Dynamic Semantic Network Neuro-Morphic Computing

Abstract

This paper proposes a novel approach to computing, termed Dynamic Semantic Network Neuro-Morphic Computing, which leverages the principles of biological neural networks to achieve parallel, adaptive learning, and reasoning for complex data structures. The core idea is to mimic the dynamic connectivity and synaptic plasticity mechanisms found in biological neurons, creating a programmable neuro-morphic architecture. This architecture utilizes dynamically adjustable neural network connections and synaptic strengths based on input data and learning algorithms. A temporal signal processing and feedback mechanism simulates the dynamic behavior of biological neural networks, combined with Graph Neural Networks (GNNs) to construct and update dynamic semantic networks for representing and inferring relationships within data. This approach addresses the limitations of existing neuro-morphic computing, which primarily focuses on static hardware architectures, and traditional GNNs facing inefficiencies in handling large-scale, dynamic semantic networks. The resulting system aims to provide real-time learning and reasoning capabilities for complex data relationships, with potential benefits of low power consumption and high parallelism. The system is formally defined as follows: Let *S* = {*s*1, *s*2, ..., *s*K} be a set of nodes representing data elements. Let *E* = {*e*1, *e*2, ..., *e*N} be a set of edges representing relationships between nodes. Let *W* = {*w*ij} be a matrix representing the connection weights between nodes *s*i and *s*j, where *w*ij ∈ ℝ. Let *θ* = {*θ*ij} be a matrix representing the synaptic strengths between nodes *s*i and *s*j, where *θ*ij ∈ ℝ. Let *a*i ∈ ℝd be the activation value of node *s*i at time *t*. Let *l*i ∈ ℝ be the learning rate for node *s*i at time *t*. Let *h*i ∈ ℝd be the hidden state of node *s*i at time *t*. The dynamic update rule for node activation is given by: *a*i(t+1) = σ(*∑*j (*w*ij*h*j(t+1)) + *θ*ij *a*i(t+1)) where σ is an activation function (e.g., sigmoid, ReLU). The dynamic update rule for hidden state is given by: *h*i(t+1) = *f*(*a*i(t+1)) where *f* is a function that transforms the activation value into a hidden state. The learning rule updates the connection weights and synaptic strengths as follows: *w*ij(t+1) = *w*ij(t) + *l*i *∑*k (*w*ik(t+1) (*a*k(t+1)) ) *θ*ij(t+1) = *θ*ij(t) + *l*i *∑*k (*w*ik(t+1) (*a*k(t+1)) ) where *l*i is the learning rate. The system's performance is evaluated based on metrics such as accuracy, convergence time, and energy consumption. The core architecture will be implemented using a neuromorphic hardware platform, potentially utilizing spiking neural networks (SNNs) for efficient temporal processing.