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The complexity of processes occurring in the brain is an intriguing issue not just for scientists and medical doctors, but the humanity in general. The cortex ability to perceive and analyze an enormous amount of information in an instance of time, the par ...
Understanding the guiding principles of sensory coding strategies is a main goal in computational neuroscience. Among others, the principles of predictive coding and slowness appear to capture aspects of sensory processing. Predictive coding postulates tha ...
The goal of neural processing assemblies is varied, and in many cases still rather unclear. However, a possibly reasonable subgoal is that sensory information may be encoded efficiently in a population of neurons. In this context, Mutual Information is a l ...
As large-scale, detailed network modeling projects are flourishing in the field of computational neuroscience, it is more and more important to design single neuron models that not only capture qualitative features of real neurons but are quantitatively ac ...
Spiking Neuron Networks (SNNs) are often referred to as the 3rd generation of neural networks. They derive their strength and interest from an accurate modelling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs ...
We propose a novel network model of spiking neurons, without preimposed topology and driven by STDP (Spike-Time-Dependent Plasticity), a temporal Hebbian unsupervised learning mode, based on biological observations of synaptic plasticity. The model is furt ...
A neuron that is stimulated repeatedly by the same time-dependent stimulus exhibits slightly different spike timing at each trial. We compared the exact solution of the time-dependent firing rate for a stochastically spiking neuron model with refractorines ...
In the present paper we propose a novel method for the identification and modeling of neural networks using extracellular spike recordings. We create a deterministic model of the effective network, whose dynamic behavior fits experimental data. The network ...
Most simple neuron models are only able to model traditional spiking behavior. As physiologists discover and classify different electrical phenotypes, computational neuroscientists become interested in using simple phenomenological models that can exhibit ...
We describe evolution of spiking neural architectures to control navigation of autonomous mobile robots. Experimental results with simple fitness functions indicate that evolution can rapidly generate spiking circuits capable of navigating in textured envi ...
