Record Details

First-Spike-Latency Codes : Significance, Relation to Neuronal Network Structure and Application to Physiological Recordings

Electronic Theses of Indian Institute of Science

View Archive Info
 
 
Field Value
 
Title First-Spike-Latency Codes : Significance, Relation to Neuronal Network Structure and Application to Physiological Recordings
 
Creator Raghavan, Mohan
 
Subject Neuronal Network Structure
First-Spike-Latency Codes
Neuronal Network Structure - Spike Latency Codes
Spike Latency - Neuronal Networks
Spatio Temporal Spike Latency
Synconset Waves
Synconset Chains
Hippocampal Neuronal Cell Culture
Neurons - Mathematical Modelling
Neuronal Cell Culture
Electrophysiology - Spike Latency
Neural Networks
Neural Coding
Spiking Onsets
Epileptogenic Network Structures
Neuronal Networks
Neuroscience
 
Description Over the last decade advances in multineuron simultaneous recording techniques have produced huge amounts of data. This has led to the investigation of probable temporal relationships between spike times of neurons as manifestations of the underlying network structure. But the huge dimensionality of data makes the search for patterns difficult. Although this difficulty may be surpassed by employing massive computing resources, understanding the significance and relation of these temporal patterns to the underlying network structure and the causative activity is still difficult. To find such relationships in networks of excitatory neurons, a simplified network structure of feedforward chains called "Synfire chains" has been frequently employed. But in a recurrently connected network where activity from feedback connections is comparable to the feedforward chain, the basic assumptions underlying synfire chains are violated. In the first part of this thesis we propose the first-spike-latency based analysis as a low complexity method of studying the temporal relationships between neurons. Firstly, spike latencies being temporal delays measured at a particular epoch of time (onset of activity after a quiescent period) are a small subset of all the temporal information available in spike trains, thereby hugely reducing the amount of data that needs to be analyzed. We also define for the first time, "Synconset waves and chains" as a sequence of first-spike-times and the causative neuron chain. Using simulations, we show the efficacy of the synconset paradigm in unraveling feedforward chains of excitatory neurons even in a recurrent network. We further create a framework for going back and forth between network structure and the observed first-spike-latency patterns. To quantify these associations between network structure and dynamics we propose a likelihood measure based on Bayesian reasoning. This quantification is agnostic to the methods of association used and as such can be used with any of the existing approaches. We also show the benefits of such an analysis when the recorded data is subsampled, as is the case with most physiological recordings. In the subsequent part of our thesis we show two sample applications of first-spike-latency analysis on data acquired from multielectrode arrays. Our first application dwells on the intricacies of extracting first-spike-latency patterns from multineuron recordings using recordings of glutamate injured cultures. We study the significance of these patterns extracted vis-a-vis patterns that may be obtained from exponential spike latency distributions and show the differences between patterns obtained in injured and control cultures. In a subsequent application, we study the evolution of latency patterns over several days during the lifetime of a dissociated hippocampal culture.
 
Contributor Amrutur, Bharadwaj
 
Date 2018-04-12T09:55:32Z
2018-04-12T09:55:32Z
2018-04-12
2013
 
Type Thesis
 
Identifier http://etd.iisc.ernet.in/2005/3393
http://etd.iisc.ernet.in/abstracts/4259/G25855-Abs.pdf
 
Language en_US
 
Relation G25855