scholarly journals Integrating Non-spiking Interneurons in Spiking Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Beck Strohmer ◽  
Rasmus Karnøe Stagsted ◽  
Poramate Manoonpong ◽  
Leon Bonde Larsen
Keyword(s):  
Neural Networks ◽  
Closed Loop ◽  
Spiking Neurons ◽  
Legged Robots ◽  
Spiking Neuron ◽  
Mixed Network ◽  
Spiking Neuron Model ◽  
Network Output ◽  
Mixed Networks ◽  
Event Based

Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well-researched biological example of such a mixed network is a sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This type of pathway is also well-researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network inspired by the internal feedback loops found in insects for posturing. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

scholarly journals Integrating Non-Spiking Interneurons in Spiking Neural Networks

2020 ◽  
Author(s):  
Beck Strohmer ◽  
Rasmus Karnøe Stagsted ◽  
Poramate Manoonpong ◽  
Leon Bonde Larsen
Keyword(s):  
Neural Networks ◽  
Closed Loop ◽  
Spiking Neurons ◽  
Legged Robots ◽  
Spiking Neuron ◽  
Mixed Network ◽  
Spiking Neuron Model ◽  
Network Output ◽  
Mixed Networks ◽  
Event Based

AbstractResearchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well researched biological example of such a mixed network is the sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This pathway is also well researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

A Very Simple Spiking Neuron Model That Allows for Modeling of Large, Complex Systems

2008 ◽  
Vol 20 (1) ◽  
pp. 65-90 ◽  
Author(s):  
Jeffrey J. Lovelace ◽  
Krzysztof J. Cios
Keyword(s):  
Action Potentials ◽  
Neuron Model ◽  
Azimuthal Angle ◽  
Spiking Neurons ◽  
Spiking Neuron ◽  
Neuron Models ◽  
Activation Patterns ◽  
Arrival Times ◽  
Object Locations ◽  
Spiking Neuron Model

This letter introduces a biologically inspired very simple spiking neuron model. The model retains only crucial aspects of biological neurons: a network of time-delayed weighted connections to other neurons, a threshold-based generation of action potentials, action potential frequency proportional to stimulus intensity, and interneuron communication that occurs with time-varying potentials that last longer than the associated action potentials. The key difference between this model and existing spiking neuron models is its great simplicity: it is basically a collection of linear and discontinuous functions with no differential equations to solve. The model's ability to operate in a complex network was tested by using it as a basis of a network implementing a hypothetical echolocation system. The system consists of an emitter and two receivers. The outputs of the receivers are connected to a network of spiking neurons (using the proposed model) to form a detection grid that acts as a map of object locations in space. The network uses differences in the arrival times of the signals to determine the azimuthal angle of the source and time of flight to calculate the distance. The activation patterns observed indicate that for a network of spiking neurons, which uses only time delays to determine source locations, the spatial discrimination varies with the number and relative spacing of objects. These results are similar to those observed in animals that use echolocation.

Mobile Robots' Wall-Following Controller Based on Probabilistic Spiking Neuron Model

2012 ◽  
Vol 588-589 ◽  
pp. 1547-1551 ◽  
Author(s):  
Xiu Qing Wang ◽  
Zeng Guang Hou ◽  
Min Tan ◽  
Yong Ji Wang ◽  
Fei Xie
Keyword(s):  
Neural Networks ◽  
Mobile Robots ◽  
Motor Neurons ◽  
Neuron Model ◽  
Hebbian Learning ◽  
The Novel ◽  
Spiking Neuron ◽  
Learning Rules ◽  
Spiking Neuron Model ◽  
Wall Following

This paper focuses on the third generation of neural networks- Spiking neural networks (SNNs), the novel Spiking neuron model- probabilistic Spiking neuron model (pSNM), and their applications. pSNM is used in mobile robots' behavior control, and a novel mobile robots' wall-following controller based on pSNM is proposed. In the pSNM controller, Spiking time-delayed coding is used for the sensory neurons of the input layer and pSNM is used for the motor neurons in the output layer. Thorpe and Hebbian learning rules are used in the controller. The experimental results show that the controller can control the mobile robots to follow the wall clockwise and counterclockwise successfully. The structure of the controller is simple, and the controller can study online.

Neuroscience-Inspired Parameter Selection of Spiking Neuron Using Hodgkin Huxley Model

2021 ◽  
Vol 13 (2) ◽  
pp. 89-106
Author(s):  
Ruchi Holker ◽  
Seba Susan
Keyword(s):  
Neural Networks ◽  
Spiking Neurons ◽  
Spiking Neural Networks ◽  
Model Parameters ◽  
Spiking Neuron ◽  
Artificial Brain ◽  
Class 1 ◽  
Optimal Values ◽  
Neuron Simulator ◽  
Selection Of

Spiking neural networks (SNN) are currently being researched to design an artificial brain to teach it how to think, perform, and learn like a human brain. This paper focuses on exploring optimal values of parameters of biological spiking neurons for the Hodgkin Huxley (HH) model. The HH model exhibits maximum number of neurocomputational properties as compared to other spiking models, as per previous research. This paper investigates the HH model parameters of Class 1, Class 2, phasic spiking, and integrator neurocomputational properties. For the simulation of spiking neurons, the NEURON simulator is used since it is easy to understand and code.

