Figure 1. The spike-triggered stimulus ensemble. (A) Discretized stimulus sequence and observed neural response (spike train). On each time step, the stimulus consists of an array of randomly chosen values (eight, for this example). These could represent, for example, the.
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Response properties of sensory neurons are commonly described using receptive fields. This description may be formalized in a model that operates with a small set of linear filters whose outputs are nonlinearly combined to determine the instantaneous firing rate. Spike-triggered average and covariance analyses can be.
In a typical neural characterization experiment, the experimenter presents a train of rapidly varying sensory stimuli and records a spike train response. Let x denote a D-dimensional vector containing the spatio-temporal stimulus affecting a neuron’s scalar spike response y in a single time bin.Spike-triggered neural characterization O Schwartz, J W Pillow, N C Rust and E P Simoncelli Published in Journal of Vision, vol.6(4), pp. 484--507, Jul 2006.Characterizing neural gain control using spike-triggered covariance. Advances in neural information processing systems, 1, 269-276. Paninski, L. (2004) Maximum Likelihood estimation of cascade point-process neural encoding models. Network: Comput. Neural Syst. ,15, 243-262. Schwartz, O. et al. (2006) Spike-triggered neural characterization.
Characterizing neural gain control using spike-triggered covariance. Advances in neural information processing systems, 1, 269-276. Paninski, L. (2004) Maximum Likelihood estimation of cascade point-process neural encoding models. Network: Comput. Neural Syst. ,15, 243 -262. Schwartz, O. et al. (2006) Spike-triggered neural characterization.
Spike-triggered averaging techniques are effective for linear characterization of neural responses. But neurons exhibit important nonlinear behaviors, such as gain control, that are not captured by such analyses. We describe a spike-triggered covariance method for retrieving suppressive components of the gain control signal in a neuron.
Neural Coding: Spike Triggered Average 1) Record stimulus over complete spike train. 2) Record all spike times. 3) For every spike, add the stimulus values surrounding the spike into the spike-triggered average array. For example, a spike at 10.12s, the stimulus at 10.02s goes into the -0.10s bin, the stimulus at 10.03s goes into the -0.09s bin.
Spike-triggered covariance (STC) analysis is a tool for characterizing a neuron's response properties using the covariance of stimuli that elicit spikes from a neuron. STC is related to the spike-triggered average (STA), and provides a complementary tool for estimating linear filters in a linear-nonlinear-Poisson (LNP) cascade model.
Spectral methods for neural characterization using generalized quadratic models. The resulting theory generalizes moment-based estimators such as the spike-triggered covariance, and, in the Gaussian noise case, provides closed-form estimators under a large class of non-Gaussian stimulus distributions.. analog and spiking data using.
We examined this class of model directly by applying spike-triggered covariance analysis to responses of monkey V1 neurons under binary white noise stimulation. The analysis extracts a low-dimensional subspace of the full stimulus space that is primarily responsible for generation of the neural response, including both excitatory and suppressive components.
We describe an information-theoretic framework for fitting neural spike responses with a Linear-Nonlinear-Poisson cascade model. This framework unifies the spike-triggered average (STA) and spike-triggered covariance (STC) approaches to neural characterization and recovers a set of linear filters that maximize mean and variance-dependent information between stimuli and spike responses.
Here we applied a neuronal spike-triggered impulse response to electrophysiological recordings from the human epileptic brain for the first time, and we evaluate functional connectivity in relation to brain areas supporting the generation of seizures.
Analysis of Neuronal Spike Trains, Deconstructed. Most approaches to neural characterization therefore focus primarily on identifying the subspace of the sensory stimulus space that affects a.
However, functional characterization of the AOX genes is required to better understand the mechanism of pheromone degradation and inactivation in B. mori. Regarding pheromone degradation by AOX, the isolation and functional characterization of an AOX2 homolog from navel orangeworm moth, Amyelois transitella, for pheromone degradation has recently been reported (Choo et al., 2013 ).
Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity.