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The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienen- ...
Uniform random sparse network architectures are ubiquitous in computational neuroscience, but the implicit hypothesis that they are a good representation of real neuronal networks has been met with skepticism. Here we used two experimental data sets, a stu ...
6 The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structur ...
To appreciate how neural circuits in the brain control behaviors, we must identify how the neurons comprising the circuit are connected. Neuronal connectivity is difficult to determine experimentally, whereas neuronal activity can often be readily measured ...
Predicting activity of single neuron is an important part of the computational neuroscience and a great challenge. Several mathematical models exist, from the simple (one compartment and few parameters, like the SRM or the IF-type models), to the more comp ...
In this paper, we present a continuous attractor network model, which we hypothesize will give some suggestion of the mechanisms underlying several neural processes, such as velocity tuning to visual stimulus, sensory discrimination, sensorimotor-transform ...
The human nervous system processes a continuous stream of multi-modal input from a rapidly changing environment. A key challenge for neural modeling is to explain how the neural microcircuits (columns, minicolumns, etc.) in the cerebral cortex whose anatom ...
Networks of fast nonlinear elements may display slowfluctuations if interactions are strong. We find a transition in the long-term variability of a sparse recurrent network of perfect integrate-and-fire neurons at which the Fano factor switches from zero t ...
The study of several aspects of the collective dynamics of interacting neurons can be highly simplified if one assumes that the statistics of the synaptic input is the same for a large population of similarly behaving neurons (mean field approach). In part ...
The integration of modulatory neurons into evolutionary artificial neural networks is proposed here. A model of modulatory neurons was devised to describe a plasticity mechanism at the low level of synapses and neurons. No initial assumptions were made on ...