scholarly journals Spiking PID Control Applied in the Van de Vusse Reaction

2021 ◽  
Vol 1 (4) ◽  
pp. 488-500
Author(s):  
Carlos Antonio Márquez-Vera ◽  
Zaineb Yakoub ◽  
Marco Antonio Márquez Vera ◽  
Alfian Ma'arif
Keyword(s):  
Neural Networks ◽  
Chemical Reaction ◽  
Pid Control ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Spiking Neuron ◽  
Control Applications ◽  
Real Behavior ◽  
Simulation Results ◽  
Inverse Response

Artificial neural networks (ANN) can approximate signals and give interesting results in pattern recognition; some works use neural networks for control applications. However, biological neurons do not generate similar signals to the obtained by ANN.  The spiking neurons are an interesting topic since they simulate the real behavior depicted by biological neurons. This paper employed a spiking neuron to compute a PID control, which is further applied to the Van de Vusse reaction. This reaction, as the inverse pendulum, is a benchmark used to work with systems that has inverse response producing the output to undershoot. One problem is how to code information that the neuron can interpret and decode the peak generated by the neuron to interpret the neuron's behavior. In this work, a spiking neuron is used to compute a PID control by coding in time the peaks generated by the neuron. The neuron has as synaptic weights the PID gains, and the peak observed in the axon is the coded control signal. The neuron adaptation tries to obtain the necessary weights to generate the peak instant necessary to control the chemical reaction. The simulation results show the possibility of using this kind of neuron for control issues and the possibility of using a spiking neural network to overcome the undershoot obtained due to the inverse response of the chemical reaction.

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

scholarly journals Exploring Optimized Spiking Neural Network Architectures for Classification Tasks on Embedded Platforms

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3240
Author(s):  
Tehreem Syed ◽  
Vijay Kakani ◽  
Xuenan Cui ◽  
Hakil Kim
Keyword(s):  
Neural Networks ◽  
Gradient Descent ◽  
Spiking Neural Networks ◽  
License Plate ◽  
Training Techniques ◽  
Neuromorphic Hardware ◽  
Private And Public ◽  
Embedded Platforms ◽  
Public Datasets ◽  
Event Based

In recent times, the usage of modern neuromorphic hardware for brain-inspired SNNs has grown exponentially. In the context of sparse input data, they are undertaking low power consumption for event-based neuromorphic hardware, specifically in the deeper layers. However, using deep ANNs for training spiking models is still considered as a tedious task. Until recently, various ANN to SNN conversion methods in the literature have been proposed to train deep SNN models. Nevertheless, these methods require hundreds to thousands of time-steps for training and still cannot attain good SNN performance. This work proposes a customized model (VGG, ResNet) architecture to train deep convolutional spiking neural networks. In this current study, the training is carried out using deep convolutional spiking neural networks with surrogate gradient descent backpropagation in a customized layer architecture similar to deep artificial neural networks. Moreover, this work also proposes fewer time-steps for training SNNs with surrogate gradient descent. During the training with surrogate gradient descent backpropagation, overfitting problems have been encountered. To overcome these problems, this work refines the SNN based dropout technique with surrogate gradient descent. The proposed customized SNN models achieve good classification results on both private and public datasets. In this work, several experiments have been carried out on an embedded platform (NVIDIA JETSON TX2 board), where the deployment of customized SNN models has been extensively conducted. Performance validations have been carried out in terms of processing time and inference accuracy between PC and embedded platforms, showing that the proposed customized models and training techniques are feasible for achieving a better performance on various datasets such as CIFAR-10, MNIST, SVHN, and private KITTI and Korean License plate dataset.

scholarly journals A Review of Algorithms and Hardware Implementations for Spiking Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 23
Author(s):  
Duy-Anh Nguyen ◽  
Xuan-Tu Tran ◽  
Francesca Iacopi
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Training Algorithms ◽  
Current State ◽  
Hardware Implementations ◽  
Neuromorphic Hardware ◽  
High Level ◽  
Event Based ◽  
Hardware Platforms

Deep Learning (DL) has contributed to the success of many applications in recent years. The applications range from simple ones such as recognizing tiny images or simple speech patterns to ones with a high level of complexity such as playing the game of Go. However, this superior performance comes at a high computational cost, which made porting DL applications to conventional hardware platforms a challenging task. Many approaches have been investigated, and Spiking Neural Network (SNN) is one of the promising candidates. SNN is the third generation of Artificial Neural Networks (ANNs), where each neuron in the network uses discrete spikes to communicate in an event-based manner. SNNs have the potential advantage of achieving better energy efficiency than their ANN counterparts. While generally there will be a loss of accuracy on SNN models, new algorithms have helped to close the accuracy gap. For hardware implementations, SNNs have attracted much attention in the neuromorphic hardware research community. In this work, we review the basic background of SNNs, the current state and challenges of the training algorithms for SNNs and the current implementations of SNNs on various hardware platforms.

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