Plastic neural network with transmission delays promotes equivalence between function and structure

Protachevicz, Paulo R.; Borges, Fernando S.; Batista, Antônio M.; Baptista, Murilo da Silva; Caldas, Iberê L.; Macau, Elbert E. N.; Lameu, Ewandson L.

doi:10.1016/j.chaos.2023.113480

Cited by 4 publications

(2 citation statements)

References 64 publications

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“…The STDP mechanism states that the synaptic strength of each synapse is updated using a nearest-spike pair-based STDP rule [66] as time t increases. There are two commonly used forms of STDP (see, e.g., [25,67,68] and [69][70][71]) for each of the forms. In our study, the update of the synaptic coupling strength g ij (t) is determined by the synaptic modification function M, which is defined based on the current value of g ij (t) [69][70][71]:…”

Section: Synapses and Stdp Rulementioning

confidence: 99%

“…It is essential to consider the effects of the inherently adaptive nature of neural networks on information processing via synchronization. Besides the colossal efforts to study synchronization in neuronal networks with synaptic plasticity (see, e.g., [22,[25][26][27][28][29]), it is essential to be mindful of the need to explore more dynamic scenarios in order to fully comprehend the emergence of synchronous patterns in adaptive networks. Synaptic plasticity in neural networks refers to the ability to modify the strength of synaptic couplings over time and/or the architecture of neural network topology through specific rules.…”

Section: Introductionmentioning

confidence: 99%

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Synchronization in STDP-driven memristive neural networks with time-varying topology

Yamakou,

Desroches,

Rodrigues

2023

J Biol Phys

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Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by spike-timing-dependent plasticity (STDP) and temporal networks subject to homeostatic structural plasticity (HSP) rules remain unclear. Here, we bridge this gap by determining the configurations required to achieve high and stable degrees of complete synchronization (CS) and phase synchronization (PS) in time-varying small-world and random neural networks driven by STDP and HSP. In particular, we found that decreasing P (which enhances the strengthening effect of STDP on the average synaptic weight) and increasing F (which speeds up the swapping rate of synapses between neurons) always lead to higher and more stable degrees of CS and PS in small-world and random networks, provided that the network parameters such as the synaptic time delay $$\tau _c$$ τ c , the average degree $$\langle k \rangle$$ ⟨ k ⟩ , and the rewiring probability $$\beta$$ β have some appropriate values. When $$\tau _c$$ τ c , $$\langle k \rangle$$ ⟨ k ⟩ , and $$\beta$$ β are not fixed at these appropriate values, the degree and stability of CS and PS may increase or decrease when F increases, depending on the network topology. It is also found that the time delay $$\tau _c$$ τ c can induce intermittent CS and PS whose occurrence is independent F. Our results could have applications in designing neuromorphic circuits for optimal information processing and transmission via synchronization phenomena.

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Section: Synapses and Stdp Rulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synchronization in STDP-driven memristive neural networks with time-varying topology

Yamakou,

Desroches,

Rodrigues

2023

J Biol Phys

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Design of the non‐associative learning memristive circuit and the application to brightness adaptation

Lin,

Zhou,

Zhou

et al. 2024

Circuit Theory & Apps

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Non‐associative learning plays an important role in the biological and cognitive systems and is one of the important pathways for unsupervised learning in biology. The proposed non‐associative learning memristive circuit is composed of a synapse module, a memristive neuron module, and a response boosting module. The circuit can simulate the characteristics of habituation and sensitization, which attenuates the output response when receiving repeated input stimuli and augments the output response when receiving strong input stimuli, and the circuit also has a certain anti‐interference ability. Additionally, the designed circuit is suitable for adaptive replication experiments in visual systems and automatic exposure compensation systems, demonstrating the practicality of the circuit.

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Dynamic modulation of external excitation enhance synchronization in complex neuronal network

Wu,

Ding,

Huang

et al. 2024

Chaos, Solitons & Fractals

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Plastic neural network with transmission delays promotes equivalence between function and structure

Cited by 4 publications

References 64 publications

Synchronization in STDP-driven memristive neural networks with time-varying topology

Synchronization in STDP-driven memristive neural networks with time-varying topology

Design of the non‐associative learning memristive circuit and the application to brightness adaptation

Dynamic modulation of external excitation enhance synchronization in complex neuronal network

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