The hardware is the software
Not on the map. Why?
Abstract (arXiv)
Human brains and bodies are not hardware running software: the hardware is the software. We reason that because the microscopic physics of artificial-intelligence hardware and of human biological "hardware" is distinct, neuromorphic engineers need to be cautious (and yet also creative) in how we take inspiration from biological intelligence. We should focus primarily on principles and design ideas that respect -- and embrace -- the underlying hardware physics of non-biological intelligent systems, rather than abstracting it away. We see a major role for neuroscience in neuromorphic computing as identifying the physics-agnostic principles of biological intelligence -- that is the principles of biological intelligence that can be gainfully adapted and applied to any physical hardware.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Oct 2023.
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Citing papers per year: 2024: 1; 2025: 4; 2026: 3.
8 papers.
- 2024Training of Physical Neural Networkscited by 47
- 2025Learning long range dependencies through time reversal symmetry breakingcited by 6
- 2025Self-Organising Memristive Networks as Physical Learning Systemscited by 2
- 2025Neuromorphic Intelligencecited by 1
- 2026Physical Foundation Models: Fixed hardware implementations of large-scale neural networkscited by 1
- 2025Programmable metasurfaces for future photonic artificial intelligencecited by 0
- 2026Physical Analogue Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Unitscited by 0
- 2026Perturbative Contrastive Physical Learningcited by 0
- 2026Perturbative Contrastive Physical Learning8 Jun 2026
- 2026Physical Foundation Models: Fixed hardware implementations of large-scale neural networks30 Apr 2026
- 2026Physical Analogue Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units7 Feb 2026
- 2025Neuromorphic Intelligence15 Sep 2025
- 2025Self-Organising Memristive Networks as Physical Learning Systems31 Aug 2025
- 2025Learning long range dependencies through time reversal symmetry breaking5 Jun 2025
- 2025Programmable metasurfaces for future photonic artificial intelligence16 May 2025
- 2024Training of Physical Neural Networks5 Jun 2024