SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo

Jimenez-Romero, Cristian; Johnson, Jeffrey

doi:10.1007/s00521-016-2398-1

Cited by 14 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In our setting, NetLogo helps us to observe and manipulate the state of every neuron and synapse. For the simulations, we have two backends: NEST (see Section 3.1.1) and SpikingLab (Jimenez-Romero and Johnson, 2017 ). SpikingLab is an engine directly integrated within NetLogo and can be easily and quickly used for small scale networks, as we present in our use case.…”

Section: Resultsmentioning

confidence: 99%

Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn

Yegenoglu

Subramoney

Hater

et al. 2022

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the development of tools and strategies to efficiently find these regions of high importance to advance brain research. Exploring the high dimensional parameter space using numerical simulations has been a frequently used technique in the last years in many areas of computational neuroscience. Today, high performance computing (HPC) can provide a powerful infrastructure to speed up explorations and increase our general understanding of the behavior of the model in reasonable times. Learning to learn (L2L) is a well-known concept in machine learning (ML) and a specific method for acquiring constraints to improve learning performance. This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model, or a whole brain simulation. In this work, we present L2L as an easy to use and flexible framework to perform parameter and hyper-parameter space exploration of neuroscience models on HPC infrastructure. Learning to learn is an implementation of the L2L concept written in Python. This open-source software allows several instances of an optimization target to be executed with different parameters in an embarrassingly parallel fashion on HPC. L2L provides a set of built-in optimizer algorithms, which make adaptive and efficient exploration of parameter spaces possible. Different from other optimization toolboxes, L2L provides maximum flexibility for the way the optimization target can be executed. In this paper, we show a variety of examples of neuroscience models being optimized within the L2L framework to execute different types of tasks. The tasks used to illustrate the concept go from reproducing empirical data to learning how to solve a problem in a dynamic environment. We particularly focus on simulations with models ranging from the single cell to the whole brain and using a variety of simulation engines like NEST, Arbor, TVB, OpenAIGym, and NetLogo.

show abstract

Section: Resultsmentioning

confidence: 99%

Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn

Yegenoglu

Subramoney

Hater

et al. 2022

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Iwadate et al ( 2014 ) used light sensors in a target-reaching task to punish wrongful behavior. Jimenez-Romero et al ( 2015 , 2016 ) and Jimenez-Romero ( 2017 ), implemented a virtual ant that learns to associate olfactory sensor input with different behaviors through a single-layer SNN. The robot was able to learn to recognize rewarding and harmful stimuli as well as simple navigation in a simulated environment.…”

Section: Learning and Robotics Applicationsmentioning

confidence: 99%

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

et al. 2018

View full text Add to dashboard Cite

Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.

show abstract

“…Machine learning is used in many areas of life. In addition to their well-known use in image, objects, sounds, recognition tasks [1][2][3] they are beginning to be actively applied to the tasks of controlling objects [4][5][6][7][8][9]. The main advantage is that machine learning algorithms allow not only to process data but also to use it for learning and prediction.…”

Section: Introductionmentioning

confidence: 99%

Stabilization of a pendulum on an elastic foundation using a multilayer perceptron

Ivanova

Kharin

Smirnova

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The article describes the solution to the problem of stabilizing a nonlinear system using machine learning methods. Despite the existence of classical approaches for solving the optimal control problem, the need for other approaches still remains. Nowadays, solving this class of problems using artificial intelligence has become popular Neural networks are one of the promising directions in this area. The article describes a model of multilayer perceptron. A pendulum on an elastic foundation was chosen as a dynamic system for the study. To train the neural network supervised learning (backpropagation) algorithm was used. Different variants of the training sample were proposed: with pre-learning and synchronous learning. The article shows the process of stabilization of a pendulum on an elastic foundation. The closed system was built and a method for a numerical solution was implemented.

show abstract

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo

Cited by 14 publications

References 19 publications

Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn

Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

Stabilization of a pendulum on an elastic foundation using a multilayer perceptron

Contact Info

Product

Resources

About