2023/10/25 by Daniel J. Mannion, Mannion, Daniel John
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Electrical engineering #Neural Networks and Reservoir Computing #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2311.12861
openalex publication_date 2023/10/25 · openalex created_date 2023/11/24 · openalex updated_date 2026/07/28
Sophisticated machine learning struggles to transition onto battery-operated devices due to the high-power consumption of neural networks. Researchers have turned to neuromorphic engineering, inspired by biological neural networks, for more efficient solutions. While previous research focused on artificial neurons and synapses, an essential component has been overlooked: dendrites. Dendrites transmit inputs from synapses to the neuron's soma, applying both passive and active transformations. However, neuromorphic circuits replace these sophisticated computational channels with metallic interconnects. In this study, we introduce a versatile circuit that emulates a segment of a dendrite which exhibits gain, introduces delays, and performs integration. We show how sound localisation - a biological example of dendritic computation - is not possible with the existing passive dendrite circuits but can be achieved using this proposed circuit. We also find that dendrites can form bursting neurons. This significant discovery suggests the potential to fabricate neural networks solely comprised of dendrite circuits.