Endowing robots with human-like emotional and cognitive abilities has garnered widespread attention, driving deep investigations into the complexities of these processes. However, few studies have examined the intricate circuits that govern the interplay between emotion and memory. This work presents a memristive circuit design that generates emotional memory, mimicking human emotional responses and memories while enabling interaction between emotions and cognition. Leveraging the hippocampal-brain emotion learning (BEL) architecture, the memristive circuit comprises seven comprehensive modules: the thalamus, sensory cortex, orbitofrontal cortex, amygdala, dentate gyrus (DG), CA3, and CA1. This design incorporates a compact biological framework, facilitating the collaborative encoding of emotional memories by the amygdala and hippocampus and allowing for flexible adjustment of circuit parameters to accommodate diverse personality traits. The proposed memristor-based circuit effectively mimics the complex interplay between emotions and memory, providing a valuable foundation for advancing the development of robots capable of replicating human-like emotional responses and cognitive integration.
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http://dx.doi.org/10.1016/j.neunet.2025.107276 | DOI Listing |
Nat Commun
March 2025
School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.
The practicality of memristor-based computation-in-memory (CIM) systems is limited by the specific hardware design and the manual parameters tuning process. Here, we introduce a software-hardware co-development approach to improve the flexibility and efficiency of the CIM system. The hardware component supports flexible dataflow, and facilitates various weight and input mappings.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
February 2025
The traditional association theory maintains that associations between cues can change only in trials where the cue is actually presented. However, the retrospective revaluation (RR) studies the phenomenon that responses to a cue can change even when the cue is not actually presented. A hardware memristor-based neural network circuit with an RR effect is proposed in this article.
View Article and Find Full Text PDFNeural Netw
June 2025
School of Integrated Circuits, Anhui University, Hefei, Anhui 230601, China. Electronic address:
Endowing robots with human-like emotional and cognitive abilities has garnered widespread attention, driving deep investigations into the complexities of these processes. However, few studies have examined the intricate circuits that govern the interplay between emotion and memory. This work presents a memristive circuit design that generates emotional memory, mimicking human emotional responses and memories while enabling interaction between emotions and cognition.
View Article and Find Full Text PDFNanomaterials (Basel)
January 2025
School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea.
Local learning algorithms, such as Equilibrium Propagation (EP), have emerged as alternatives to global learning methods like backpropagation for training neural networks. EP offers the potential for more energy-efficient hardware implementation by utilizing only local neuron information for weight updates. However, the practical implementation of EP using memristor-based circuits has significant challenges due to the immature fabrication processes of memristors, resulting in defects and variability issues.
View Article and Find Full Text PDFMater Horiz
February 2025
State Key Laboratory of Brain Machine Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Spiking neural networks (SNNs) represent a promising frontier in artificial intelligence (AI), offering event-driven, energy-efficient computation that mimics rich neural dynamics in the brain. However, running large-scale SNNs on mainstream computing hardware faces significant challenges to efficiently emulate these dynamical processes using synchronized and logical chips. Memristor based systems have recently demonstrated great potential for AI acceleration, sparking speculations and explorations of using these emerging devices for SNN tasks.
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