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Integrating Non-spiking Interneurons in Spiking Neural Networks
Beck Strohmer1, Rasmus Karnøe Stagsted1, Poramate Manoonpong1
1SDU Biorobotics, Maersk McKinney Moller Institute, University of Southern Denmark, Odense, Denmark.
Frontiers in Neuroscience
|March 22, 2021
Summary
This study introduces a novel method to combine spiking and non-spiking neurons for adaptive control in robotics. This approach enables sensorimotor pathways to interpret analog input, enhancing legged robot locomotion.
Area of Science:
- Computational Neuroscience
- Robotics
- Artificial Intelligence
Background:
- Historically, neural network research bifurcated between computer-friendly non-spiking neurons and biologically plausible but hardware-intensive spiking neurons.
- Natural neural networks feature heterogeneous populations of both spiking and non-spiking neurons, each contributing unique advantages.
- Sensorimotor pathways, crucial for mapping sensory input to motor output, exemplify such mixed biological networks and are vital in robotics for legged robot control.
Purpose of the Study:
- To investigate the integration of spiking and non-spiking neurons within a sensorimotor pathway.
- To develop a method for creating non-spiking neurons capable of interpreting analog information and interfacing with spiking neurons.
- To explore new network architectures for adaptive controllers, particularly for improving legged robot locomotion.
Main Methods:
- Proposed a novel approach using sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron.
- Developed an event-based architecture for mixed spiking and non-spiking neural networks.
- Simulated a closed-loop amplitude regulating network inspired by insect posturing feedback loops.
Main Results:
- Demonstrated the creation of non-spiking neurons that can interpret analog input and communicate with spiking neurons.
- Validated the methodology through simulation of an insect-inspired closed-loop amplitude regulating network.
- Showcased the ability of non-spiking neurons to effectively manipulate post-synaptic spiking neurons in an event-based system.
Conclusions:
- Combining spiking and non-spiking neurons offers a promising avenue for developing advanced adaptive controllers.
- This hybrid approach can enhance the locomotion strategies and control capabilities of legged robots.
- The proposed method provides a foundation for exploring novel neural network architectures in computational neuroscience and robotics.
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