… in the input and the amplitude of the signal relative to the … explain how to estimate this
information in a spike train (the … be due to neural adaptation to the input or to non-stationary …

… in particular involves non-stationary spike trains, which … 25] and can be computationally
relevant for signal detection … of estimated instantaneous rates and CVs from the training data (left) …

… -related signals to reproduce such nonstationary effects. … method to estimate the model
parameters from spiking data … at most one spike falls in each time bin, we define the spike train as …

… Tn of a neuron subject to an input signal, we consider the FPT Tk of a stochastic process Vk(t)
(… Shinomoto, Estimating nonstationary inputs from a single spike train based on a neuron …

… Most recorded spike trains are non-stationary due to temporal … optimal refractory period can
be estimated by finding the value (… address the details of signal integration by single neurons. …

… in decoding simulated non-stationary spike signals, and the … the neural firing rate with Eq
(3), we estimated the spike … of spike signal as a Bernoulli trial, so the binned spike counts …

… estimated the input parameters μ and σ of an I&F neuron from the observed spike train for
each stimulus by maximizing the spike train … be highly nonstationary, so that the spike trains of …

… of neural spike train data. In fact, interaction measures like correlation and GC are typically
defined for continuously valued signals … for the estimation of the MIR between spike trains. …

… , we denote the spike train of the qth neuron by N(q) … neural signals such as EEG and LFP,
spike trains take discrete 0-1 values and a Poisson point process likelihood is more suitable …

… In this work we present a framework for modelling neuronal spike trains, including the absolute
and relative refractory … Shinomoto, “Estimating nonstationary input signals from a single …