Spiking Neuron Visual Encoder Adaptation
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Solution Overview
Problem
Existing artificial retinal systems fail to effectively encode visual data into spike output using spiking neuron networks, limiting their ability to reproduce complex visual features and achieve efficient data compression.
Innovation Solution
A computer-implemented method and apparatus that scales the excitability of spiking neurons based on statistical parameters of visual input, adjusts the excitability threshold, and generates responses in the form of spike rates or latencies, enabling efficient encoding and compression of visual data into spike output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing artificial retinal systems use conventional encoding methods, then the system structure is simpler, but the ability to encode visual data into spike output is insufficient and data compression is limited
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting neuron excitability based on statistical parameters of visual input. The excitability is scaled according to the variance or standard deviation of the input signal, allowing the system to adapt its encoding efficiency to different input conditions without changing the fundamental system structure.
Solution Approach 2:
The system implements dynamics through adaptive threshold adjustment and time-varying excitability scaling. The threshold is dynamically modified based on recent spike history, and the excitability scaling factor changes according to the statistical properties of the visual input, enabling the system to optimize encoding efficiency in real-time.
2Productivity
If the excitability threshold is kept constant, then the system operation is simpler, but the encoding of complex visual features and data compression performance deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the threshold adjustment depends on the neuron's recent spike history. The threshold is increased after each spike and decays over time, creating a feedback loop that regulates spike rate and improves data compression by preventing excessive spiking during sustained high-input conditions.
Solution Approach 2:
The system performs preliminary action by pre-scaling the excitability based on statistical parameters calculated from the visual input before the actual encoding process. This preliminary scaling optimizes the encoding efficiency for the expected input characteristics, improving productivity before the main encoding operation begins.
3Loss of information
If statistical parameter-based excitability scaling is implemented, then visual data encoding and compression improve, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by computing only the necessary statistical parameters (variance or standard deviation) of the visual input rather than full feature extraction. This partial computation provides sufficient information for excitability scaling without the excessive computational burden of complete image analysis, balancing encoding efficiency with computational complexity.
Data Source
AI summary
Sensory encoder may be implemented. Visual encoder apparatus may comprise spiking neuron network configured to receive photodetector input. Excitability of neurons may be adjusted and output spike may be generated based on the input. When neurons generate spiking response, spiking threshold may be dynamically adapted to produce desired output rate. The encoder may dynamically adapt its input range to match statistics of the input and to produce output spikes at an appropriate rate and/or latency. Adaptive input range adjustment and/or spiking threshold adjustment collaborate to enable recognition of features in sensory input of varying dynamic range.