Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2005.863457
Title: Dynamics analysis and analog associative memory of networks with LT neurons
Authors: Tang, H.
Tan, K.C. 
Teoh, E.J.
Keywords: Associative memory
Continuous real-valued patterns
Dynamics analysis
Linear threshold (LT) network
Issue Date: Mar-2006
Source: Tang, H., Tan, K.C., Teoh, E.J. (2006-03). Dynamics analysis and analog associative memory of networks with LT neurons. IEEE Transactions on Neural Networks 17 (2) : 409-418. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2005.863457
Abstract: The additive recurrent network structure of linear threshold neurons represents a class of biologically-motivated models, where nonsaturating transfer functions are necessary for representing neuronal activities, such as that of cortical neurons. This paper extends the existing results of dynamics analysis of such linear threshold networks by establishing new and milder conditions for boundedness and asymptotical stability, while allowing for multistability. As a condition for asymptotical stability, it is found that boundedness does not require a deterministic matrix to be symmetric or possess positive off-diagonal entries. The conditions put forward an explicit way to design and analyze such networks. Based on the established theory, an alternate approach to study such networks is through permitted and forbidden sets. An application of the linear threshold (LT) network is analog associative memory, for which a simple design method describing the associative memory is suggested in this paper. The proposed design method is similar to a generalized Hebbian approach, but with distinctions of additional network parameters for normalization, excitation and inhibition, both on a global and local scale. The computational abilities of the network are dependent on its nonlinear dynamics, which in turn is reliant upon the sparsity of the memory vectors. © 2006 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/55733
ISSN: 10459227
DOI: 10.1109/TNN.2005.863457
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