Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks
arXiv:2602.14885v2 Announce Type: replace-cross Abstract: Recurrent neural networks (RNNs) provide a theoretical framework for understanding computation in biological neural circuits, yet classical results, such as Hopfield's model of associative memory, rely on symmetric connectivity that restricts network dynamics to gradient-like flows. In contrast, biological networks support rich time-dependent behaviour facilitated by their asymmetry. Here we introduce a general framework, which we term drift-diffusion matching, for training continuous-time RNNs to represent arbitrary, nonlinear stochast
This paper represents continued academic progress in understanding and developing more biologically plausible and capable recurrent neural networks for complex dynamic tasks.
Improving continuous-time RNNs to handle arbitrary nonlinear stochastic dynamics could lead to significantly more robust and adaptable AI systems, particularly for real-time control and cognitive modeling.
The proposed 'drift-diffusion matching' framework offers a new method to train RNNs to represent richer, time-dependent behaviors, moving beyond limitations of symmetric connectivity models like Hopfield networks.
- · AI researchers
- · Robotics
- · Cognitive computing
- · Neuromorphic hardware
- · AI models reliant on overly simplified dynamics
More sophisticated and biologically inspired AI models become possible, enabling better simulation of complex dynamic systems.
This could accelerate advances in AI agents that need to operate in highly dynamic and unpredictable environments by better handling time-dependent phenomena.
Long-term implications could include human-like learning and adaptation in AI systems, moving closer to artificial general intelligence.
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Read at arXiv cs.LG