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Métodos de reconstrucción, identificación e implementación para pinchar cir neuronal-
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Última actualización el 22 sep 2024 16:41:20 H.EspVer todas las actualizacionesVer todas las actualizaciones
Características del artículo
- Estado
- ISBN-13
- 9783319860725
- Type
- NA
- Publication Name
- NA
- ISBN
- 9783319860725
- 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
- 2018
- Format
- Trade Paperback
- Language
- English
- Illustrator
- Yes
- Genre
- Mathematics, Technology & Engineering, Science, Medical
- Topic
- Neuroscience, Life Sciences / Neuroscience, Signals & Signal Processing, Applied
- Item Weight
- 87 Oz
- Item Width
- 6.1 in
- Number of Pages
- Xiv, 139 Pages
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Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3319860720
ISBN-13
9783319860725
eBay Product ID (ePID)
9038682387
Product Key Features
Book Title
Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits
Number of Pages
Xiv, 139 Pages
Language
English
Publication Year
2018
Topic
Neuroscience, Life Sciences / Neuroscience, Signals & Signal Processing, Applied
Illustrator
Yes
Genre
Mathematics, Technology & Engineering, Science, Medical
Book Series
Springer Theses Ser.
Format
Trade Paperback
Dimensions
Item Weight
87 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Number of Volumes
1 vol.
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
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