Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Jonathan eTapson"'
Autor:
Gregory Kevin Cohen, Garrick eOrchard, Sio Hoi eIeng, Jonathan eTapson, Ryad Benjamin Benosman, André evan Schaik
Publikováno v:
Frontiers in Neuroscience, Vol 10 (2016)
The growing demands placed upon the field of computer vision has renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame
Externí odkaz:
https://doaj.org/article/99b121db1e5b4e6a9fa7273bc9738d49
Autor:
Chetan Singh Thakur, Saeed eAfshar, Runchun Mark Wang, Tara Julia Hamilton, Jonathan eTapson, André evan Schaik
Publikováno v:
Frontiers in Neuroscience, Vol 10 (2016)
In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations
Externí odkaz:
https://doaj.org/article/5d0f21d40b144ed7ad392349ba3d5ccf
Sound stream segregation: a neuromorphic approach to solve the ‘cocktail party problem’ in real-time
Autor:
Chetan Singh Thakur, Runchun Mark Wang, Saeed eAfshar, Tara Julia Hamilton, Jonathan eTapson, Shihab eShamma, André evan Schaik
Publikováno v:
Frontiers in Neuroscience, Vol 9 (2015)
The human auditory system has the ability to segregate complex auditory scenes into a foreground component and a background, allowing us to listen to specific speech sounds from a mixture of sounds. Selective attention plays a crucial role in this pr
Externí odkaz:
https://doaj.org/article/fae4c3288d444bff9677a49a11662484
Publikováno v:
Frontiers in Neuroscience, Vol 9 (2015)
We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP) and Spike Timing Dependent Delay Plasticity (STDDP). We present a fully digital implementation as wel
Externí odkaz:
https://doaj.org/article/0376f3db89c24d9c84e9ec38ed4915c4
Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron with Adaptive Kernels
Publikováno v:
Frontiers in Neuroscience, Vol 8 (2014)
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investi
Externí odkaz:
https://doaj.org/article/8544a92c4d8a478ca74537ed578e8613
Publikováno v:
Frontiers in Neuroscience, Vol 8 (2014)
We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Depend
Externí odkaz:
https://doaj.org/article/37011ca91733497195427dfdbc1d4c04
Autor:
Saeed eAfshar, Greg Kevin Cohen, Runchun Mark Wang, André evan Schaik, Jonathan eTapson, Torsten eLehmann, Tara Julia Hamilton
Publikováno v:
Frontiers in Neuroscience, Vol 7 (2013)
We present the biologically inspired Ripple Pond Network (RPN), a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns suitable for recognition by temporal codi
Externí odkaz:
https://doaj.org/article/32b0c084bd2c4db6b9f062403cbf1d90
Publikováno v:
Frontiers in Cellular Neuroscience, Vol 7 (2013)
We measured the action potential backpropagation delays in apical dendrites of layer 5 pyramidal neurons of the somatosensory cortex under different stimulation regimes that exclude synaptic involvement. These delays showed robust features and did no
Externí odkaz:
https://doaj.org/article/40e8304cd1d040188e34b66615ec16f8