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The collective discussions reflect excitement about the novel methods used, such as meta-plasticity and gradient-free algorithms, with comments framing the research as a significant step forward for neuromorphic AI, using words like 'remarkable' and 'powerful neuroscience-derived approach' that convey enthusiasm and perceived importance.
Most discussions are supportive, highlighting the innovative aspects and the significance of the research, with some expressing enthusiasm for its neuroscience inspiration.
The topic engages participants who show curiosity about neuromorphic hardware and meta-learning techniques, indicating a high level of interest.
Posts delve into technical details like gradient-free optimization and metaplasticity, signifying deep engagement.
Few posts debate broader implications, but many recognize the research’s potential to advance AI hardware and understanding of brain functions, suggesting high perceived impact.
A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to learn" AI results delivered on the superfast @BrainScaleS analogue neuromorphic platform. https://t.co/BEIqcZ3WOB
New Research: Neuromorphic Hardware Learns to Learn: Hyperparameters and learning algorithms for neuromorphic hardware are usually chosen by hand to suit a particular task. In contrast, networks of neurons in the brain were optimized… https://t.co/HPpYtQHWxU #Neuroscience
Learning to Learn on Neuromorphic Hardware: https://t.co/2WK9kzCsDe - Neuromorphic hardware (mixed-signal) - Transfer learning - Meta-plasticity - Spiking neural networks
Nice work which explores implementations of various #metalearning methods in the #neuromorphic hardware context. Especially valuable +novel results on #metaplasticity which is powerful neuroscience-derived approach https://t.co/JzDGQ8BVyR
Read @BrainScaleS's latest research published this week to #arxiv #Hyperparameter #GeneticAlgorithms https://t.co/618QJRLRem
"Learns to learn" gives off the feeling of a hyperbolic statement for what is really achieved (just a meta-optimisation of the process of fitting), but the report makes for an interesting reading https://t.co/eVlOCgZZA8
This video discusses how neuromorphic computing requires less development time and resources, offering cost-effective and energy-efficient solutions. It explores recent innovations in hardware learning capabilities…
This video explores problems with artificial general intelligence, discussing current research challenges and limitations, including insights from a key paper. It emphasizes the complexity of creating truly autonomous…
RT @cbennett10: Nice work which explores implementations of various #metalearning methods in the #neuromorphic hardware context. Especially…
Nice work which explores implementations of various #metalearning methods in the #neuromorphic hardware context. Especially valuable +novel results on #metaplasticity which is powerful neuroscience-derived approach https://t.co/JzDGQ8BVyR
Neuromorphic Hardware Learns to Learn https://t.co/J9r80SSaF4 https://t.co/UwaROtIh1P
https://t.co/5CJ1SpJ5TH
RT @diegosunshine1: Learning to Learn on Neuromorphic Hardware: https://t.co/2WK9kzCsDe - Neuromorphic hardware (mixed-signal) - Transfer…
RT @FrontNeurosci: New Research: Neuromorphic Hardware Learns to Learn: Hyperparameters and learning algorithms for neuromorphic hardware a…
Brain in abstraction - @FrontiersIn reports on hardware that learns to learn. Reverse engineering may be the next big shortcut to understanding brain function from a mesoscale level. https://t.co/1fWNj7NLCA
RT @HumanBrainProj: A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to lea…
RT @diegosunshine1: Learning to Learn on Neuromorphic Hardware: https://t.co/2WK9kzCsDe - Neuromorphic hardware (mixed-signal) - Transfer…
Neuromorphic hardware learns how to learn. Now all we need to do is feed it @rocallahan 's record and replay project and blammo: Self-awareness, right? ;) https://t.co/AzRsE1wne8 https://t.co/HVOKjtm9MO
RT @FrontNeurosci: New Research: Neuromorphic Hardware Learns to Learn: Hyperparameters and learning algorithms for neuromorphic hardware a…
