Abstract
Currently, the research in memristor-based associative memory neural networks pays more attention to positive stimuli and lays less attention to negative stimuli. Negative stimuli are superior to positive stimuli in some ways, but lack the associated circuit implementation. In this paper, a memristor-based circuit design of avoidance learning with time delay is designed. The circuit can respond to a negative stimulus after initial avoidance learning and the effect of delay time between stimuli is considered. The realization of avoidance learning is confirmed in the PSPICE simulation results. In addition, an extended application circuit based on the memristor-based circuit design of avoidance learning with time delay is proposed. The application circuit is based on the advantage of negative stimuli is more difficult to forget than positive stimuli in associative memory. Based on the features of objects as input, the output of the circuit is used to achieve the function of avoidance learning. The application circuit provides more references for neural networks of automatic driving with further development.
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Bao B, Hu J, Bao H, Xu Q, Chen M (2024) Memristor-coupled dual-neuron mapping model: initials-induced coexisting firing patterns and synchronization activities. Cogn Neurodyn 18(2):539–555
Bolles RC, Grossen NE (1970) Function of the CS in shuttle-box avoidance learning by rats. J Comp Physiol Psychol 70(1p1):165
Chua L (1971) Memristor-the missing circuit element. IEEE Trans Circuit Theory 18(5):507–519
Dou G, Guo W, Kong L, Sun J, Guo M, Wen S (2024) Operant conditioning neuromorphic circuit with addictiveness and time memory for automatic learning. IEEE Trans Biomed Circuits Syst. https://doi.org/10.1109/TBCAS.2024.3388673
Du S, Deng Q, Hong Q, Wang C (2021) A memristor-based circuit design of Pavlov associative memory with secondary conditional reflex and its application. Neurocomputing 463:341–354
Fu J, Liao Z, Liu J, Smith SC, Wang J (2021) Memristor-based variation-enabled differentially private learning systems for edge computing in IoT. IEEE Internet Things J 8(12):9672–9682. https://doi.org/10.1109/JIOT.2020.3023623
Guo M, Kong L, Dou G, Iu HH-C (2024) Neuromorphic circuit of classical and operant conditioning based on tunable neural circuitry motifs. IEEE Trans Circuits Syst I Regul Pap. https://doi.org/10.1109/TCSI.2024.3431588
Guo M, Zhang D, Guo W, Dou G, Sun J (2024) Implementing brain-like fear generalization and emotional arousal associated with memory. IEEE Trans Cogn Dev Syst 45:1–12. https://doi.org/10.1109/TCDS.2024.3425845
Gupta RK, Choudhry MS, Saxena V, Taran S (2024) A single MOS-memristor emulator circuit. Circuits Syst Signal Process 43(1):54–73
Hong Q, Yan R, Wang C, Sun J (2020) Memristive circuit implementation of biological nonassociative learning mechanism and its applications. IEEE Trans Biomed Circuits Syst 14(5):1036–1050. https://doi.org/10.1109/TBCAS.2020.3018777
Hong Q, Yang L, Du S, Li Y (2022) Memristive recurrent neural network circuit for fast solving equality-constrained quadratic programming with parallel operation. IEEE Internet Things J 9(23):24560–24571. https://doi.org/10.1109/JIOT.2022.3189407
Ismail M, Rasheed M, Mahata C, Kang M, Kim S (2023) Mimicking biological synapses with a-HFSIOx-based memristor: implications for artificial intelligence and memory applications. Nano Converg 10(1):33
Kanagaraj S, Durairaj P, Karthikeyan A, Rajagopal K (2024) Unraveling the dynamics of a flux coupled Chialvo neurons and the existence of extreme events. Cognit Neurodyn 1–10
Kumar A, Bawa S (2020) A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services. Soft Comput 24(6):3909–3922
Li H, Li C, Du J (2023) Discretized locally active memristor and application in logarithmic map. Nonlinear Dyn 111(3):2895–2915
Mineka S (1979) The role of fear in theories of avoidance learning, flooding, and extinction. Psychol Bull 86(5):985–1010
Mostafa S, Amer FZ, ElKhatib RI, Mohamed M, ElKhatib A (2024) memristors modelling and simulation for digital to analog converter circuit. Russ Microlectron 53(2):188–195
Skinner FB (1951) How to teach animals. Sci Am 185(6):26–29
Staddon JE, Cerutti DT (2003) Operant conditioning. Annu Rev Psychol 54:115
Sun J, Wang Y, Liu P, Wen S, Wang Y (2023) Memristor-based neural network circuit with multimode generalization and differentiation on Pavlov associative memory. IEEE Trans Cybern 53(5):3351–3362. https://doi.org/10.1109/TCYB.2022.3200751
Sun J, Wang Y, Liu P, Wen S (2023) Memristor-based circuit design of pad emotional space and its application in mood congruity. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2023.3267778
Sun J, Li C, Wang Y, Wang Z (2024) Dynamic analysis of FN-HR neural network coupled of bistable memristor and encryption application based on fibonacci q-matrix. Cognit Neurodyn 25:1–18
Wang Z, Wang X (2018) A novel memristor-based circuit implementation of full-function pavlov associative memory accorded with biological feature. IEEE Trans Circuits Syst I Regul Pap 65(7):2210–2220. https://doi.org/10.1109/TCSI.2017.2780826
Wang Z, Wang X, Lu Z, Wu W, Zeng Z (2020) The design of memristive circuit for affective multi-associative learning. IEEE Trans Biomed Circuits Syst 14(2):173–185
Wang Z, Wang X, Lu Z, Wu W, Zeng Z (2020) The design of memristive circuit for affective multi-associative learning. IEEE Trans Biomed Circuits Syst 14(2):173–185
Wang Z, Wang X, Zeng Z (2021) Memristive circuit design of brain-like emotional learning and generation. IEEE Trans Cybern 53(1):222–235
Wen S, Xie X, Yan Z, Huang T, Zeng Z (2018) General memristor with applications in multilayer neural networks. Neural Netw 103:142–149
Xie Y, Ye Z, Li X, Wang X, Jia Y (2024) A novel memristive neuron model and its energy characteristics. Cogn Neurodyn 42:1–13
Zhang Y, Wang X, Li Y, Friedman EG (2016) Memristive model for synaptic circuits. IEEE Trans Circuits Syst II Express Briefs 64(7):767–771
Zhang Y, Li Y, Wang X, Friedman EG (2017) Synaptic characteristics of Ag/AgInSbTe/Ta-based memristor for pattern recognition applications. IEEE Trans Electron Devices 64(4):1806–1811
Zhang Y, Lv J, Zeng Z (2022) The framework and memristive circuit design for multisensory mutual associative memory networks. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2022.3227161
Zhou W, Wen S, Liu Y, Liu L, Liu X, Chen L (2023) Forgetting memristor based STDP learning circuit for neural networks. Neural Netw 158:293–304. https://doi.org/10.1016/j.neunet.2022.11.023
Zhou H, Li S, Ang K-W, Zhang Y-W (2024) Recent advances in in-memory computing: exploring memristor and memtransistor arrays with 2D materials. Nano-Micro Lett 16(1):121
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 62276239 and 62272424, in part by Zhongyuan Talents Program under Grant ZYYCYU202012154, and in part by Henan Natural Science Foundation-Outstanding Youth Foundation under Grant 222300420095.
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Sun, J., Wang, H., Xu, Y. et al. A memristor-based circuit design of avoidance learning with time delay and its application. Cogn Neurodyn (2024). https://doi.org/10.1007/s11571-024-10173-2
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DOI: https://doi.org/10.1007/s11571-024-10173-2