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pro vyhledávání: '"Nicolas Perez-Nieves"'
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-8 (2023)
Abstract Machine learning (ML) models have long overlooked innateness: how strong pressures for survival lead to the encoding of complex behaviors in the nascent wiring of a brain. Here, we derive a neurodevelopmental encoding of artificial neural ne
Externí odkaz:
https://doaj.org/article/914623c2f57d433ea7325269f19c3bb6
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
The authors show that heterogeneity in spiking neural networks improves accuracy and robustness of prediction for complex information processing tasks, results in optimal parameter distribution similar to experimental data and is metabolically effici
Externí odkaz:
https://doaj.org/article/5f3aee1234744188899e09099851da31
Publikováno v:
2019 Conference on Cognitive Computational Neuroscience
It is very common in studies of the learning capabilities of spiking neural networks (SNNs) to use homogeneous neural and synaptic parameters (time constants, thresholds, etc.). Even in studies in which these parameters are distributed heterogeneousl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd8327b67c8ec10d3d18cffa1868005c
http://hdl.handle.net/10044/1/78173
http://hdl.handle.net/10044/1/78173
