Memristive field effect in a single and multilayer neural network with different connection topologies

Sriram, Sridevi; Rajagopal, Karthikeyan; Karthikeyan, Anitha; Akgül, Akif

doi:10.1016/j.amc.2023.128171

Cited by 5 publications

(1 citation statement)

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“…Researchers in this field are now exploring innovative techniques to further improve the scalability and reliability of memristor-based systems, aiming to integrate them into practical computing systems. Additionally, there is ongoing research into utilizing memristors for unconventional applications, such as cognitive computing and brain-inspired architectures, which hold great promise for the future [10][11][12][13]. As memristor technology continues to advance, it is poised to redefine the landscape of memory and computing, offering remarkable advantages that could revolutionize various industries and lead to the development of more efficient and intelligent electronic systems [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Applying exponential unit for breaking symmetry of memristive maps

Phu Thoai,

Volos,

Vincenzo Radogna

et al. 2024

Phys. Scr.

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The emergence of memristors has piqued significant interest in memristive maps due to their unique characteristics. In this paper, we introduce a novel and effective method for constructing memristor maps, leveraging the power of exponential units. Interestingly, the incorporation of these exponential units disrupts symmetry and alters the count of fixed points within the map. The method is simple to build maps with chaos and higher order maps. These make our work different from existing methods. To demonstrate the efficacy of our approach, we have focused our attention on examining the dynamics, feasibility, and practical applications of a specific map, referred to as the EPMM1 map. Furthermore, we show that by extending this approach, it becomes straightforward to create other innovative memristive maps, including those with multiple memristors.

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