Spiking Neuron Network Plasticity for Visual Processing

The patent implements dynamic adjustment of connection efficacy between neurons based on spike timing patterns. The system continuously adapts the strength of inhibitory and excitatory connections during real-time processing, allowing the network to optimize its computational operations on-the-fly. This dynamic plasticity enables the network to handle complex visual processing tasks at speeds suitable for portable devices by adapting its computational complexity requirements.

The spiking neuron network performs self-adjustment through activity-dependent plasticity rules. The network automatically modifies its own connection weights based on observed spike timing patterns without external intervention. This self-service mechanism reduces the need for complex external control systems and manual tuning, thereby lowering overall device complexity while maintaining high processing speed for visual tasks.

The patent implements periodic evaluation of spike timing patterns to adjust connection efficacy. Rather than continuous adjustment, the system evaluates and updates connections at discrete time intervals based on accumulated spike timing information. This periodic action reduces the computational energy required compared to continuous adjustment, while still achieving high accuracy in identifying salient visual features through accumulated temporal patterns.

The system changes the efficacy parameter of connections based on observed spike timing patterns. By modifying connection weights as a function of temporal patterns, the network achieves accurate feature identification without requiring proportional increases in computational energy. The parameter adjustment is driven by biological plausibility and efficiency, allowing accurate processing with constrained energy resources suitable for portable devices.