Spiking neural state machine for gait frequency entrainment in a flexible modular robot

• URL: http://arxiv.org/abs/2007.07346v2
• Date: Fri, 25 Sep 2020 17:59:50 GMT
• Title: Spiking neural state machine for gait frequency entrainment in a flexible modular robot
• Authors: Alex Spaeth, Maryam Tebyani, David Haussler, Mircea Teodorescu
• Abstract summary: We propose a modular architecture for neuromorphic closed-loop control based on bistable relaxation oscillator modules consisting of three spiking neurons each. A concrete case study for the approach is provided by a modular robot constructed from flexible plastic volumetric pixels, in which we produce a forward crawling gait entrained to the natural frequency of the robot by a minimal system of twelve neurons organized into four modules.
• Score: 0.294944680995069
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a modular architecture for neuromorphic closed-loop control based on bistable relaxation oscillator modules consisting of three spiking neurons each. Like its biological prototypes, this basic component is robust to parameter variation but can be modulated by external inputs. By combining these modules, we can construct a neural state machine capable of generating the cyclic or repetitive behaviors necessary for legged locomotion. A concrete case study for the approach is provided by a modular robot constructed from flexible plastic volumetric pixels, in which we produce a forward crawling gait entrained to the natural frequency of the robot by a minimal system of twelve neurons organized into four modules.

Related papers

• Emergent Modularity in Pre-trained Transformers [127.08792763817496]
  We consider two main characteristics of modularity: functional specialization of neurons and function-based neuron grouping. We study how modularity emerges during pre-training, and find that the modular structure is stabilized at the early stage. It suggests that Transformers first construct the modular structure and then learn fine-grained neuron functions.
  arXiv  Detail & Related papers  (2023-05-28T11:02:32Z)
• Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity [0.0]
  We produce spiking neural systems that emulate the patterns of behavior of biological neurons in vitro. Our models were able to produce a level of network-wide synchrony. The genomes of the top-performing models indicate the excitability and density of connections in the model play an important role in determining the complexity of the produced activity.
  arXiv  Detail & Related papers  (2021-10-15T17:55:04Z)
• Versatile modular neural locomotion control with fast learning [6.85316573653194]
  Legged robots have significant potential to operate in highly unstructured environments. Currently, controllers must be either manually designed for specific robots or automatically designed via machine learning methods. We propose a simple yet versatile modular neural control structure with fast learning.
  arXiv  Detail & Related papers  (2021-07-16T12:12:28Z)
• Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agent [62.997667081978825]
  We use neural cellular automata to control a cart-pole agent. We trained the model using deep-Q learning, where the states of the output cells were used as the Q-value estimates to be optimized.
  arXiv  Detail & Related papers  (2021-06-29T10:49:42Z)
• Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis [91.3755431537592]
  This paper presents the Membrane Potential and Activation Threshold Homeostasis (MPATH) neuron model. The model allows neurons to maintain a form of dynamic equilibrium by automatically regulating their activity when presented with input. Experiments demonstrate the model's ability to adapt to and continually learn from its input.
  arXiv  Detail & Related papers  (2021-04-22T04:01:32Z)
• Neuromorphic adaptive spiking CPG towards bio-inspired locomotion of legged robots [58.720142291102135]
  Spiking Central Pattern Generator generates different locomotion patterns driven by an external stimulus. The locomotion of the end robotic platform (any-legged robot) can be adapted to the terrain by using any sensor as input.
  arXiv  Detail & Related papers  (2021-01-24T12:44:38Z)
• A Spiking Central Pattern Generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards [1.8139771201780368]
  We propose a spiking neural network and its implementation on neuromorphic hardware as a means to control a simulated lamprey model. We show that by modifying the input to the network, which can be provided by sensory information, the robot can be controlled dynamically in direction and pace. This category of spiking algorithms shows a promising potential to exploit the theoretical advantages of neuromorphic hardware in terms of energy efficiency and computational speed.
  arXiv  Detail & Related papers  (2021-01-18T11:04:16Z)
• Control for Multifunctionality: Bioinspired Control Based on Feeding in Aplysia californica [0.3277163122167433]
  We develop a hybrid Boolean model framework capable of modeling neural bursting activity and simple biomechanics at speeds faster than real time. We present a multifunctional model of Aplysia californica feeding that qualitatively reproduces three key feeding behaviors. We demonstrate that the model can be used for formulating testable hypotheses and discuss the implications of this approach for robotic control and neuroscience.
  arXiv  Detail & Related papers  (2020-08-11T19:26:50Z)
• Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped [64.64924554743982]
  We present a framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
  arXiv  Detail & Related papers  (2020-04-09T14:32:49Z)
• Flexible Transmitter Network [84.90891046882213]
  Current neural networks are mostly built upon the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons. We propose the Flexible Transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity. We present the Flexible Transmitter Network (FTNet), which is built on the most common fully-connected feed-forward architecture.
  arXiv  Detail & Related papers  (2020-04-08T06:55:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.