Abstract Intelligence

Mizoguchi, Fumio; Wang, Yingxu; Patel, Shushma; Raskin, Victor; Tsumoto, Shusaku; Baciu, George; Widrow, Bernard; Zadeh, Lotfi A.; Howard, Newton; Beaufays, Françoise; Hsu, David; Luo, Guangsheng; Wei, Wei; Zhang, Du

doi:10.4018/ijcini.2017010101

Cited by 29 publications

(1 citation statement)

References 83 publications

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“…It can be beneficial to have neurons wired together as it can help us to learn, store and recall information in an effective way. It is established that sensory perceptions and abstract thoughts of human corresponds to the activity of cell assemblies (Wang et al, 2017). The question arises "How the cell assemblies represent, memorize and compute the information".…”

Section: Introductionmentioning

confidence: 99%

A Pragmatic Approach to Understand Hebbian Cell Assembly

Cherukuri

Shivhare

Abraham

et al. 2021

International Journal of Cognitive Informatics and Natural Intelligence

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Formed at the cerebral cortex, neuron cell assemblies are regarded as basic units in cortical representation. Proposed by Hebb, these cell assemblies are regarded as the distributed neural representation of relevant objects, concepts or constellations. Each cell assembly contains a group of neurons having strong mutual excitatory connections. During a stimulus, these cells get activated. This activation either performs a given action or represent a given percept or concept in brain. This theory is in the strongest connection of the problem of concept forming in the brain. The challenge is to model coordinated activity among neurons in brain mathematically. The need of modelling it mathematically enables this paper to give clear view of functionality of Hebbian cell assembly. Therefore this paper proposes a pragmatic approach to Hebbian cell assemblies using mathematical model grounded in lattice based formalism that utilizes Galois connections. During this proposal, the authors also show the connections of the proposal to cognitive model of memory in particularly long-term memory (LTM).

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Section: Introductionmentioning

confidence: 99%

A Pragmatic Approach to Understand Hebbian Cell Assembly

Cherukuri

Shivhare

Abraham

et al. 2021

International Journal of Cognitive Informatics and Natural Intelligence

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Abstract Intelligence

Sadiku

Musa

2021

A Primer on Multiple Intelligences

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Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

González-Ruiz

Peña

Grisales

et al. 2019

Applied Soft Computing

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In general, the development of economic infrastructure systems requires a behavioural comprehensive analysis of different financial variables or rates to establish its long-term success with regards to the Equity Internal Rate of Return (EIRR) expectation. For this reason, several financial organizations have developed economic scenarios supported by computational techniques and models to identify the evolution of these financial rates. However, these models and techniques have shown a series of limitations with regard to the financial management process and its impact on EIRR over time. To address these limitations in an inclusive way, researchers have developed different approaches and methodologies focused on the development of financial models using stochastic simulation methods and computational intelligence techniques. This paper proposes a Stochastic Fuzzy Logistic Model (S-FLM) inspired by a Fuzzy Cognitive Map (FCM) structure to model financial scenarios. Where the input consists in financial rates that are characterized as linguistic rates through a series of adaptive logistic functions. The stochastic process that explains the behaviour of the financial rates over time and their partial effects on EIRR is based on a Monte Carlo sampling process carried out on the fuzzy sets that characterize each linguistic rate. The S-FLM was evaluated by applying three financing scenarios to an airport infrastructure system (pessimistic, moderate/base, optimistic), where it was possible to show the impact of different linguistic rates on the EIRR. The behaviour of the S-FLM was validated using three different models: (1) a financial management tool; (2) a general FCM without pre-loaded causalities among the variables; and (3) a Statistical S-FLM model (S-FLMS), where the causalities between the concepts or rates were obtained as a result of an independent effects analysis applying a cross modelling between variables and by using a statistical multi-linear model (statistical significance level) and a multi-linear neural model (MADALINE). The results achieved by the S-FLM show a higher EIRR than expected for each scenario. This was possible due to the incorporation of an adaptive multi-linear causality matrix and a fuzzy credibility matrix into its structure. This allowed to stabilize the effects of the financial variables or rates on the EIRR throughout a financing period. Thus, the S-FLM can be considered as a tool to model dynamic financial scenarios in different knowledge areas in a comprehensive manner. This way, overcoming the limitations imposed by the traditional computational models used to design these financial scenarios.

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Abstract Intelligence

Cited by 29 publications

References 83 publications

A Pragmatic Approach to Understand Hebbian Cell Assembly

A Pragmatic Approach to Understand Hebbian Cell Assembly

Abstract Intelligence

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

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