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Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning
tl;dr
We find that the optimal strategy of up- and downregulating synaptic efficacies depends on the relative timing between presynaptic spike arrival and desired postsynaptic firing. Expand
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Bayesian Inference Explains Perception of Unity and Ventriloquism Aftereffect: Identification of Common Sources of Audiovisual Stimuli
tl;dr
We study a computational model of audiovisual integration by setting a Bayesian observer that localizes visual and auditory stimuli without presuming the binding of auditory and visual information. Expand
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Modeling the Dynamic Interaction of Hebbian and Homeostatic Plasticity
Hebbian and homeostatic plasticity together refine neural circuitry, but their interactions are unclear. In most existing models, each form of plasticity directly modifies synaptic strength.Expand
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A Theory of the Transition to Critical Period Plasticity: Inhibition Selectively Suppresses Spontaneous Activity
What causes critical periods (CPs) to open? For the best-studied case, ocular dominance plasticity in primary visual cortex in response to monocular deprivation (MD), the maturation of inhibition isExpand
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Self-tuning of neural circuits through short-term synaptic plasticity.
Numerous experimental data show that cortical networks of neurons are not silent in the absence of external inputs, but rather maintain a low spontaneous firing activity. This aspect of corticalExpand
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Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model
tl;dr
We derive an optimal learning rule in the sense of mutual information maximization for a spiking neuron model which depends on the time course of excitatory postsynaptic potentials (EPSPs) and the autocorrelation function of the post synaptic neuron. Expand
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Statistical mechanics of an NP-complete problem: subset sum
We study the statistical properties of an NP-complete problem, the subset sum, using the methods and concepts of statistical mechanics. The problem is a generalization of the number partitioningExpand
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Beyond the edge of chaos: amplification and temporal integration by recurrent networks in the chaotic regime.
Randomly connected networks of neurons exhibit a transition from fixed-point to chaotic activity as the variance of their synaptic connection strengths is increased. In this study, we analyticallyExpand
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Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding
tl;dr
We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. Expand
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Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness
tl;dr
We introduce a model that captures strong refractoriness, retains all of the easy fitting properties of the standard generalized linear model, and leads to much more accurate approximations of mean firing rates and cross-correlations that retain fine temporal behaviors. Expand
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  • Open Access