| name | neuro-vesicles-neuromodulation |
| description | Neuro-Vesicles framework for dynamical neuromodulation in neural networks. Introduces mobile discrete vesicle population as event-based interaction layer alongside network tensors. Applies to: neuromodulation, dynamic network modulation, spiking networks, neuromorphic hardware. Activation: neuro vesicles, neuromodulation dynamical, mobile modulation, vesicle framework, programmable neuromodulation. |
Neuro-Vesicles Framework
A dynamical neuromodulation framework using mobile discrete vesicles as an event-based interaction layer, replacing tensor-based conditioning with stochastic population dynamics.
Metadata
- Source: arXiv:2512.06966
- Authors: Zilin Li, Weiwei Xu, Vicki Kane
- Published: 2025-12-07
Core Methodology
Key Innovation
Replaces static tensor-based modulation (FiLM, hypernetworks, attention) with a dynamical population of mobile vesicles that emit, migrate, dock, and release content to locally modify network behavior.
Vesicle Model
Each vesicle is a self-contained object:
v = (c, κ, l, τ, s)
- c: Vector payload (content)
- κ: Type label
- l: Location on graph G = (V, E)
- τ: Remaining lifetime
- s: Optional internal state
Lifecycle Operations
- Emission: Triggered by activity, errors, or meta-signals
- Migration: Move along learned transition kernels on the graph
- Docking: Probabilistic attachment at target nodes
- Release: Content-dependent modification of activations, parameters, or learning rules
- Decay/Absorption: Vesicle termination after lifetime expires
Continuous Density Relaxation
- Yields differentiable reaction-diffusion dynamics on the graph
- Dense, short-lived vesicles ≈ tensor mechanisms (FiLM, hypernetworks)
- Sparse, long-lived vesicles ≈ mobile agents intervening at critical moments
Technical Framework
Mathematical Specification
Emission: p(v_emitted|node_state, error_signal)
Migration: p(l_{t+1}|l_t, transition_kernel)
Docking: p(dock|l, node_type, affinity)
Release: effect = release_operator(v.c, target.activation)
Decay: τ_{t+1} = τ_t - 1; absorb if τ ≤ 0
Integration with Learning
- RL View: Vesicle control as policy optimized for downstream performance
- Gradient-based: Continuous relaxation enables backpropagation
- Spiking Extension: Formalism extends to SNNs and neuromorphic hardware (e.g., Darwin3 chip)
Applications
- Dynamic network modulation without parameter explosion
- Programmable neuromodulation on neuromorphic chips
- Adaptive learning rate and regularization
- Context-dependent network behavior switching
- Memory-augmented neural computation
Pitfalls
- Early-stage theoretical design — no implementation yet
- Vesicle population management adds computational overhead
- Transition kernel learning requires careful initialization
- Graph structure must support efficient vesicle routing
- Hyperparameters: emission rate, lifetime, migration speed
Related Skills
- neuromodulated-synaptic-plasticity
- dual-timescale-memory-spiking-neuron-astrocyte
- neuroplastic-plasticity-optimizer
