Because every spike of a neuron is determined by input signals, a train of spikes may
contain information about the dynamics of unobserved neurons. A state-space method …

Recent work has examined the estimation of models of stimulus-driven neural activity in
which some linear filtering process is followed by a nonlinear, probabilistic spiking stage …

Neuronal information processing is often studied on the basis of spiking patterns. The
relevant statistics such as firing rates calculated with the peri-stimulus time histogram are …

Synapses change on multiple timescales, ranging from milliseconds to minutes, due to a
combination of both short-and long-term plasticity. Here we develop an extension of the …

When a neuronal spike train is observed, what can we deduce from it about the properties of
the neuron that generated it? A natural way to answer this question is to make an …

Integrate-and-Fire-type models are usually criticized because of their simplicity. On the other
hand, the Integrate-and-Fire model is the basis of most of the theoretical studies on spiking …

Neuronal activity in the mammalian cortex exhibits a considerable amount of trial-by-trial
variability. This may be reflected by the magnitude of the activity as well as by the response …

Mathematical models of neurons are widely used to improve understanding of neuronal
spiking behavior. These models can produce artificial spike trains that resemble actual spike …

The mean input and variance of the total synaptic input to a neuron can vary independently,
suggesting two distinct information channels. Here we examine the impact of rapidly varying …

Five parameters of one of the most common neuronal models, the diffusion leaky integrate-
and-fire model, also known as the Ornstein-Uhlenbeck neuronal model, were estimated on …