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SLoN: a spiking looming perception network exploiting neural encoding and processing in ON/OFF channels.

Zhifeng Dai1, Qinbing Fu1, Jigen Peng1

  • 1Machine Life and Intelligence Research Centre, School of Mathematics and Information Science, Guangzhou University, Guangzhou, China.

Frontiers in Neuroscience
|March 21, 2024
PubMed
Summary

This study introduces a novel spiking neural network (SLoN) that mimics biological vision for improved looming perception, outperforming traditional artificial neural networks in detecting approaching objects.

Keywords:
ON/OFF channelseccentric down-samplinglooming selectivityphase codingspiking looming perception network

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Computer Vision

Background:

  • Biological systems possess highly evolved looming perception for survival.
  • Current artificial vision systems lack robust capabilities in sensing approaching objects.
  • Spiking neural networks offer a more biologically plausible approach to motion perception.

Purpose of the Study:

  • To propose a novel spiking neural network (SLoN) for enhanced looming perception.
  • To develop a biologically inspired model for detecting approaching objects.
  • To improve artificial vision systems' ability to process motion information.

Main Methods:

  • Developed a spiking neural network (SLoN) utilizing phase coding for neural encoding.
  • Implemented ON/OFF channels with eccentric down-sampling, mimicking mammalian visual processing.
  • Modeled neuronal networks using the leaky integrate-and-fire (LIF) paradigm.
  • Tested the SLoN model with diverse synthetic and real-world visual collision scenarios.

Main Results:

  • The SLoN demonstrated selective spiking for looming features amidst various motion types (translating, receding, grating).
  • The model exhibited robust selectivity, aligning with biological principles of motion perception.
  • ON/OFF channels, phase coding with delay, and eccentric processing proved effective for looming perception.

Conclusions:

  • The SLoN presents a novel, biologically plausible paradigm for artificial looming perception.
  • This approach enhances artificial vision systems' capability to process motion information realistically.
  • The study highlights the effectiveness of spiking neural networks in mimicking biological visual processing.