Neural Homeostatic Circuit With Feedback Current Stabilization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional homeostatic circuits for neural networks face challenges in maintaining stability against environmental variations, such as temperature changes, leading to high power consumption and difficulty in large-scale integration due to the need for external memory and processors.
Innovation Solution
A homeostatic circuit for neural networks is designed with a feedback circuit, electronic switches, and capacitors to regulate synaptic driving current, using a comparator and control voltage generator to adjust the current in a way that maintains it within a predetermined range without interfering with signal processing, utilizing N-type and P-type Metal-Oxide Semiconductor Field Effect Transistors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional homeostatic actions are realized by floating gate transistors or software control with external memory and processor, then the neural network can maintain stable operation, but power consumption increases and integration density decreases
Solution Approach 1:
The patent combines the homeostatic control function directly into the synapse circuit by integrating a floating gate transistor within the synaptic weight storage element. This merging eliminates the need for separate external memory and processor components, thereby reducing power consumption while maintaining stable operation. The floating gate transistor modifies the synaptic weight locally based on feedback signals,实现ing homeostasis at the circuit level rather than requiring system-level control.
Solution Approach 2:
The patent extracts the homeostatic control logic from external processors and embeds it directly into the synapse circuit through the floating gate transistor. By taking out the control function from the central processor and placing it locally at each synapse, the system eliminates the power consumption associated with external memory access and processor operations, while maintaining the ability to regulate synaptic weights.
2Reliability
If conventional homeostatic actions use external memory and processor, then stable operation is achieved, but device complexity and integration difficulty increase
Solution Approach 1:
The patent merges the homeostatic control functionality directly into the synapse circuit by using a floating gate transistor as part of the synaptic weight storage mechanism. This integration eliminates the need for separate external memory and processor components, significantly reducing device complexity and enabling large-scale high-density integration. The control logic is embedded at the synaptic level, allowing direct modification of weights without external intervention.
3Speed
If homeostatic actions occur on a short time scale, then rapid adaptation to variations is achieved, but interference with signal processing or learning mechanism occurs
Solution Approach 1:
The patent implements dynamic control of the floating gate transistor through feedback signals that adjust the synaptic weight in real-time. The homeostatic mechanism operates continuously but with variable speed, allowing rapid response to significant deviations while maintaining signal processing integrity during normal operation. The feedback loop dynamically modulates the floating gate voltage based on the actual synaptic output, achieving adaptive speed control.
Solution Approach 2:
The patent employs a feedback mechanism where the output of the synapse circuit is monitored and used to generate control signals for the floating gate transistor. This feedback loop ensures that homeostatic actions are triggered only when necessary to maintain stable operation, preventing interference with normal signal processing. The feedback signal adjusts the synaptic weight gradually, ensuring that corrections are made without disrupting the learning mechanism.
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 solution allows for low power consumption and cost-effective implementation, enabling large-scale and high-density integration with other artificial neural computing systems while maintaining stability and optimizing synaptic driving current.
Implementation Method 1
The total synaptic driving current is produced by the integrating effect of the homeostatic circuit for neural networks through the first capacitor
Implementation Method 2
The comparator includes a first input port, a second input port and an output port. The first input port is configured to receive a reference current, the second input port being configured to receive the total synaptic driving current and the output port being configured to output a second voltage signal
Implementation Method 3
When the first comparison voltage is greater than the second comparison voltage, the second capacitor is controlled by the fifth electronic switch to be charged. When the first comparison voltage is lower than the second comparison voltage, the fifth electronic switch controls the second capacitor to be discharged so as to control the feedback voltage to vary slowly
Implementation Method 4
The first electronic switch and the second electronic switch may be N-type Metal-Oxide Semiconductor Field Effect Transistors while the third electronic switch, the fourth electronic switch and the fifth electronic switch may be P-type Metal-Oxide Semiconductor Field Effect Transistors
Data Source
AI summary
A homeostatic circuit for neural networks includes a feedback circuit, a first electronic switch, a synapse circuit, a second electronic switch, a third electronic switch and a first capacitor. The feedback circuit is configured to receive the total synaptic driving current and output a feedback voltage which varies with the total synaptic driving current. The first electronic switch is connected with the synapse circuit and the second electronic switch and configured to receive the feedback voltage and output a current control signal according to the feedback voltage. The second electronic switch is connected with the synapse circuit and the third electronic switch and configured to output a first voltage signal according to the current control signal. The third electronic switch is configured to adjust the total synaptic driving current in a direction opposite to variation tendency of the total synaptic driving current according to the first voltage signal.