Read all about the #neuromorphic circuits giving BrainScaleS-2 its unique features, flexibility and unprecedented accuracy! Accepted for @IEEEICECS 2022: https://t.co/orWJ9A7Tae!
Simulate with EBRAINS! Learn about the research infrastructure and Fenix resources for simulations on a scale from molecules via small and large networks of point or structured spiking neurons to simplified whole brain activity and virtual environments! https://t.co/R6bEaHgak2
Uniting efficiency and usability of #neuromorphic computing! "A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware" explains the experiment workflow, API layering, software design, and platform operation for @BrainScaleS-2! https://t.co/MoqskhJ4ah
📚New book chapter by our @HBPNeuromorphic@BrainScaleS colleauges about
"Accelerated Analog Neuromorphic Computing"
https://t.co/RD3SBDLNVw
#OpenAccess: https://t.co/EdJ6yNKlDN
A new fantastic Special Issue is available in Neuroscience: Dendritic contributions to biological and artificial computations by Guest Editors
@YiotaPoirazi & @SchillerJackie. Look out https://t.co/FozEvfDUGn @NeurosciIBRO @ELSneuroscience
Software for #neuromorphic hardware is a challenge! Our new pre-print "A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware" explains how it is done for @BrainScaleS-2! https://t.co/f3U6Di1N69
The 9th Neuro Inspired Computational Elements Conference (NICE 2022) starts next week (29 March - 1 April 2022)!
The workshop offers #neuromorphic computing related talks as well as SpiNNaker, @BrainScaleS and Loihi hands-on sessions.
https://t.co/qiVYGY2WbE @NICE_Workshop
What better way to spend the coming Saturday evening than attending Andreas Baumbach's talk at the #SIAMPP22 mini-symposium on Neuromorphic Architectures at 8.30 pm CET (Feb 26)?
-> Abstract: https://t.co/mla9Ve53Zx
@BrainScaleS
Neuromorphic chips could be the energy-saving future of AI. But there’s been a major setback for analog chips, which most closely model the efficient processing of brains. Today in @QuantaMagazine I cover new exciting progress with the @BrainScaleS-2 chip.
https://t.co/Xg4DjKgevQ
Out now, "Surrogate gradients for analog neuromorphic computing." We show that learning self-calibrates analog spiking hardware, an essential step toward bio-inspired ultra-low-power computing. Very proud of this collab between @FMIscience & @BrainScaleS.
https://t.co/Tvpw7mYjjY
From point to structured neurons! Jakob shows you how to run experiments on the @BrainScaleS#neuromorphic hardware via @EBRAINS_eu: https://t.co/PiFZBIbt3q!