Statistical analysis of neural data: Maximum a posteriori techniques for decoding spike trains
نویسنده
چکیده
2 Maximum a posteriori neural decoding 3 2.1 Gaussian approximations to the posterior p(~x|D) are tractable and useful . . 4 2.1.1 Moment-matching provides an alternative method for constructing the Gaussian approximation . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Numerical implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 MAP decoding examples: correlated and spatial stimuli . . . . . . . . . . . . 7 2.4 Decoding Poisson image observations . . . . . . . . . . . . . . . . . . . . . . . 11
منابع مشابه
Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains
One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models,...
متن کاملStatistical models for neural encoding, decoding, and optimal stimulus design.
There are two basic problems in the statistical analysis of neural data. The "encoding" problem concerns how information is encoded in neural spike trains: can we predict the spike trains of a neuron (or population of neurons), given an arbitrary stimulus or observed motor response? Conversely, the "decoding" problem concerns how much information is in a spike train, in particular, how well can...
متن کاملModel-based decoding, information estimation, and change- point detection in multi-neuron spike trains
Understanding how stimulus information is encoded in spike trains is a central problem in computational neuroscience. Decoding methods provide an important tool for addressing this problem, by allowing us to explicitly read out the information contained in spike responses. Here we introduce several decoding methods based on point-process neural encoding models (i.e. “forward” models that predic...
متن کاملMarkov Chain Monte Carlo Methods for Decoding Neural Spike Trains
Stimulus reconstruction or decoding methods provide an important tool for understanding how sensory and motor information is represented in neural activity. We address Bayesian decoding methods based on an encoding generalized linear model (GLM) [1, 2] that accurately describes how stimuli are transformed into the spike trains of a group of neurons. The log-concave GLM likelihood is combined wi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007