New Research: Neuromorphic Hardware Learns to Learn: Hyperparameters and learning algorithms for neuromorphic hardware are usually chosen by hand to suit a particular task. In contrast, networks of neurons in the brain were optimized… https://t.co/HPpYtQHWxU #Neuroscience
RT @HumanBrainProj: A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to lea…
RT @HumanBrainProj: A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to lea…
RT @HumanBrainProj: A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to lea…
RT @HumanBrainProj: A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to lea…
RT @HumanBrainProj: A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to lea…
RT @diegosunshine1: Learning to Learn on Neuromorphic Hardware: https://t.co/2WK9kzCsDe - Neuromorphic hardware (mixed-signal) - Transfer…
RT @HumanBrainProj: A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to lea…
RT @markomanka: "Learns to learn" gives off the feeling of a hyperbolic statement for what is really achieved (just a meta-optimisation of…
RT @HumanBrainProj: A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to lea…
RT @HumanBrainProj: A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to lea…
"Learns to learn" gives off the feeling of a hyperbolic statement for what is really achieved (just a meta-optimisation of the process of fitting), but the report makes for an interesting reading https://t.co/eVlOCgZZA8
RT @HumanBrainProj: A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to lea…
RT @HumanBrainProj: A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to lea…
RT @HumanBrainProj: A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to lea…
A new paper co-authored by @HBPNeuromorphic's late great Prof. Karlheinz Meier has revealed remarkable "learning to learn" AI results delivered on the superfast @BrainScaleS analogue neuromorphic platform. https://t.co/BEIqcZ3WOB
Learning to Learn on Neuromorphic Hardware: https://t.co/2WK9kzCsDe - Neuromorphic hardware (mixed-signal) - Transfer learning - Meta-plasticity - Spiking neural networks
"Neuromorphic Hardware learns to learn" https://t.co/q9xJBpnqvG
RT @arXiv_Daily: Read @BrainScaleS's latest research published this week to #arxiv #Hyperparameter #GeneticAlgorithms https://t.co/618QJRLR…
Read @BrainScaleS's latest research published this week to #arxiv #Hyperparameter #GeneticAlgorithms https://t.co/618QJRLRem
Neuromorphic Hardware learns to learn: https://t.co/Ym4n3MGmnN We employ powerful gradient-free optimization tools, such as cross-entropy methods and evolutionary strategies, in order to port functions of biological optimization processes to neuromorphic hardware. #GloryToAtheism https://t.co/Cs7a3Z8E2J
"Neuromorphic Hardware learns to learn", Thomas Bohnstingl, Franz Scherr, Christian Pehle, Karlheinz Meier, Wolfgan… https://t.co/MLAGg3GYun
Sci. hardware learns to learn. Front. Neurosci. 13, 483. doi: 10.3389/fnins.2019.00483. China Inf. Sci. 62, 1–19. doi: 10.1007/s11432-018-9863-6. Buzsáki, G ...
doi: 10.3389/ Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro 38, fnins.2019.00483 82–99. doi: 10.1109/MM.2018.112130359. Boybat ...
DOI: 10.3389/fnins.2019.00260. Large-Scale Simulations of Plastic Neural ... DOI: 10.3389/fnins.2019.00483. The neglected medial part of macaque area PE ...
doi: 10.3389/fbioe.2019.00483 modal (visual + olfaction). They further ... doi: 10.3389/fnins.2018.00123 Reynolds and Heeger, 2009); One of these ...
org/10.3389/fnins.2015.00220. Hardy, J.A., Higgins, G.A., 1992. Alzheimer's ... https://doi.org/10.3389/fphar.2019.00483. Parent, André, 1984 ...
https://doi.org/10.3389/fphar.2019.00483. 108. Holsboer, F.; Ising, M ... https://doi.org/10.3389/fnins.2021.728810. 112. Tan, X.; Zhang, L.; Wang, D ...
DOI: 10.3389/fnins.2019.00260. Large-Scale Simulations of Plastic ... DOI: 10.3389/fnins.2019.00483. The neglected medial part of macaque ...
DOI: 10.3389/fnins.2019.00483. The neglected medial part of macaque area PE: segregated processing of reach depth and direction (se abrirá en ...
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