The Experts below are selected from a list of 20559 Experts worldwide ranked by ideXlab platform
Kazuyuki Aihara - One of the best experts on this subject based on the ideXlab platform.
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Variable Timescales of Repeated Spike Patterns in Synfire Chain with Mexican-Hat Connectivity
Neural computation, 2007Co-Authors: Kosuke Hamaguchi, Kazuyuki AiharaAbstract:Repetitions of precise spike patterns observed both in vivo and in vitro have been reported for more than a decade. Studies on the spike volley (a pulse packet) propagating through a homogeneous Feedforward Network have demonstrated its capability of generating spike patterns with millisecond fidelity. This model is called the synfire chain and suggests a possible mechanism for generating repeated spike patterns (RSPs). The propagation speed of the pulse packet determines the temporal property of RSPs. However, the relationship between propagation speed and Network structure is not well understood. We studied a Feedforward Network with Mexican-hat connectivity by using the leaky integrate-and-fire neuron model and analyzed the Network dynamics with the Fokker-Planck equation. We examined the effect of the spatial pattern of pulse packets on RSPs in the Network with multistability. Pulse packets can take spatially uniform or localized shapes in a multistable regime, and they propagate with different speeds. These distinct pulse packets generate RSPs with different timescales, but the order of spikes and the ratios between interspike intervals are preserved. This result indicates that the RSPs can be transformed into the same template pattern through the expanding or contracting operation of the timescale.
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theory of localized synfire chain characteristic propagation speed of stable spike pattern
Neural Information Processing Systems, 2004Co-Authors: Kosuke Hamaguchi, Kazuyuki AiharaAbstract:Repeated spike patterns have often been taken as evidence for the synfire chain, a phenomenon that a stable spike synchrony propagates through a Feedforward Network. Inter-spike intervals which represent a repeated spike pattern are influenced by the propagation speed of a spike packet. However, the relation between the propagation speed and Network structure is not well understood. While it is apparent that the propagation speed depends on the excitatory synapse strength, it might also be related to spike patterns. We analyze a Feedforward Network with Mexican-Hat-type connectivity (FMH) using the Fokker-Planck equation. We show that both a uniform and a localized spike packet are stable in the FMH in a certain parameter region. We also demonstrate that the propagation speed depends on the distinct firing patterns in the same Network.
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NIPS - Theory of localized synfire chain: characteristic propagation speed of stable spike pattern
2004Co-Authors: Kosuke Hamaguchi, Kazuyuki AiharaAbstract:Repeated spike patterns have often been taken as evidence for the synfire chain, a phenomenon that a stable spike synchrony propagates through a Feedforward Network. Inter-spike intervals which represent a repeated spike pattern are influenced by the propagation speed of a spike packet. However, the relation between the propagation speed and Network structure is not well understood. While it is apparent that the propagation speed depends on the excitatory synapse strength, it might also be related to spike patterns. We analyze a Feedforward Network with Mexican-Hat-type connectivity (FMH) using the Fokker-Planck equation. We show that both a uniform and a localized spike packet are stable in the FMH in a certain parameter region. We also demonstrate that the propagation speed depends on the distinct firing patterns in the same Network.
Kosuke Hamaguchi - One of the best experts on this subject based on the ideXlab platform.
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Variable Timescales of Repeated Spike Patterns in Synfire Chain with Mexican-Hat Connectivity
Neural computation, 2007Co-Authors: Kosuke Hamaguchi, Kazuyuki AiharaAbstract:Repetitions of precise spike patterns observed both in vivo and in vitro have been reported for more than a decade. Studies on the spike volley (a pulse packet) propagating through a homogeneous Feedforward Network have demonstrated its capability of generating spike patterns with millisecond fidelity. This model is called the synfire chain and suggests a possible mechanism for generating repeated spike patterns (RSPs). The propagation speed of the pulse packet determines the temporal property of RSPs. However, the relationship between propagation speed and Network structure is not well understood. We studied a Feedforward Network with Mexican-hat connectivity by using the leaky integrate-and-fire neuron model and analyzed the Network dynamics with the Fokker-Planck equation. We examined the effect of the spatial pattern of pulse packets on RSPs in the Network with multistability. Pulse packets can take spatially uniform or localized shapes in a multistable regime, and they propagate with different speeds. These distinct pulse packets generate RSPs with different timescales, but the order of spikes and the ratios between interspike intervals are preserved. This result indicates that the RSPs can be transformed into the same template pattern through the expanding or contracting operation of the timescale.
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theory of localized synfire chain characteristic propagation speed of stable spike pattern
Neural Information Processing Systems, 2004Co-Authors: Kosuke Hamaguchi, Kazuyuki AiharaAbstract:Repeated spike patterns have often been taken as evidence for the synfire chain, a phenomenon that a stable spike synchrony propagates through a Feedforward Network. Inter-spike intervals which represent a repeated spike pattern are influenced by the propagation speed of a spike packet. However, the relation between the propagation speed and Network structure is not well understood. While it is apparent that the propagation speed depends on the excitatory synapse strength, it might also be related to spike patterns. We analyze a Feedforward Network with Mexican-Hat-type connectivity (FMH) using the Fokker-Planck equation. We show that both a uniform and a localized spike packet are stable in the FMH in a certain parameter region. We also demonstrate that the propagation speed depends on the distinct firing patterns in the same Network.
