Synaptic Connectivity Graph for Neural Network Training
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Solution Overview
Problem
Current machine learning models, particularly deep neural networks, face challenges in efficiently processing complex data tasks due to their high computational requirements and the need for extensive training data, which can be resource-intensive and time-consuming.
Innovation Solution
The development of a system that generates a synaptic connectivity graph from a biological organism's brain image, allowing for the creation of a brain emulation neural network with a specified architecture, which can be used to train a student neural network with a less complex architecture to mimic the brain emulation network's outputs, thereby reducing computational resources and training data needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep neural networks are used to process complex data tasks, then processing capability is improved, but computational requirements and training data needs increase
Solution Approach 1:
The patent creates a simplified copy of the brain's neural network architecture (student network) that mimics the essential connectivity patterns of biological neurons without replicating their full complexity. This copying approach captures the core processing capabilities while reducing computational requirements by using abstracted neural connections rather than detailed biological simulations.
Solution Approach 2:
The patent extracts only the essential synaptic connectivity patterns from biological brain images, removing unnecessary biological details and complexities. By taking out only the critical architectural features needed for data processing while discarding redundant biological information, the system achieves efficient computation with reduced resource requirements.
2Productivity
If deep neural networks are used to process complex data tasks, then processing capability is improved, but training time and resource consumption increase
Solution Approach 1:
The patent performs preliminary action by using brain emulation networks to pre-process and generate synthetic training data that encapsulates essential learning patterns. This preliminary generation of training examples allows student networks to learn faster with fewer iterations, as the training data is pre-optimized to reflect effective neural processing strategies observed in biological systems.
Solution Approach 2:
By copying the effective processing patterns from brain emulation networks into student network architectures, the system transfers learned capabilities efficiently. This copying of proven neural processing strategies enables student networks to achieve comparable performance with significantly reduced training time and resource consumption.
3Measurement precision
If brain emulation neural networks with complex architectures are created, then processing accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex brain network into modular student network units with simplified architectures. Each student network module handles specific processing functions with reduced complexity, while collectively maintaining high processing accuracy through coordinated operation of multiple specialized modules rather than a single complex network.
Solution Approach 2:
The patent applies local quality by optimizing specific regions of the neural network architecture to handle particular types of processing tasks with appropriate complexity levels. Different parts of the student network are tailored with specialized structures matched to their functional requirements, achieving high overall accuracy without uniformly increasing complexity throughout the entire system.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student neural network. In one aspect, there is provided a method comprising: processing a training input using the student neural network to generate a student neural network output comprising a respective score for each of a plurality of classes; processing the training input using a brain emulation neural network to generate a brain emulation neural network output comprising a respective score for each of the plurality of classes; and adjusting current values of the student neural network parameters using gradients of an objective function that characterizes a similarity between: (i) the student neural network output for the training input, and (ii) the brain emulation neural network output for the training input.