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IEEE TRANSACTIONS ON neural NETWORKS AND LEARNING SYSTEMS 2012年第6期23卷 916-927页

作者： Ranhel, Joao Univ Sao Paulo Polytech Sch BR-05508970 Sao Paulo Brazil

Spiking neurons can realize several computational operations when firing cooperatively. This is a prevalent notion, although the mechanisms are not yet understood. A way by which neural assemblies compute is proposed in this paper. It is shown how neural coalitions represent things (and world states), memorize them, and control their hierarchical relations in order to perform algorithms. It is described how neural groups perform statistic logic functions as they form assemblies. neural coalitions can reverberate, becoming bistable loops. Such bistable neural assemblies become short-or long-term memories that represent the event that triggers them. In addition, assemblies can branch and dismantle other neural groups generating new events that trigger other coalitions. Hence, such capabilities and the interaction among assemblies allow neural networks to create and control hierarchical cascades of causal activities, giving rise to parallel algorithms. computing and algorithms are used here as in a nonstandard computation approach. In this sense, neural assembly computing (NAC) can be seen as a new class of spiking neural network machines. NAC can explain the following points: 1) how neuron groups represent things and states;2) how they retain binary states in memories that do not require any plasticity mechanism;and 3) how branching, disbanding, and interaction among assemblies may result in algorithms and behavioral responses. Simulations were carried out and the results are in agreement with the hypothesis presented. A MATLAB code is available as a supplementary material.

关键词： Bistable neural assemblies branching dismantling neural assembly computing neural coalition polychronous groups spiking neural networks

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A neural assembly-Based State Machine Design 67

A Neural Assembly-Based State Machine Design

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67th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Rahmatinezhad, Mitra Ahmadi, Arash Ahmadi, Majid Carleton Univ Dept Elect Ottawa ON Canada Univ Windsor Dept Elect & Comp Engn Windsor ON Canada

ISBN: (纸本)9798350387186;9798350387179

neural assembly computing (NAC) is a framework that employs spiking neural networks to model the computational processes of biological neural cell assemblies. In this paper, we propose a method to design state machines based on NAC, capable of generating complex and intelligent behaviors. This study, as an example, presents a simple algorithm for recognizing a sequence of distinct symbols (geometric shapes) by utilizing four states that mirror the activity of neural assemblies. Through simulation outcomes, we demonstrate that NAC provides a biologically realistic and efficient strategy for implementing state machines and algorithms.

关键词： neural assembly computing Spiking neural Network Cell Assemblies Sequential Machines State Machines

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neural Assemblies and Finite State Automata

Neural Assemblies and Finite State Automata

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1st BRICS Countries Congress on Computational Intelligence / 11th Brazilian Congress on Computational Intelligence (BRICS-CCI and CBIC)

作者： Ranhel, Joao UFPE Univ Fed Pernambuco Dept Elect & Syst Recife PE Brazil

ISBN: (纸本)9781479931941

neural assembly computing (NAC) is a framework for investigating computational operations realized by spiking cell assemblies and for designing spiking neural machines. NAC concerns the way assemblies interact and how it results in information processing with causal and hierarchical relations. In addition, NAC investigates how assemblies represent states of the world, how they control data flux carried by spike streaming, how they create parallel processes by branching and dismantling other assemblies, how they reverberate and create memory loops, among other issues. As cell coalitions interact they realize logical functions. Memory loops and logical functions are the elements engineers use to create finite state machines (FSM). An overview of NAC is provided, a methodology for implementing FSM in NAC is presented in this paper, a finite state automaton is designed, and a simulation and respective results are shown. Supplemental materials are available for download. Discussions about how FSM on NAC and how NAC itself can contribute for designing new types of spiking neural machines are presented.

关键词： neural assembly computing cell ensembles finite state machines neural coalition finite-state automaton

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Variable Time Delays and Representational Capacity in Sparsely Connected Populations of Spiking Neurons

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Procedia Computer Science 2013年 20卷 22-26页

作者： Corey B. Hart Lockheed Martin IS&GS Advanced Technologies and Innovations 230 Mall Blvd King of Prussia PA 19406 USA

Successive generations of artificial neural networks have leveraged their multiplicity of connections and weights for significant improvements in information processing capability and memory capacity. The most recent generation of artificial neural networks, third generation networks, consist of spiking neuron models that attempt to mimic the complex dynamic features exhibited by real biological neurons in the hopes of improvements in computational and representational capacities. While the theoretical capabilities of these networks are impressive, understanding the nature and extent of their computational advantages, and the appropriate network architectures and algorithms necessary for their successful exploitation, have lagged far behind the theory. With this in mind, we herein explore the representational capacity of two related forms of neural networks: synfire chains, and polychronic networks. We find that the computational capacity of such cellular assembly based networks increases with the size of between-neural-pool time delays and that for relatively small changes in time delay, linear increases in network representational capacities are obtained.

关键词： Artificial neural networks Spiking neurons neural assembly computing

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