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 struc- ture 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.

Efficient Neural Architecture Search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing, but suffers from an issue of slow propagation speed of search model with deep topology. In this paper, we propose a Broad version for ENAS (BE-NAS) to solve the above issue, by learning broad architecture whose propagation speed is fast with reinforcement learning and parameter sharing used in ENAS, thereby achieving a higher search efficiency. In particular, we elaborately design Broad Convolutional Neural Network (BCNN), the search paradigm of BENAS with fast propagation speed, which can obtain a satisfactory performance with broad topology, i.e. fast forward and backward propagation speed. The proposed BCNN extracts multi-scale features and enhancement representations, and feeds them into global average pooling layer to yield more reasonable and comprehensive representations so that the achieved performance of BCNN with shallow topology can be promised. In order to verify the effectiveness of BENAS, several experiments are performed and experimental results show that 1) BENAS delivers 0.23 day which is 2x less expensive than ENAS, 2) the architecture learned by BENAS based small-size BCNNs with 0.5 and 1.1 millions parameters obtain state-of-the-art performance, 3.63% and 3.40% test error on CIFAR-10, 3) the learned architecture based BCNN achieves 25.3% top-1 error on ImageNet just using 3.9 millions parameters. 1 Introduction Recently, Neural Architecture Search (NAS) [25] which automates the process of model designing is gaining around in past several years. However, early approaches [20, 25, 26] suffer from the issue of inefficiency. To solve this issue, some one-shot approaches [1, 6, 11, 14, 19] are proposed.

Light, on the other hand, travels 186,282 miles in a second. Imagine the possibilities if we were that quick-witted. Well, computers are getting there. Researchers from UCLA on Thursday revealed a 3D-printed, optical neural network that allows computers to solve complex mathematical computations at the speed of light. In other words, we don't stand a chance.

An easily implementable path solution algorithm for 2D spatial problems, based on excitable/programmable characteristics of a specific cellular nonlinear network (CNN) model is presented and numerically investigated. The network is a single layer bioinspired model which was also implemented in CMOS technology. It exhibits excitable characteristics with regionally bistable cells. The related response realizes propagations of trigger autowaves, where the excitable mode can be globally preset and reset. It is shown that, obstacle distributions in 2D space can also be directly mapped onto the coupled cell array in the network. Combining these two features, the network model can serve as the main block in a 2D path computing processor. The related algorithm and configurations are numerically experimented with circuit level parameters and performance estimations are also presented. The simplicity of the model also allows alternative technology and device level implementation, which may become critical in autonomous processor design of related micro or nanoscale robotic applications.

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-Hattype 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.

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-Hattype 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.

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 isnot 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-Hattype 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.