Prof. Chung-Chuan Lo Profile

Prof. Chung-Chuan Lo

at National Tsing Hua Univ

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Proceedings Article | 31 May 2022 Presentation + Paper

HDR luminance normalization via contextual facilitation in highly recurrent neuromorphic spiking networks

Alexander White, Chou Hung, Chung-Chuan Lo

Proceedings Volume 12124, 121240E (2022) https://doi.org/10.1117/12.2623789

KEYWORDS: Neurons, High dynamic range imaging, Visualization, Neural networks, Optical flow, Brain, Modulation, Image segmentation, Sensors, Network architectures

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Previous research has shown that many luminance normalization mechanisms are engaged when viewing scenes with high dynamic range (HDR) luminance. In one such phenomenon, areas of similar luminance contextually facilitate the perception of ambiguous textures. Using inspiration from biological circuitry, we developed a recurrent spiking neural network that reproduces experimental results of contextual facilitation in HDR images. The network uses correlations between luminance and texture to correctly classify and segment ambiguous textures in images. While many deep neural networks can successfully perform many types of image analysis, they have limited ability to process images under naturalistic HDR illumination, requiring millions of neurons and power hungry GPUs. It is an open question if a recurrent spiking neural network can minimize the number of neurons required to perform HDR image segmentation based on texture. To that end, we designed a biologically inspired proof-of-concept recurrent SNN that can perform such a task. The network is implemented using leaky integrate-and-fire neurons, with CuBa synapses. We use the Nengo LOIHI API to simulate the network, so it can be run on Intel’s LOIHI neuromorphic hardware. The network uses a highly recurrent structure to both group image elements based on luminance and texture, and to seamlessly combine these modalities to correctly segment ambiguous textures. Furthermore, we can continuously modulate how much luminance or texture contribute to the segmentation. We surmise that further development of this network will improve the resilience of optical flow computations under environments with complex naturalistic illumination.

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