Neuroscience 2013 14(Suppl 1):P262 -
Binary matrices and simplicial complexes: an algebraic-statistics tool to analyze co-activation of electrophysiological signals in cortical cultures
Citation: BMC Neuroscience 2013 14(Suppl 1):P263 -
Concurrent scale-free and small-world networks support criticality in cortical ensembles
Citation: BMC Neuroscience 2013 14(Suppl 1):P264 -
Modular versus uniform cultured neuronal networks: a modeling study
Citation: BMC Neuroscience 2013 14(Suppl 1):P265 -
Inferring effective dynamics in large-scale networks of cortical neurons
Citation: BMC Neuroscience 2013 14(Suppl 1):P266 -
Non-instantaneous synaptic transmission in spiking neuron networks and equivalence with delay distribution
Citation: BMC Neuroscience 2013 14(Suppl 1):P267 -
Dynamical features of stimulus integration by interacting cortical columns
Citation: BMC Neuroscience 2013 14(Suppl 1):P268 -
Ca2+buffering as a mechanism of short-term synaptic plasticity
Citation: BMC Neuroscience 2013 14(Suppl 1):P269 -
Likelihood-free Bayesian analysis of neural network models
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Temporal sequence learning in reentrantly coupled winner-take-all networks of spiking neurons
Citation: BMC Neuroscience 2013 14(Suppl 1):P271
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