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10.1038/s41467-025-64232-1 http://scihub22266oqcxt.onion/10.1038/s41467-025-64232-1 C12533029!12533029 !41102189     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=41102189 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\41102189 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Nat+Commun 2025 ; 16 (1 ): 9192 Nephropedia Template TP {{tp|p=41102189 |t=2025. Noise-aware training of neuromorphic dynamic device networks |pdf=|usr=}}{{41102189 }} gab.com Text Noise-aware training of neuromorphic dynamic device networks JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=41102189 #FOAvip In materio computing offers the potential for widespread embodied intelligence by leveraging the intrinsic dynamics of complex systems for efficient sensing, processing, and interaction. While individual devices offer basic data processing capabilities, networks of interconnected devices can perform more complex and varied tasks. However, designing such networks for dynamic tasks is challenging in the absence of physical models and accurate characterization of device noise. We introduce the Noise-Aware Dynamic Optimization (NADO) framework for training networks of dynamical devices, using Neural Stochastic Differential Equations (Neural-SDEs) as differentiable digital twins to capture both the dynamics and stochasticity of devices with intrinsic memory. Our approach combines backpropagation through time with cascade learning, enabling effective exploitation of the temporal properties of physical devices. We validate this method on networks of spintronic devices across both temporal classification and regression tasks. By decoupling device model training from network connectivity optimization, our framework reduces data requirements and enables robust, gradient-based programming of dynamical devices without requiring analytical descriptions of their behaviour. Twit Text FOAVip Noise-aware training of neuromorphic dynamic device networks ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=41102189 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41102189 #FOAvip English Wikipedia <ref>{{Cite pmid|41102189 }}</ref> Noise-aware training of neuromorphic dynamic device networks #MMPMID41102189 Manneschi L ; Vidamour IT ; Stenning KD ; Swindells C ; Venkat G ; Griffin D ; Gui L ; Sonawala D ; Donskikh D ; Hariga D ; Donati E ; Stepney S ; Branford WR ; Gartside JC ; Hayward TJ ; Ellis MOA ; Vasilaki E Nat Commun 2025[Oct]; 16 (1 ): 9192 PMID41102189 show ga In materio computing offers the potential for widespread embodied intelligence by leveraging the intrinsic dynamics of complex systems for efficient sensing, processing, and interaction. While individual devices offer basic data processing capabilities, networks of interconnected devices can perform more complex and varied tasks. However, designing such networks for dynamic tasks is challenging in the absence of physical models and accurate characterization of device noise. We introduce the Noise-Aware Dynamic Optimization (NADO) framework for training networks of dynamical devices, using Neural Stochastic Differential Equations (Neural-SDEs) as differentiable digital twins to capture both the dynamics and stochasticity of devices with intrinsic memory. Our approach combines backpropagation through time with cascade learning, enabling effective exploitation of the temporal properties of physical devices. We validate this method on networks of spintronic devices across both temporal classification and regression tasks. By decoupling device model training from network connectivity optimization, our framework reduces data requirements and enables robust, gradient-based programming of dynamical devices without requiring analytical descriptions of their behaviour. äNoise-aware training of neuromorphic dynamic device networks (epmc).pdf DeepDyve Pubget Overpricing