Rate and phase codes are believed to be important in neural information processing. Hippocampal place cells provide a good example where both coding schemes coexist during spatial information processing. Spike rate increases in the place field, whereas spike phase precesses relative to the ongoing theta oscillation. However, what intrinsic mechanism allows for a single neuron to generate spike ...

Variability in single neuron models is typically implemented either by a stochastic Leaky-Integrate-and-Fire model or by a model of the Generalized Linear Model (GLM) family. We use analytical and numerical methods to relate state-of-theart models from both schools of thought. First we find the analytical expressions relating the subthreshold voltage from the Adaptive Exponential Integrate-andF...

The functional reconfiguration of central neuronal networks, a phenomenon by which neurons change their participation in network operation, is important for organizing adaptive behaviors. Such reconfiguration can be expressed in a long-lasting manner (hours, days) after a training paradigm. The present study shows that such a long-lasting network reconfiguration requires a cooperation of both p...

A central question in artificial intelligence is how to design agents capable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by utilizing artificial neural networks (NNs) as agent controllers, and mechanisms such as neuromodulation and synaptic gating. The novel aspect of this work is the introduction ...

This paper introduces a new model of a spiking neuron with active dendrites and dynamic synapses (ADDS). The neuron employs the dynamics of the synapses and the active properties of the dendrites as an adaptive mechanism for maximising its response to a specific spatio-temporal distribution of incoming action potentials. The paper also presents a new spike-timing-dependent plasticity (STDP) alg...

Automated and accurate localization and morphometry of somas in 3D neuron images is essential for quantitative studies of neural networks in the brain. However, previous methods are limited in obtaining the location and surface morphology of somas with variable size and uneven staining in large-scale 3D neuron images. In this work, we proposed a method for automated soma locating in large-scale...