◑ applying · kind experiment · level 1 · 12.17h
- Requiere: Spiking Neural Network (SNN) · Elixir · AdEx Neuron
- Aplica: ETS · Backpressure
A split design: Elixir orchestrates the connectome (routing, backpressure, ETS graph) while Python (NumPy) vectorises the AdEx neuron updates, bridged over Erlang Ports. Separation with union at the data boundary.
Neuro-symbolic AI is a subfield of artificial intelligence that combines neural networks and symbolic AI approaches, such as knowledge representation and automated reasoning, to create more robust, more reliable, and more trustworthy AI. This combination allows statistical patterns to be combined with explicitly defined rules and knowledge to give AI systems the ability to better represent, reason and generalize. Thus, neuro-symbolic AI provides a reasoning infrastructure to state-of-the-art machine learning for solving a wider range of problems more effectively.
Enlaces
- Requiere: Spiking Neural Network (SNN) · Elixir · AdEx Neuron
- Aplica: ETS · Backpressure