Associative memory,one of the major cognitive functions in the hippocampal CA3 region,includes auto-associative memory and hetero-associative memory.Many previous studies have shown that Alzheimer's disease(AD) ca...Associative memory,one of the major cognitive functions in the hippocampal CA3 region,includes auto-associative memory and hetero-associative memory.Many previous studies have shown that Alzheimer's disease(AD) can lead to loss of functional synapses in the central nervous system,and associative memory functions in patients with AD are often impaired,but few studies have addressed the effect of AD on hetero-associative memory in the hippocampal CA3 region.In this study,based on a simplified anatomical structure and synaptic connections in the hippocampal CA3 region,a three-layered Hopfield-like neural network model of hippocampal CA3 was proposed and then used to simulate associative memory functions in three circumstances:normal,synaptic deletion and synaptic compensation,according to Ruppin's synaptic deletion and compensation theory.The influences of AD on hetero-associative memory were further analyzed.The simulated results showed that the established three-layered Hopfield-like neural network model of hippocampal CA3 has both auto-associative and hetero-associative memory functions.With increasing synaptic deletion level,both associative memory functions were gradually impaired and the mean firing rates of the neurons within the network model were decreased.With gradual increasing synaptic compensation,the associative memory functions of the network were improved and the mean firing rates were increased.The simulated results suggest that the Hopfield-like neural network model can effectively simulate both associative memory functions of the hippocampal CA3 region.Synaptic deletion affects both auto-associative and hetero-associative memory functions in the hippocampal CA3 region,and can also result in memory dysfunction.To some extent,synaptic compensation measures can offset two kinds of associative memory dysfunction caused by synaptic deletion in the hippocampal CA3 area.展开更多
The global stability problem of Takagi-Sugeno(T-S) fuzzy Hopfield neural networks(FHNNs) with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarante...The global stability problem of Takagi-Sugeno(T-S) fuzzy Hopfield neural networks(FHNNs) with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism.Firstly,using both Finsler's lemma and an improved homogeneous matrix polynomial technique,and applying an affine parameter-dependent Lyapunov-Krasovskii functional,we obtain the convergent LMI-based stability criteria.Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique.Secondly,to further reduce the conservatism,a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs,which is suitable to the homogeneous matrix polynomials setting.Finally,two illustrative examples are given to show the efficiency of the proposed approaches.展开更多
The algorithm for VLSI channel routing using Hopfield neural model is discussed inthis paper.The basic methods of mapping VLSI channel routing problem to Hopfield neural net-work,constructing energy function,setting i...The algorithm for VLSI channel routing using Hopfield neural model is discussed inthis paper.The basic methods of mapping VLSI channel routing problem to Hopfield neural net-work,constructing energy function,setting initial neural status,and selecting various parametersare proposed.Finally,some experimental results are given.展开更多
In order to explore the structural features of neural networks and the ap-proaches to local interconnection,the geometrical structural information is introduced tothe Hopfield neural network model which is applied to ...In order to explore the structural features of neural networks and the ap-proaches to local interconnection,the geometrical structural information is introduced tothe Hopfield neural network model which is applied to associative memory.The dynamicsof the recalling is studied theoretically and cxpcrimcntally.The rcsults show that the geo-metrical structural information is helpless to the associative memory of monolayeredneural networks,furthermore,it makes the error probability increased.If the geometricalstructural information of the stored patterns is necessary to be introduced,somc new ap-proaches have to be explored.展开更多
基金the National Natural Science Foundation of China,No.30870649the Natural Science Foundation of Tianjin,No.08JCYBJC03300
文摘Associative memory,one of the major cognitive functions in the hippocampal CA3 region,includes auto-associative memory and hetero-associative memory.Many previous studies have shown that Alzheimer's disease(AD) can lead to loss of functional synapses in the central nervous system,and associative memory functions in patients with AD are often impaired,but few studies have addressed the effect of AD on hetero-associative memory in the hippocampal CA3 region.In this study,based on a simplified anatomical structure and synaptic connections in the hippocampal CA3 region,a three-layered Hopfield-like neural network model of hippocampal CA3 was proposed and then used to simulate associative memory functions in three circumstances:normal,synaptic deletion and synaptic compensation,according to Ruppin's synaptic deletion and compensation theory.The influences of AD on hetero-associative memory were further analyzed.The simulated results showed that the established three-layered Hopfield-like neural network model of hippocampal CA3 has both auto-associative and hetero-associative memory functions.With increasing synaptic deletion level,both associative memory functions were gradually impaired and the mean firing rates of the neurons within the network model were decreased.With gradual increasing synaptic compensation,the associative memory functions of the network were improved and the mean firing rates were increased.The simulated results suggest that the Hopfield-like neural network model can effectively simulate both associative memory functions of the hippocampal CA3 region.Synaptic deletion affects both auto-associative and hetero-associative memory functions in the hippocampal CA3 region,and can also result in memory dysfunction.To some extent,synaptic compensation measures can offset two kinds of associative memory dysfunction caused by synaptic deletion in the hippocampal CA3 area.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60974004)the Natural Science Foundation of Jilin Province,China (Grant No. 201115222)
文摘The global stability problem of Takagi-Sugeno(T-S) fuzzy Hopfield neural networks(FHNNs) with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism.Firstly,using both Finsler's lemma and an improved homogeneous matrix polynomial technique,and applying an affine parameter-dependent Lyapunov-Krasovskii functional,we obtain the convergent LMI-based stability criteria.Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique.Secondly,to further reduce the conservatism,a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs,which is suitable to the homogeneous matrix polynomials setting.Finally,two illustrative examples are given to show the efficiency of the proposed approaches.
文摘The algorithm for VLSI channel routing using Hopfield neural model is discussed inthis paper.The basic methods of mapping VLSI channel routing problem to Hopfield neural net-work,constructing energy function,setting initial neural status,and selecting various parametersare proposed.Finally,some experimental results are given.
文摘In order to explore the structural features of neural networks and the ap-proaches to local interconnection,the geometrical structural information is introduced tothe Hopfield neural network model which is applied to associative memory.The dynamicsof the recalling is studied theoretically and cxpcrimcntally.The rcsults show that the geo-metrical structural information is helpless to the associative memory of monolayeredneural networks,furthermore,it makes the error probability increased.If the geometricalstructural information of the stored patterns is necessary to be introduced,somc new ap-proaches have to be explored.
