Non-renewal Markov models for spike-frequency adapting neural ensembles
نویسندگان
چکیده
منابع مشابه
Spike-Frequency Adapting Neural Ensembles: Beyond Mean Adaptation and Renewal Theories
We propose a Markov process model for spike-frequency adapting neural ensembles that synthesizes existing mean-adaptation approaches, population density methods, and inhomogeneous renewal theory, resulting in a unified and tractable framework that goes beyond renewal and mean-adaptation theories by accounting for correlations between subsequent interspike intervals. A method for efficiently gen...
متن کاملAdapting Hidden Markov Models for Online Learning
In modern computer systems, the intermittent behaviour of infrequent, additional loads affects performance. Often, representative traces of storage disks or remote servers can be scarce and obtaining real data is sometimes expensive. Therefore, stochastic models, through simulation and profiling, provide cheaper, effective solutions, where input model parameters are obtained. A typical example ...
متن کاملSpike sorting with hidden Markov models.
The ability to detect and sort overlapping spike waveforms in extracellular recordings is key to studies of neural coding at high spatial and temporal resolution. Most spike-sorting algorithms are based on initial spike detection (e.g. by a voltage threshold) and subsequent waveform classification. Much effort has been devoted to the clustering step, despite the fact that conservative spike det...
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 کاملAdapting to Non-stationarity with Growing Expert Ensembles
When dealing with time series with complex non-stationarities, low retrospective regret on individual realizations is a more appropriate goal than low prospective risk in expectation. Online learning algorithms provide powerful guarantees of this form, and have often been proposed for use with non-stationary processes because of their ability to switch between different forecasters or “experts”...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Neuroscience
سال: 2007
ISSN: 1471-2202
DOI: 10.1186/1471-2202-8-s2-s12