… 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) …

… 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 …

… investigation of neuronal coding mechanisms and abnormal signaling. However, …
nonstationarity, or employ dictionary-based Lempel-Ziv (LZ) methods that converge too slowly for …

… (a) A 10-slong EMG signal during a simulated dynamic … spike train estimated from the
simulated signals with the proposed update strategy. (c) The spike train same as (b) but estimated …

… We can calculate the parameter values by fitting the … only one-bit (spatial) resolution,
but if we dither the analog signal … stationary input, we can directly generalize to the non-stationary …

… neurons to model highly non-stationary, collective spiking … wave, population spike rate
histogram and estimates from our … , weak input signals from spike trains of cortical neurons and …

… To efficiently estimate the model parameters and compare … For comparing true and inferred
mean input signal of each … of neurons receiving non–stationary doubly–stochastic inputs. …

… The accuracy for the FHN method was obtained by calculating the consistency between
the spike train processed using the optimal FHN model and the corresponding ground-truth …

… , 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 …

… modeling on the nonstationary neural signals from different … The relationship between the
neuronal spike train and the … of characterizing nonstationary activity becomes estimating the …