Neuro-Vesicles: Neuromodulation Should Be a Dynamical System, Not a Tensor Decoration

2025-12-07

AI Summary

Existing neural modulation methods predominantly rely on static tensor operations, failing to capture the dynamic, discrete, event-driven nature of biological neuromodulation mediated by synaptic vesicles. Method: This paper introduces Neuro-Vesicles—a novel framework that decouples neuromodulation into a dedicated, network-external computational layer modeling populations of dynamic vesicles. It formalizes vesicle release, migration, docking, and cascade-triggering via reaction-diffusion dynamics on graphs, and jointly optimizes sparse long-term interventions and dense short-term regulation through continuous density relaxation and reinforcement learning. Structural traces are embedded in topological memory. Contribution/Results: Neuro-Vesicles is compatible with spiking neural networks and neuromorphic hardware (e.g., Darwin3), approximates tensor-based mechanisms (e.g., FiLM, hypernetworks, attention), and enables programmable, agent-like, event-driven neuromodulation—establishing a biologically grounded paradigm for brain-inspired computing.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Neuro-Symbolic Learning
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

We introduce Neuro-Vesicles, a framework that augments conventional neural networks with a missing computational layer: a dynamical population of mobile, discrete vesicles that live alongside the network rather than inside its tensors. Each vesicle is a self contained object v = (c, kappa, l, tau, s) carrying a vector payload, type label, location on the graph G = (V, E), remaining lifetime, and optional internal state. Vesicles are emitted in response to activity, errors, or meta signals; migrate along learned transition kernels; probabilistically dock at nodes; locally modify activations, parameters, learning rules, or external memory through content dependent release operators; and finally decay or are absorbed. This event based interaction layer reshapes neuromodulation. Instead of applying the same conditioning tensors on every forward pass, modulation emerges from the stochastic evolution of a vesicle population that can accumulate, disperse, trigger cascades, carve transient pathways, and write structured traces into topological memory. Dense, short lived vesicles approximate familiar tensor mechanisms such as FiLM, hypernetworks, or attention. Sparse, long lived vesicles resemble a small set of mobile agents that intervene only at rare but decisive moments. We give a complete mathematical specification of the framework, including emission, migration, docking, release, decay, and their coupling to learning; a continuous density relaxation that yields differentiable reaction diffusion dynamics on the graph; and a reinforcement learning view where vesicle control is treated as a policy optimized for downstream performance. We also outline how the same formalism extends to spiking networks and neuromorphic hardware such as the Darwin3 chip, enabling programmable neuromodulation on large scale brain inspired computers.

Problem

• Introduces a framework augmenting neural networks with mobile vesicles for neuromodulation
• Models neuromodulation as a dynamical system using vesicles that modify network behavior
• Enables programmable neuromodulation for spiking networks and neuromorphic hardware

Innovation

• Dynamical vesicles augment neural networks with mobile computational layer
• Vesicles migrate, dock, and modify network via event-based interactions
• Framework enables programmable neuromodulation for brain-inspired computing