By Patricia M. DiLorenzo, Jonathan D. Victor
Brings jointly information regarding spike timing from a variety of fields and perspectives
offers theoretical and computational stories, in addition to experimental stories in various key version systems
Describes tools for measuring and examining spike timing
offers a history for investigators to think about spike timing because it applies to their very own work
deals a view of the long run course of labor on the topic of mechanisms of spike timing, and its position in neural function
offers a systematic foundation for inclusion of impulse timing within the improvement of novel computational technologies
Neuronal conversation kinds the foundation for all habit, from the smallest flow to our grandest idea approaches. among the mechanisms that help those features, spike timing is likely one of the strongest and—until recently—perhaps the least studied. within the final 20 years, even though, the learn of spike timing has exploded. The heightened curiosity is because of a number of elements. those contain the advance of physiological instruments for measuring the job of neural ensembles and analytical instruments for assessing and characterizing spike timing. those advances are coupled with a becoming appreciation of spike timing’s theoretical value for the layout ideas of the brain.
Spike Timing: Mechanisms and serve as examines the functionality of spike timing in sensory, motor, and integrative approaches, offering readers with a vast viewpoint on how spike timing is produced and utilized by the worried procedure. It brings jointly the paintings and concepts of leaders within the box to deal with present considering in addition to destiny possibilities.
The first part of the ebook describes the basis for quantitative research and idea. It examines the data contained in spike timing, the way it should be quantified, and the way neural platforms can extract it. the second one part explores how input-output relationships are mirrored in spike timing throughout a number sensory systems.
Drawing jointly a number of views, together with theoretical and computational reviews in addition to experimental experiences in quite a number version platforms, the ebook presents a company history for investigators to contemplate spike timing because it applies to their very own paintings. It additionally deals a glimpse of destiny advances concerning mechanisms of spike timing and its function in neural functionality, akin to the improvement of novel computational technologies.
Table of Contents
Spike Timing: instruments and Models
Spike Trains as occasion Sequences: basic Implications; Jonathan D. Victor and Sheila Nirenberg
Neural Coding and interpreting with Spike instances; Ran Rubin, Robert Gütig, and Haim Sompolinsky
Can we think each Spike? Richard Naud and Wolfram Gerstner
Statistical id of Synchronous Spiking; Matthew T. Harrison, Asohan Amarasingham, and Robert E. Kass
Binless Estimation of Mutual details in Metric areas; Ayelet-Hashahar Shapira, and Israel Nelken
Measuring details in Spike Trains approximately Intrinsic mind signs; Gautam Agarwal and Friedrich T. Sommer
Role of Oscillation-Enhanced Neural Precision in details Transmission among mind parts; Paul H. Tiesinga, Saša Koželj, and Francesco P. Battaglia
Spike Timing: Coding, deciphering, and Sensation
Timing info in Insect Mechanosensory platforms; Alexander G. Dimitrov and Zane N. Aldworth
Neural Encoding of Dynamic Inputs via Spike Timing; Matthew H. Higgs and William J. Spain
Relating Spike occasions to belief: Auditory Detection and Discrimination; Laurel H. Carney
Spike Timing and Neural Codes for Odors; Sam Reiter and Mark Stopfer
Spike Timing as a Mechanism for style Coding within the Brainstem; Patricia M. Di Lorenzo
Increases in Spike Timing Precision Improves Gustatory Discrimination upon studying; Ranier Gutierrez and Sidney A. Simon
Spike Timing in Early phases of visible Processing; Paul R. Martin and Samuel G. Solomon
Cortical Computations utilizing Relative Spike Timing; Timothy J. Gawne
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Additional resources for Spike timing : mechanisms and function
Interval h + 1) contains the stimulus. Moreover, the observer knows that there is an ambiguity because all neurons of class ≤h are firing, and all neurons of class ≥h + 1 are not firing. Once this ambiguity is removed, the atypical trial would yield the same amount of information as a typical one—since then, the relevant interval and class would be known. The above analysis shows that we can calculate Iclass(N1,. 23) 29 Spike Trains as Event Sequences where b(h) is the probability that there is an ambiguity between the intervals represented by class h and h + 1, and Iambig(h) is the information required to resolve this ambiguity.
Since the thresholds are unknown, the response variable r is simply the number of neurons that fire (k), which can range from 0 to N. As mentioned in the main text, the probability pN(s|k) can be determined by thinking of the stimulus value and the N thresholds as a set of N + 1 random numbers drawn from the [0,1]-interval: N random numbers that describe the neurons, along with the random number, s, that describes the stimulus. , that it is higher than the descriptors of the k neurons that fired and lower than the descriptors of the N − k neurons that did not fire).
5. 6 extends the model, incorporating synaptic inputs as c onductance changes. This section highlights the computational role of synaptic conductances in processing inputs with variable timescales (the “timewarp” problem). 7.