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Evolving spiking networks with variable resistive memories.

Gerard Howard1, Larry Bull, Ben de Lacy Costello

  • 1Department of Computer Science, University of the West of England, Bristol, BS16 1QY, UK david4.howard@uwe.ac.uk.

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Neuromorphic computing utilizes brainlike systems with adaptive learning. Variable resistive memories in spiking neural networks enhance performance in robotic tasks compared to static or standard connections.

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Area of Science:

  • Neuromorphic Engineering
  • Artificial Intelligence
  • Materials Science

Background:

  • Neuromorphic computing mimics the brain for efficient information processing.
  • Adaptive learning is crucial for these systems, often implemented using spiking neural networks.
  • Plastic resistive memories serve as synapses, enabling synaptic plasticity.

Purpose of the Study:

  • To investigate the efficacy of variable resistive memories as synapses in spiking neuro-evolutionary systems.
  • To compare the performance of networks with variable resistive memories against static resistive memories and standard connections.
  • To explore the impact of synapse conductance profiles on network adaptability and performance.

Main Methods:

  • Employing a spiking neuro-evolutionary system with parameter self-adaptation for autonomous network design (topology and synaptic weights).
  • Implementing variable resistive memories with unique, evolvable conductance profiles for each synapse.
  • Evaluating network performance on a dynamic-reward robotic scenario with inherent noise.

Main Results:

  • Networks incorporating variable resistive memories demonstrated superior performance compared to those with static resistive memories and standard connections.
  • The enhanced behavioral degrees of freedom offered by variable resistive memories contributed to improved adaptability and task success.
  • Synaptic conductance profiles could be effectively evolved, influencing the plastic behavior of the neuromorphic system.

Conclusions:

  • Variable resistive memories represent a promising advancement for neuromorphic computing, offering greater adaptability and performance.
  • Evolving synapse characteristics is a viable strategy for optimizing neuromorphic network function.
  • This research highlights the potential of materials with tunable properties for next-generation intelligent systems.