A Neuromorphic Circuit With Supramodal Attention Effects Based on Cognitive Resource Limitation
When organisms face complex environments, the cognitive resource limitation is an important mechanism to ensure the quality of perceived information and prevent information overload. Organisms allocate cognitive resources rationally by regulating attention, thus promoting more important cognitive orientations. However, this phenomenon has been scarcely investigated within the realm of memristive biomimetic circuits. The prefrontal cortex (PFC), as the highest central hub for attention control, achieves goal-oriented attentional selection by assigning the basal ganglion to suppress irrelevant information. Based on this biological mechanism, a neuromorphic circuit has been designed in this paper to implement the attentional regulation function of the PFC between bimodal sensory inputs. When organisms face multi-sensory information input, the enhancement and inhibition effects in the supramodal attention effects are considered. In addition, the circuit realizes biological phenomena such as temporal consistency, semantic consistency, emotional attention, and attention fatigue. The attention regulation mechanism is further extended to more senses with variable sensitivities, providing variable strategies for performing tasks in different scenarios. Performance analysis results demonstrate that the circuit exhibits excellent robustness. This work provides guidance for the further development of information processing in brain-inspired intelligence.
Authors
- Herbert Ho‐Ching Iu (ORCID: https://orcid.org/0000-0002-0687-4038)
- Canfeng Zheng
- Gang Dou (ORCID: https://orcid.org/0000-0002-2631-1734)
- Mei Guo (ORCID: https://orcid.org/0000-0003-3002-9368)
Institutions
- The University of Western Australia (AU)
- Shandong University of Science and Technology (CN)
Publication Details
- Journal
- IEEE Transactions on Circuits and Systems I Regular Papers
- Published
- 2025-09-08
- DOI
- https://doi.org/10.1109/tcsi.2025.3605405
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73