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NIPS - Theory of localized synfire chain: characteristic propagation speed of stable spike pattern
2004Co-Authors: Kosuke Hamaguchi, Kazuyuki AiharaAbstract:Repeated spike patterns have often been taken as evidence for the synfire chain, a phenomenon that a stable spike synchrony propagates through a Feedforward Network. Inter-spike intervals which represent a repeated spike pattern are influenced by the propagation speed of a spike packet. However, the relation between the propagation speed and Network structure is not well understood. While it is apparent that the propagation speed depends on the excitatory synapse strength, it might also be related to spike patterns. We analyze a Feedforward Network with Mexican-Hat-type connectivity (FMH) using the Fokker-Planck equation. We show that both a uniform and a localized spike packet are stable in the FMH in a certain parameter region. We also demonstrate that the propagation speed depends on the distinct firing patterns in the same Network.
Halbert White - One of the best experts on this subject based on the ideXlab platform.
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Correction: Some Asymptotic Results for Learning in Single Hidden-Layer Feedforward Network Models
Journal of the American Statistical Association, 1992Co-Authors: Halbert WhiteAbstract:I am indebted to Professor Vaclav Fabian for bringing to my attention an error in the statement of Proposition 3.1 and (consequently) Theorem 3.1 of White (1989). The error lies in the final conclusion of part (c) of Proposition 3.1, which reads: "Then as n oo, either fi tends to a local minimum of Q(O) with probability 1 or fin 0o with probability 1." The difficulty lies in the fact that the "limit" reached by fin will depend on the realized sequence of observations. Let the iid sequence {Zn } be defined on the probability space (Q, I, P), so that w E Q indexes a realization {Zn(w)}. As defined the recursive m estimator fin is a measurable function on Q X RI; a realization 0.(?w, Oo) depends on both c and the starting value bo E RI. Let 0W denote the union of { oo } with the set of all local minima of Q(O). A correct conclusion to Proposition 3.1 (c) can now be stated as follows: Then there exists a measurable mapping Ot: Q X R' -* such that for all c E c1, c1 E G, P(4) = 1, and O 0(, fO) Ot(,w, Oo) -* 0 as n -* oo. The measurability claimed here follows from, for example, Theorem 13.4 of Billingsley (1979). The final conclusion of Theorem 3.1 of White (1989) must be modified identically. Although they are correct as stated, Proposition 4.1, Theorem 4.1, Proposition 5. 1, Proposition 5.2, and Theorem 5.1 of White (1989) can be modified to apply more directly to the situation described in the corrected versions of Proposition 3.1 or Theorem 3.1. To illustrate the appropriate modification, we discuss only Theorem 4.1. In this theorem it is assumed that fi n* 0* almost surely, where 0* is an isolated stationary point of Q(0). Given the behavior of En discussed in the preceding paragraph, it is more relevant to define 4V 3 {(( bo) E Q X R'IOn(w, Oo) @.* 0* } for 0*, a given isolated stationary point of Q(0), and replace the assumption that fn -* 0* almost surely with the condition that (w, G0) E V*. The identical conclusion follows. Similar or identical modifications are appropriate in the other results mentioned, with any conclusions holding almost surely modified to reflect restriction to V*.
Anup Kumar Kolya - One of the best experts on this subject based on the ideXlab platform.
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detecting generic music features with single layer Feedforward Network using unsupervised hebbian computation
International Journal of Distributed Artificial Intelligence (IJDAI), 2020Co-Authors: Sourav Das, Anup Kumar KolyaAbstract:With the ever-increasing number of digital music and vast music track features through popular online music streaming software and apps, feature recognition using the neural Network is being used for experimentation to produce a wide range of results across a variety of experiments recently. Through this work, the authors extract information on such features from a popular open-source music corpus and explored new recognition techniques, by applying unsupervised Hebbian learning techniques on their single-layer neural Network using the same dataset. The authors show the detailed empirical findings to simulate how such an algorithm can help a single layer Feedforward Network in training for music feature learning as patterns. The unsupervised training algorithm enhances their proposed neural Network to achieve an accuracy of 90.36% for successful music feature detection. For comparative analysis against similar tasks, authors put their results with the likes of several previous benchmark works. They further discuss the limitations and thorough error analysis of their work. The authors hope to discover and gather new information about this particular classification technique and its performance, and further understand future potential directions and prospects that could improve the art of computational music feature recognition.
Suiyang Khoo - One of the best experts on this subject based on the ideXlab platform.
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robust single hidden layer Feedforward Network based pattern classifier
IEEE Transactions on Neural Networks, 2012Co-Authors: Zhihong Man, Kevin Lee, Dianhui Wang, Zhenwei Cao, Suiyang KhooAbstract:In this paper, a new robust single-hidden layer Feedforward Network (SLFN)-based pattern classifier is developed. It is shown that the frequency spectrums of the desired feature vectors can be specified in terms of the discrete Fourier transform (DFT) technique. The input weights of the SLFN are then optimized with the regularization theory such that the error between the frequency components of the desired feature vectors and the ones of the feature vectors extracted from the outputs of the hidden layer is minimized. For the linearly separable input patterns, the hidden layer of the SLFN plays the role of removing the effects of the disturbance from the noisy input data and providing the linearly separable feature vectors for the accurate classification. However, for the nonlinearly separable input patterns, the hidden layer is capable of assigning the DFTs of all feature vectors to the desired positions in the frequency-domain such that the separability of all nonlinearly separable patterns are maximized. In addition, the output weights of the SLFN are also optimally designed so that both the empirical and the structural risks are well balanced and minimized in a noisy environment. Two simulation examples are presented to show the excellent performance and effectiveness of the proposed classification scheme.
