Population-Activity Feedback Control for Neural Input Scaling
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Solution Overview
Problem
Existing data processing methods, such as LSTM networks, face challenges in converging to a desired solution efficiently, often requiring extensive computational power and may fail to prevent overloading or underloading, leading to suboptimal solutions and inefficiencies in training and resource usage.
Innovation Solution
A computer-implemented method involving a first control module to measure population activity in a processing unit and provide a control signal to adjust the system input, scaling it to prevent overloading and ensure convergence, using a second control module to scale the input based on the control signal and processing unit output, thereby facilitating efficient data processing and avoiding activity saturation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If parameter alteration is performed to increase likelihood of convergence, then convergence probability is improved, but computational power requirement increases extensively
Solution Approach 1:
The patent implements feedback by measuring population activity in the processing unit and using this measurement to dynamically adjust the scaling of system input. The control module continuously monitors neural network activity and modifies input scaling accordingly, creating a closed-loop system that adapts to current processing conditions rather than relying on extensive pre-computation of parameter alternatives.
Solution Approach 2:
The patent changes the parameter being controlled from fixed time extent of memorized data to dynamic scaling of system input based on measured population activity. This allows the system to adapt its processing characteristics in real-time based on actual network state, improving convergence while avoiding exhaustive computational search through parameter space.
2Reliability
If extensive computational power is used to iteratively test parameter settings, then solution quality may be improved, but training time increases
Solution Approach 1:
The system performs self-service by autonomously monitoring its own population activity and automatically adjusting input scaling without external intervention or exhaustive parameter testing. The control module enables the neural network to self-regulate its processing dynamics, eliminating the need for time-consuming iterative parameter evaluation by external systems.
Solution Approach 2:
The patent applies preliminary action by pre-establishing the feedback mechanism and population activity measurement system before training begins. This allows the system to immediately adapt to conditions during training rather than requiring time-consuming parameter exploration, as the adaptive infrastructure is already in place to respond to changing conditions.
3Productivity
If system input is not scaled, then processing capacity is fully utilized, but overloading occurs leading to activity saturation
Solution Approach 1:
The patent applies dynamics by making the system input scaling dynamic rather than static. The scaling factor is continuously adjusted based on real-time population activity measurements, allowing the system to transition between different operating states. This dynamic adaptation enables the system to maintain high processing capacity utilization while avoiding overloading conditions that would cause activity saturation.
Data Source
AI summary
The disclosure relates to a computer-implemented or hardware-implemented method (200) for processing data, comprising: measuring (210), preferably by a first control module (110), a population activity of a processing unit (130) comprising a population, the processing unit (130) receiving a processing unit input (156) and producing a processing unit output (158); providing (220), preferably by the first control module (110), a first control signal (160), the first control signal (160) being based on a processing unit output (158) and based on the measured population activity of the processing unit (130); receiving (230), preferably by a second control module (120), a system input (152) comprising data to be processed; scaling (240), preferably by a second control module (120), the system input (152), based on the first control signal (160), thereby providing a scaled input to the processing unit (130) in the next time step; and utilizing (250) the processing unit output (158) as a system output (162). The disclosure further relates to a computer program product, a data processing system and a first control module.