ePoster · Computational Neuroscience
Co-Design of Analog Neuromorphic Systems and Cortical Motifs with Local Dendritic Learning Rules
Maryada Maryada, Chiara De Luca, Arianna Rubino, Chenxi Wen, Melika Payvand, Giacomo Indiveri · Bernstein Conference 2024
Analog neuromorphic circuits emulate the dynamic properties of biological neural systems through their physics [1], sharing similarities and constraints with them [2,3]. This alignment can be leveraged for fundamental research to validate or challenge theories of neural computation as well as for applied research to develop compact, power-efficient brain-inspired computing systems. We present a novel spike-based dendritic learning mechanism embedded in a balanced cortical network, co-designed with neuromorphic circuits, that aligns theoretical neuroscience with analog circuit design techniques