Role of Itinerancy Among Attractors as Dynamical Map in Distributed Coding Scheme

Hoshino, Osamu; Usuba, Noriaki; Kashimori, Yoshiki; Kambara, Takeshi

doi:10.1016/s0893-6080(97)00022-1

Cited by 35 publications

(28 citation statements)

References 54 publications

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“…This map is a relatively simple discrete time random dynamical system on the interval, obtained from a neural network justified by the findings of [12] on modeling the neurocortex with a variant of Hopfield's asynchronous recurrent neural network presented in [9]. In Hopfield's network, memories are represented by stable attractors and an unlearning mechanism is suggested in [10] to account for unpinning of these states (see also, e.g., [11]). In the network presented in [12], however, these are replaced by Milnor attractors, which appear due to a combination of symmetrical and asymmetrical couplings and some resetting mechanism.…”

Section: Introductionmentioning

confidence: 68%

Chaotic Itinerancy in an Associative Memory Model

Galatolo¹,

Monge²,

Marangio³

et al. 2018

Preprint

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We consider a random dynamical system arising in a model of associative memory. This system can be seen as a small (stochastic and deterministic) perturbation of a determinstic system having two weak attractors which are destroyed after the perturbation. We show, with a computer aided proof, that the system has a kind of chaotic itineracy. Typical orbits are globally chaotic, while they spend relatively long time visiting attractor's ruins.

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Section: Introductionmentioning

confidence: 68%

Chaotic Itinerancy in an Associative Memory Model

Galatolo¹,

Monge²,

Marangio³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the real dynamic property of the present point attractor differs from the ideal one (Hoshino et al 1997(Hoshino et al , 1998. That is, when the network state is within the basin of the point attractor corresponding to Xn, the firing pattern of the network fluctuates randomly around the pattern fn i ðXnÞg.…”

Section: Formation Of Cognitive Mapsmentioning

confidence: 90%

“…That is, when the network state is within the basin of the point attractor corresponding to Xn, the firing pattern of the network fluctuates randomly around the pattern fn i ðXnÞg. Dynamic properties of these point attractors have been shown in detail (Hoshino et al 1997(Hoshino et al , 1998.…”

Section: Formation Of Cognitive Mapsmentioning

confidence: 99%

“…Figure 2b shows the ongoing state of the IT and PC networks, which is characterized by random itinerancy among point attractors. The dynamic representation of cortical networks by random or cyclic itinerancy is called a ''dynamical map'' (Hoshino et al 1997(Hoshino et al , 1998(Hoshino et al , 2001, which could be interpreted as a ready state for sensory input and has been proposed as one of the cortical sensory mapping schemes.…”

Section: Structure Of the Modelmentioning

confidence: 99%

“…The value of c is 10 for the memorization process and 0.1 for the categoryassociation task. Such high intensity (c ¼ 10) is necessary for the creation of dynamical maps and corresponds to the sensitivity increase that may arise from some kinds of behaviorally attentional processes (Hoshino et al 1997(Hoshino et al , 1998.…”

Section: Formation Of Cognitive Mapsmentioning

confidence: 99%

See 2 more Smart Citations

Roles of dynamic linkage of stable attractors across cortical networks in recalling long-term memory

Hoshino

Zheng

Kuroiwa

2003

Biological Cybernetics

Self Cite

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We propose a neural network model for a category-association task. By simulating the model, neuronal relevance of cortical interactions to recalling long-term memory was investigated. The model consists of the left and right hemispheres, each of which has IT (inferotemporal cortex) and PC (prefrontal cortex) networks. Information about visual features and their categories were encoded into point attractors of the IT and PC networks, respectively. In the task, the IT network of the right hemisphere was stimulated with a cue feature. After a delay period, the IT network of the left hemisphere was simultaneously stimulated with the choice feature and an irrelevant feature. The cue and choice features belong to the same category, while the irrelevant feature belongs to another category. To complete the task, the IT network must select the point attractor corresponding to the choice feature. We demonstrate that the top-down pathway (PC-to-IT) triggers the retrieval of long-term memory of the choice feature from the IT, and the bottom-up pathway (IT-to-PC) contributes to the maintenance of the retrieved memory during the delay period. The key mechanism for the retrieval and maintenance of that memory is the dynamic linkage of attractors across separate cortical networks. We show that a single hemisphere is sufficient for the memory retrieval, but it is advantageous to use the two hemispheres because the retrieved memory is thereby retained with greater reliability until the brain chooses the choice feature.

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Chaotic Itinerancy for Patterns Separation

Matykiewicz

2004

Lecture Notes in Computer Science

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Role of Itinerancy Among Attractors as Dynamical Map in Distributed Coding Scheme

Cited by 35 publications

References 54 publications

Chaotic Itinerancy in an Associative Memory Model

Chaotic Itinerancy in an Associative Memory Model

Roles of dynamic linkage of stable attractors across cortical networks in recalling long-term memory

Chaotic Itinerancy for Patterns Separation

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