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Synaptic Delays for Insect-Inspired Temporal Feature Detection in Dynamic Neuromorphic Processors.

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Spiking neural networks utilize dynamic delay elements for spatiotemporal pattern recognition. This study introduces novel inhibitory-excitatory synapse pairs for tunable delays, enhancing neuromorphic processing capabilities.

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Area of Science:

  • Neuromorphic Engineering
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Spiking neural networks (SNNs) excel at spatiotemporal feature detection.
  • Neuromorphic processors often require dedicated delay mechanisms.
  • Existing methods for implementing delays can be complex.

Purpose of the Study:

  • To investigate disynaptic delay elements formed by inhibitory-excitatory (IE) synapse pairs.
  • To explore the potential of these elements for temporal feature tuning in SNNs.
  • To mimic biological auditory processing circuits using these novel delay elements.

Main Methods:

  • Configured IE disynaptic delay elements on the DYNAP-SE neuromorphic processor.
  • Characterized delayed excitation distributions considering device mismatch.
  • Designed a network mimicking the cricket's auditory feature detection circuit.

Main Results:

  • Disynaptic delay elements offer tunable temporal features up to 100 ms.
  • Timing and magnitude of delayed excitation are controlled by synaptic efficacies.
  • A single neuron with multiple delay elements can achieve pattern selectivity.
  • Network performance is influenced by synaptic weights, input noise, and temperature.

Conclusions:

  • IE disynaptic delay elements provide a flexible method for synapse-level temporal feature tuning.
  • These elements enhance the capabilities of neuromorphic processors for complex pattern recognition.
  • The findings offer a pathway for more biologically plausible and efficient neuromorphic systems.