Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation
H Kim, S Shinomoto - Mathematical Biosciences & Engineering, 2013 - aimsciences.org
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 …
contain information about the dynamics of unobserved neurons. A state-space method …
Maximum likelihood estimation of a stochastic integrate-and-fire neural model
L Paninski, E Simoncelli… - Advances in Neural …, 2003 - proceedings.neurips.cc
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 …
which some linear filtering process is followed by a nonlinear, probabilistic spiking stage …
Ergodicity of spike trains: when does trial averaging make sense?
N Masuda, K Aihara - Neural computation, 2003 - direct.mit.edu
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 …
relevant statistics such as firing rates calculated with the peri-stimulus time histogram are …
Tracking fast and slow changes in synaptic weights from simultaneously observed pre-and postsynaptic spiking
G Wei, IH Stevenson - Neural computation, 2021 - direct.mit.edu
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 …
combination of both short-and long-term plasticity. Here we develop an extension of the …
Estimating parameters of generalized integrate-and-fire neurons from the maximum likelihood of spike trains
Y Dong, S Mihalas, A Russell… - Neural …, 2011 - ieeexplore.ieee.org
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 …
the neuron that generated it? A natural way to answer this question is to make an …
Integrate-and-fire models with adaptation are good enough
R Jolivet, A Rauch, HR Lüscher… - Advances in neural …, 2005 - proceedings.neurips.cc
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 …
hand, the Integrate-and-Fire model is the basis of most of the theoretical studies on spiking …
Elimination of response latency variability in neuronal spike trains
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 …
variability. This may be reflected by the magnitude of the activity as well as by the response …
Spike train probability models for stimulus-driven leaky integrate-and-fire neurons
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 …
spiking behavior. These models can produce artificial spike trains that resemble actual spike …
Transient responses to rapid changes in mean and variance in spiking models
P Khorsand, F Chance - PloS One, 2008 - journals.plos.org
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 …
suggesting two distinct information channels. Here we examine the impact of rapidly varying …
The parameters of the stochastic leaky integrate-and-fire neuronal model
P Lansky, P Sanda, J He - Journal of Computational Neuroscience, 2006 - Springer
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 …
and-fire model, also known as the Ornstein-Uhlenbeck neuronal model, were estimated on …