Neural Lattice Architecture for Online Multi-Modal Learning
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Solution Overview
Problem
Current neuromorphic and synaptronic computation systems face challenges in implementing universal, online learning in multi-modal perception-action semilattices, particularly in effectively interconnecting neurons to facilitate sensory-motor modalities and handling massive data influx while maintaining stable activity levels.
Innovation Solution
The system interconnects neurons in a lattice structure with acyclic digraphs, using bottom-up and top-down signaling pathways to enable online learning, self-tuning, and adaptation, allowing for unsupervised, supervised, and reinforcement learning within a single substrate, with each node representing sensory or motor modalities and exchanging signals through synaptic connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neurons are interconnected in a lattice structure with multiple signaling pathways, then learning capability and adaptability are improved, but system complexity increases
Solution Approach 1:
The neural network is segmented into distinct lattice layers with specific connectivity patterns. Each layer processes information independently through bottom-up and top-down pathways, allowing complex learning tasks to be divided into manageable processing stages while maintaining overall system adaptability
Solution Approach 2:
The lattice structure implements universal learning rules that can handle multiple learning paradigms (unsupervised, supervised, reinforcement learning) within a single unified architecture. The same structural framework supports diverse learning capabilities without requiring separate specialized systems
2Productivity
If the system processes massive data influx through parallel distributed processing, then productivity is improved, but maintaining stable activity levels becomes more difficult
Solution Approach 1:
Top-down signaling pathways provide feedback mechanisms that regulate bottom-up information flow. This feedback control allows the system to maintain stable activity levels during parallel distributed processing by dynamically adjusting neuronal responses based on higher-level processing outcomes
Solution Approach 2:
The lattice structure implements dynamic processing where connectivity and signal transmission are adjusted in real-time based on processing demands. This dynamic adaptation enables the system to handle variable data influx rates while maintaining stability through flexible resource allocation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables stable activity levels, parallel distributed processing, and adaptation to spiking neurons, effectively handling massive data influx and solving various learning problems within a unified computational architecture.
Implementation Method 1
The synaptic conductance changes with time as a function of the relative spike times of pre-synaptic and post-synaptic neurons, as per spike-timing dependent plasticity (STDP). The STDP rule increases the conductance of a synapse if its post-synaptic neuron fires after its pre-synaptic neuron fires, and decreases the conductance of a synapse if the order of the two firings is reversed.
Data Source
AI summary
In one embodiment, the present invention provides a method for interconnecting neurons in a neural network. At least one node among a first set of nodes is interconnected to at least one node among a second set of nodes, and nodes of the first and second set are arranged in a lattice. At least one node of the first set represents a sensory-motor modality of the neural network. At least one node of the second set is a union of at least two nodes of the first set. Each node in the lattice has an acyclic digraph comprising multiple vertices and directed edges. Each vertex represents a neuron population. Each directed edge comprises multiple synaptic connections. Vertices in different acyclic digraphs are interconnected using an acyclic bottom-up digraph. The bottom-up digraph has a corresponding acyclic top-down digraph. Vertices in the bottom-up digraph are interconnected to vertices in the top-down digraph.