The Dynamic Brain: An Exploration of Neuronal Variability by Mingzhou Ding PhD, Dennis Glanzman PhD
By Mingzhou Ding PhD, Dennis Glanzman PhD
It's a recognized truth of neurophysiology that neuronal responses to identically offered stimuli are tremendous variable. This variability has some time past usually been considered as "noise." on the unmarried neuron point, interspike period (ISI) histograms built in the course of both spontaneous or stimulus evoked task show a Poisson style distribution. those observations were taken as facts that neurons are intrinsically "noisy" of their firing houses. in reality, using averaging strategies, like post-stimulus time histograms (PSTH) or event-related potentials (ERPs) have mostly been justified according to the presence of what used to be believed to be noise within the neuronal responses.
More contemporary makes an attempt to degree the data content material of unmarried neuron spike trains have published superb quantity of knowledge will be coded in spike trains even within the presence of trial-to-trial variability. a number of unmarried unit recording experiments have prompt that variability previously attributed to noise in unmarried telephone recordings may possibly as an alternative easily replicate system-wide alterations in mobile reaction homes. those observations elevate the prospect that, at the very least on the point of neuronal coding, the range visible in unmarried neuron responses would possibly not easily mirror an underlying noisy method. They extra elevate the very unique hazard that noise may possibly actually comprise genuine, significant info that's to be had for the frightened procedure in info processing.
To know how neurons paintings in live performance to lead to coherent habit and its breakdown in ailment, neuroscientists now normally checklist at the same time from hundreds of thousands of alternative neurons and from varied mind components, after which try and review the community actions by way of computing a variety of interdependence measures, together with move correlation, part synchronization and spectral coherence. This ebook examines neuronal variability from theoretical, experimental and scientific views.
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Extra info for The Dynamic Brain: An Exploration of Neuronal Variability and Its Functional Significance
N. (2006). Selective enhancement of associative learning by microstimulation of the anterior caudate. Nature Neuroscience, 9, 4, 562–568. N. A. Single neurons in the monkey hippocampus and learning of new associations. Science, 300, 5625, 1578–1581. N. A. (2009). Comparison of associative learning-related signals in the macaque perirhinal cortex and hippocampus. Cerebral Cortex, 19, 5, 1064–1078. 18 THE DYNAMIC BRAIN APPENDIX A Details of the Recursive Filter In this section, we provide details of the derivation of equations (8)-(12).
Analysis of population activity demonstrates signiﬁcantly larger trial-to-trial variability in the time from stimulus onset until activity changes signiﬁcantly from baseline in the “jumping” mode compared to “ramping” mode. Averaging activity across multiple trials produces similar average behavior across both modes, however. Thus, only analysis based on multiple spike trains observed simultaneously in each trial can identify the general mode of network operation. Comparison of the outputs of the network in the two modes of operation indicates that in the presence of typical internal noise, the “jumping” mode responds more reliably to a small bias in its inputs than does the “ramping” mode (Miller and Katz, 2010).
Hidden Markov Modeling (HMM) HMM is a statistical method most commonly used in speech recognition software in engineering and for analysis of DNA sequences in biology. , 2007). HMM assumes the presence of two Markov processes, each of which, by deﬁnition, depends only on the current state of the system and is independent of the history of prior states. When applied to neural spike trains, one of the Markov processes is the emission of spikes by a neuron at any point in time. The Markov assumption for spike emission assumes that spike trains follow a Poisson process at a ﬁxed mean rate, given a particular state of the system.