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Self-organisation of motion features with a temporal asynchronous dynamic vision sensor

Related publications (37)

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Applying the Multivariate Time-Rescaling Theorem to Neural Population Models

Joao Emanuel Felipe Gerhard

Statistical models of neural activity are integral to modern neuroscience. Recently interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on ne ...

Massachusetts Institute of Technology Press2011

Neurons in the brain form complicated networks through synaptic connections. Traditionally, functional connectivity between neurons has been analyzed using simple metrics such as correlation, which do not provide direction of influence. Recently, an inform ...

IEEE2011

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The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties. Traditionally, various current waveforms have been injected at the soma to probe neuron dynamics, but the rationale for ...

Public Library of Science2011

Statistical models of neural activity are at the core of the field of modern computational neuroscience. The activity of single neurons has been modeled to successfully explain dependencies of neural dynamics to its own spiking history, to external stimuli ...

2010

Connectivity reflects coding: a model of voltage-based STDP with homeostasis

Wulfram Gerstner, Eleni Vasilaki, Claudia Clopath

Electrophysiological connectivity patterns in cortex often have a few strong connections, which are sometimes bidirectional, among a lot of weak connections. To explain these connectivity patterns, we created a model of spike timing–dependent plasticity (S ...

Nature Publishing Group2010

The coordinated, collective spiking activity of neuronal populations encodes and processes information. One approach towards understanding such population based computation is to fit statistical models to simultaneously recorded spike trains and use these ...

2010

Generalized Linear Models (GLMs) are an increasingly popular framework for modeling neural spike trains. They have been linked to the theory of stochastic point processes and researchers have used this relation to assess goodness-of-fit using methods from ...

Massachusetts Institute of Technology Press2010

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning r ...

Massachusetts Institute of Technology Press2009

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 ...

EPFL2008

Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full poster ...

2008