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Picture 1 of 1
Reconstruction
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eBay item number:386711654916
Item specifics
- Condition
- ISBN-13
- 9783319570808
- Publication Name
- NA
- ISBN
- 9783319570808
- Book Title
- Reconstruction, Identification, and Implementation Methods for Spiking Neural Circuits
- Book Series
- Springer Theses Ser.
- Publisher
- Springer International Publishing A&G
- Item Length
- 9.3 in
- Publication Year
- 2017
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Illustrator
- Yes
- Genre
- Mathematics, Technology & Engineering, Science, Medical
- Topic
- Neuroscience, Life Sciences / Neuroscience, Signals & Signal Processing, Applied
- Item Weight
- 127.4 Oz
- Item Width
- 6.1 in
- Number of Pages
- Xiv, 139 Pages
About this product
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3319570803
ISBN-13
9783319570808
eBay Product ID (ePID)
237664684
Product Key Features
Book Title
Reconstruction, Identification, and Implementation Methods for Spiking Neural Circuits
Number of Pages
Xiv, 139 Pages
Language
English
Topic
Neuroscience, Life Sciences / Neuroscience, Signals & Signal Processing, Applied
Publication Year
2017
Illustrator
Yes
Genre
Mathematics, Technology & Engineering, Science, Medical
Type
Textbook
Book Series
Springer Theses Ser.
Format
Hardcover
Dimensions
Item Weight
127.4 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Dewey Edition
23
Number of Volumes
1 vol.
Dewey Decimal
612.82
Table Of Content
Nomenclature.- Acronyms.- 1 Introduction.- 2 Time Encoding and Decoding in Bandlimited and Shift-Invariant Spaces.- 3 A Novel Framework for Reconstructing Bandlimited Signals Encoded by Integrate and-Fire Neurons.- 4 A Novel Reconstruction Framework in Shift-Invariant Spaces for Signals Encoded with Integrate-and-Fire Neurons.- 5 A New Approach to the Identification of Sensory Processing Circuits Based on Spiking Neuron Data.- 6 A New Method for Implementing Linear Filters in the Spike Domain.- 7 Conclusions and Future Work.- Bibliography.
Synopsis
This work is motivated by the ongoing open question of how information in the outside world is represented and processed by the brain. Consequently, several novel methods are developed. A new mathematical formulation is proposed for the encoding and decoding of analog signals using integrate-and-fire neuron models. Based on this formulation, a novel algorithm, significantly faster than the state-of-the-art method, is proposed for reconstructing the input of the neuron. Two new identification methods are proposed for neural circuits comprising a filter in series with a spiking neuron model. These methods reduce the number of assumptions made by the state-of-the-art identification framework, allowing for a wider range of models of sensory processing circuits to be inferred directly from input-output observations. A third contribution is an algorithm that computes the spike time sequence generated by an integrate-and-fire neuron model in response to the output of alinear filter, given the input of the filter encoded with the same neuron model., This work is motivated by the ongoing open question of how information in the outside world is represented and processed by the brain. Consequently, several novel methods are developed. A new mathematical formulation is proposed for the encoding and decoding of analog signals using integrate-and-fire neuron models. Based on this formulation, a novel algorithm, significantly faster than the state-of-the-art method, is proposed for reconstructing the input of the neuron. Two new identification methods are proposed for neural circuits comprising a filter in series with a spiking neuron model. These methods reduce the number of assumptions made by the state-of-the-art identification framework, allowing for a wider range of models of sensory processing circuits to be inferred directly from input-output observations. A third contribution is an algorithm that computes the spike time sequence generated by an integrate-and-fire neuron model in response to the output of a linear filter, given the input of the filter encoded with the same neuron model.
LC Classification Number
TK5102.9
Item description from the seller
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