# name
Real-time hardware emulation of neural cultures: A comparative study of invitro, in silico and in duris silico models

# metadata.dc.description.abstract[].value
Biological neural networks are well known for their capacity to process information with extremely low power consumption. Fields such as Artificial Intelligence, with high computational costs, are seeking for alternatives inspired in biological systems. An inspiring alternative is to implement hardware architectures that replicate the behavior of biological neurons but with the flexibility in programming capabilities of an electronic device, all combined with a relatively low operational cost. To advance in this quest, here we analyze the capacity of the HEENS hardware architecture to operate in a similar manner as an in vitro neuronal network grown in the laboratory. For that, we considered data of spontaneous activity in living neuronal cultures of about 400 neurons and compared their collective dynamics and functional behavior with those obtained from direct numerical simulations (in silico) and hardware implementations (in duris silico). The results show that HEENS is capable to mimic both the in vitro and in silico systems with high efficient-cost ratio, and on different network topological designs. Our work shows that compact low-cost hardware implementations are feasible, opening new avenues for future, highly efficient neuromorphic devices and advanced human–machine interfacing.

# metadata.dc.rights[].value
cc-by-nc-nd (c) Vallejo-Mancero, B., et al., 2024

# metadata.dc.source[].value
Articles publicats en revistes (Física de la Matèria Condensada)

# metadata.dc.title[].value
Real-time hardware emulation of neural cultures: A comparative study of invitro, in silico and in duris silico models