* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Using the State-Space Paradigm to Analyze Information Representation in Neural Systems
Clinical neurochemistry wikipedia , lookup
Single-unit recording wikipedia , lookup
Biological neuron model wikipedia , lookup
Premovement neuronal activity wikipedia , lookup
Neuroesthetics wikipedia , lookup
Binding problem wikipedia , lookup
Incomplete Nature wikipedia , lookup
Neurocomputational speech processing wikipedia , lookup
Feature detection (nervous system) wikipedia , lookup
Neurophilosophy wikipedia , lookup
Process tracing wikipedia , lookup
Cortical cooling wikipedia , lookup
Central pattern generator wikipedia , lookup
Multielectrode array wikipedia , lookup
Synaptic gating wikipedia , lookup
Microneurography wikipedia , lookup
Neural modeling fields wikipedia , lookup
Neuroanatomy wikipedia , lookup
Time series wikipedia , lookup
Neuroethology wikipedia , lookup
Convolutional neural network wikipedia , lookup
Neuroeconomics wikipedia , lookup
Neural oscillation wikipedia , lookup
Neural correlates of consciousness wikipedia , lookup
Optogenetics wikipedia , lookup
Neuropsychopharmacology wikipedia , lookup
Channelrhodopsin wikipedia , lookup
Neural coding wikipedia , lookup
Artificial neural network wikipedia , lookup
Types of artificial neural networks wikipedia , lookup
Metastability in the brain wikipedia , lookup
Nervous system network models wikipedia , lookup
Recurrent neural network wikipedia , lookup
Neural binding wikipedia , lookup
The University of Chicago Department of Statistics Seminar Series EMERY N. BROWN, M.D., Ph.D. Harvard Medical School/MIT Division of Health Sciences and Technology Department of Anesthesia and Critical Care Massachusetts General Hospital “Using the State-Space Paradigm to Analyze Information Representation in Neural Systems” MONDAY November 1, 2004 at 4:00 PM 133 Eckhart Hall, 5734 S. University Avenue Refreshments following the seminar in Eckhart 110. ABSTRACT Neural systems encode representations of relevant biological signals in the firing patterns of their spike trains. Spike trains are point process time-series and their codes are both dynamic and stochastic. Even though the signal is often continuous, its representation in the nervous systems is as a high-dimensional point process time-series. Because neural spike trains are point processes, standard signal processing techniques for continuous-valued data will have limited utility in the analysis of neural systems. Accurate processing of neural signals requires the development of quantitative techniques to characterize correctly the point process nature of neural encoding. The advent in the last 10 years of the capability to record with multiple electrode arrays the simultaneous spiking activity of many neurons (¿100) has made it possible to study information encoding by ensembles rather than by simply single neurons. Hence, an important question in neuroscience is developing algorithms to analyze dynamic, high-dimensional spike train (point process) measurements. The statespace modeling paradigm is a well-known engineering framework for studying systems that evolve through time. In this presentation, we will discuss the application of this paradigm in neural spike train data analysis. We use the Baye’s rule, Chapman-Kolmogorov equations to derive algorithms useful for neural spike train decoding, dynamic analysis of neural encoding (neural plasticity) and adaptive-decoding. We will illustrate the methods in three examples: Decoding position from the ensemble activity of hippocampal pyramidal neurons, tracking the temporal evolution in hippocampal place receptive fields, tracking movement encoding in by neurons in the motor cortex.