Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space

Iranmehr, Ensieh; Shouraki, Saeed Bagheri; Faraji, Mohammad Mahdi; Bagheri, Niloofar; Linares-Barranco, B.

doi:10.3389/fnins.2019.01085

Cited by 18 publications

(9 citation statements)

References 62 publications

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“…The scientific truth about the existence of a finite interaction speed, in general, was recently confirmed by providing experimental evidence for the existence of gravitational waves. 2 The experimental evidence also indirectly underpins that the mathematical background of the Minkowski transform is well-established and correctly describes nature. Modern treatments of special relativity base it on the single postulate of Minkowski spacetime [6].…”

Section: Introductionmentioning

confidence: 90%

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On the Spatiotemporal Behavior in Biology-Mimicking Computing Systems

Végh¹,

Berki²

2020

Preprint

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Both the growing demand to cope with "big data" (based on, or assisted by, artificial intelligence) and the interest in understanding the operation of our brain more completely, stimulated the efforts to build biology-mimicking computing systems from inexpensive conventional components and build different ("neuromorphic") computing systems. On one side, those systems require an unusually large number of processors, which introduces performance limitations and nonlinear scaling. On the other side, the neuronal operation drastically differs from the conventional workloads. The conduction time (transfer time) is ignored in both in conventional computing and "spatiotemporal" computational models of neural networks, although von Neumann warned: "In the human nervous system the conduction times along the lines (axons) can be longer than the synaptic delays, hence our procedure of neglecting them aside of the processing time would be unsound" [1], section 6.3. This difference alone makes imitating biological behavior in technical implementation hard. Besides, the recent issues in computing called the attention to that the temporal behavior is a general feature of computing systems, too. Some of their effects in both biological and technical systems were already noticed. Instead of introducing some "looks like" models, the correct handling of the transfer time is suggested here. Introducing the temporal logic, based on the Minkowski transform, gives quantitative insight into the operation of both kinds of computing systems, furthermore provides a natural explanation of decades-old empirical phenomena. Without considering their temporal behavior correctly, neither effective implementation nor a true imitation of biological neural systems are possible.

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Section: Introductionmentioning

confidence: 90%

“…The biological computing uses a description for the experienced "spatiotemporal" behavior, where the "space" and "time" are handled mathematically as separated functions. As a consequence, "the available models cannot be used for machine learning and or recognizing spatiotemporal patterns", see [2] and its cited references.…”

Section: Introductionmentioning

confidence: 99%

On the Spatiotemporal Behavior in Biology-Mimicking Computing Systems

Végh¹,

Berki²

2020

Preprint

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“…The biological computing uses a description for the experienced "spatiotemporal" behavior, where the "space" and "time" are handled mathematically as separated functions. As a con-sequence, "the available models cannot be used for machine learning and or recognizing spatiotemporal patterns", see [2] and its cited references.…”

Section: Introductionmentioning

confidence: 99%

“…The measurable parameters of an event change their value both in time and space, and we see the same event at a different time in a properly chosen place. This common experience is expressed with the wording, that the systems show a "spatiotemporal" behavior [3,2]. The used phraseology is closely related to the "space-time" coordinates, introduced by Minkowski.…”

Section: Introductionmentioning

confidence: 99%

On the Spatiotemporal Behavior in Biology-Mimicking Computing Systems

Végh

Berki

2021

Preprint

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Both the growing demand to cope with ”big data” (based on, or assisted by, artiﬁcial intelligence) and the interest in understanding the operation of our brain more completely, stimulated the eﬀorts to build biology-mimicking computing systems from inexpensive conventional components and build diﬀerent (”neuro-morphic”) computing systems. On one side, those systems require an unusually large number of processors, which introduces performance limitations and nonlinear scaling. On the other side, the neuronal operation drastically diﬀers from the conventional workloads. The conduction time (transfer time) is ignored in both in conventional computing and ”spatiotemporal” computational models of neural networks, although von Neumann warned: ”In the human nervous system the con-duction times along the lines (axons) can be longer than the synaptic delays, hence our above procedure of neglecting them aside of τ [the processing time] would be unsound” [1], section 6.3. This diﬀerence alone makes imitating biological behavior in technical implementation hard. Besides, the recent issues in computing called the attention to that the temporal behavior is a general feature of computing systems, too. Some of their eﬀects in both biological and technical systems were al-ready noticed. Instead of introducing some ”looks like” models, the correct handling of the transfer time is suggested here. Introducing the temporal logic, based on the Minkowski transform, gives quantitative insight into the operation of both kinds of computing systems, furthermore provides a natural explanation of decades-old empirical phenomena. Without considering their temporal behavior correctly, neither eﬀective implementation nor a true imitation of biological neural systems are possible.

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“…Depending on the data type and the problem to be solved, different algorithms have been developed so far. Among them, feed-forward [41], [42], recurrent [43] and reservoir neural networks [44], [45], [46] should be mentioned as powerful tools to handle labelled data. If we encounter a problem that data labels are not available, we can benefit of some other techniques such as dimension reduction [47], clustering [48], [49] to recognize patterns.…”

Section: Introductionmentioning

confidence: 99%

Developing a Data-Driven Unsupervised Pattern Recognition Approach for Sensor Signal Anomaly Detection

Iranmehr¹,

Ferreira²,

Böhnert³

et al. 2021

Preprint

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Coming up with a system for early detection of machine damages and failures is one of the important challenges in the industrial maintenance procedure to avoid additional costs and downtimes. To approach this goal, this paper uses the signal gathered by a sensing system which employed a spintropic sensor to measure the magnetic field around the machine which somehow shows the machine's behaviour. Using this signal and focusing on analysing and processing the signal, this paper develops a data-driven method to recognize signal patterns and subsequently detects anomalies. A challenging task that we succeeded to overcome in this paper is recognizing relevant signal patterns without having any prior knowledge. An algorithm designed for this task is therefore completely unsupervised which makes it consistent and suitable to apply it for the signals gathered for other types of machines. Using both frequency and time domain information, the proposed algorithm, which utilizes signal processing and machine learning techniques, is able to efficiently identify relevant signal patterns. Clustering results on the real data gathered by the aforementioned sensor have shown the high accuracy of 99.38% in recognizing patterns. Furthermore, an anomaly score measure is used and according to its distribution, anomalies are detected appropriately. <br>

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Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space

Cited by 18 publications

References 62 publications

On the Spatiotemporal Behavior in Biology-Mimicking Computing Systems

On the Spatiotemporal Behavior in Biology-Mimicking Computing Systems

On the Spatiotemporal Behavior in Biology-Mimicking Computing Systems

Developing a Data-Driven Unsupervised Pattern Recognition Approach for Sensor Signal Anomaly Detection

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