Parallel Event-Driven Neural Network Simulations Using the Hodgkin-Huxley Neuron Model

2005 ◽  
Author(s):  
C.J. Lobb ◽  
Zenas Chao ◽  
R.M. Fujimoto ◽  
S.M. Potter
Keyword(s):  
Neural Network ◽  
Neuron Model ◽  
Event Driven ◽  
Network Simulations

scholarly journals FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gianluca Susi ◽  
Pilar Garcés ◽  
Emanuele Paracone ◽  
Alessandro Cristini ◽  
Mario Salerno ◽  
...  
Keyword(s):  
Neuron Model ◽  
Neuronal Dynamics ◽  
Multi Scale ◽  
Spiking Neuron Model ◽  
Event Driven ◽  
Long Time ◽  
Hardware Configuration ◽  
Network Simulations ◽  
Spike Latency ◽  
New Strategies

AbstractNeural modelling tools are increasingly employed to describe, explain, and predict the human brain’s behavior. Among them, spiking neural networks (SNNs) make possible the simulation of neural activity at the level of single neurons, but their use is often threatened by the resources needed in terms of processing capabilities and memory. Emerging applications where a low energy burden is required (e.g. implanted neuroprostheses) motivate the exploration of new strategies able to capture the relevant principles of neuronal dynamics in reduced and efficient models. The recent Leaky Integrate-and-Fire with Latency (LIFL) spiking neuron model shows some realistic neuronal features and efficiency at the same time, a combination of characteristics that may result appealing for SNN-based brain modelling. In this paper we introduce FNS, the first LIFL-based SNN framework, which combines spiking/synaptic modelling with the event-driven approach, allowing us to define heterogeneous neuron groups and multi-scale connectivity, with delayed connections and plastic synapses. FNS allows multi-thread, precise simulations, integrating a novel parallelization strategy and a mechanism of periodic dumping. We evaluate the performance of FNS in terms of simulation time and used memory, and compare it with those obtained with neuronal models having a similar neurocomputational profile, implemented in NEST, showing that FNS performs better in both scenarios. FNS can be advantageously used to explore the interaction within and between populations of spiking neurons, even for long time-scales and with a limited hardware configuration.

Large Neural Network Simulations on Multiple Hardware Platforms

1997 ◽  
pp. 919-923 ◽  
Author(s):  
Per Hammarlund ◽  
Örjan Ekeberg ◽  
Tomas Wilhelmsson ◽  
Anders Lansner
Keyword(s):  
Neural Network ◽  
Network Simulations ◽  
Hardware Platforms

scholarly journals Neural Network Training Acceleration With RRAM-Based Hybrid Synapses

2021 ◽  
Vol 15 ◽  
Author(s):  
Wooseok Choi ◽  
Myonghoon Kwak ◽  
Seyoung Kim ◽  
Hyunsang Hwang
Keyword(s):  
Neural Network ◽  
Parallel Learning ◽  
Network Training ◽  
Multiple Devices ◽  
Network Simulations ◽  
Conductance States ◽  
Hardware Neural Network ◽  
Training Efficiency ◽  
Computation Speed ◽  
Intrinsic Gain

Hardware neural network (HNN) based on analog synapse array excels in accelerating parallel computations. To implement an energy-efficient HNN with high accuracy, high-precision synaptic devices and fully-parallel array operations are essential. However, existing resistive memory (RRAM) devices can represent only a finite number of conductance states. Recently, there have been attempts to compensate device nonidealities using multiple devices per weight. While there is a benefit, it is difficult to apply the existing parallel updating scheme to the synaptic units, which significantly increases updating process’s cost in terms of computation speed, energy, and complexity. Here, we propose an RRAM-based hybrid synaptic unit consisting of a “big” synapse and a “small” synapse, and a related training method. Unlike previous attempts, array-wise fully-parallel learning is possible with our proposed architecture with a simple array selection logic. To experimentally verify the hybrid synapse, we exploit Mo/TiOx RRAM, which shows promising synaptic properties and areal dependency of conductance precision. By realizing the intrinsic gain via proportionally scaled device area, we show that the big and small synapse can be implemented at the device-level without modifications to the operational scheme. Through neural network simulations, we confirm that RRAM-based hybrid synapse with the proposed learning method achieves maximum accuracy of 97 %, comparable to floating-point implementation (97.92%) of the software even with only 50 conductance states in each device. Our results promise training efficiency and inference accuracy by using existing RRAM devices.

Temporal Correlations in Presentation Order during Learning Affects Human Object Recognition

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 33-33
Author(s):  
G M Wallis ◽  
H H Bülthoff
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Recognition Performance ◽  
Presentation Order ◽  
Temporal Association ◽  
Spatial Correlations ◽  
Temporal Correlations ◽  
Human Object ◽  
Network Simulations ◽  
Recognition Learning

The view-based approach to object recognition supposes that objects are stored as a series of associated views. Although representation of these views as combinations of 2-D features allows generalisation to similar views, it remains unclear how very different views might be associated together to allow recognition from any viewpoint. One cue present in the real world other than spatial similarity, is that we usually experience different objects in temporally constrained, coherent order, and not as randomly ordered snapshots. In a series of recent neural-network simulations, Wallis and Baddeley (1997 Neural Computation9 883 – 894) describe how the association of views on the basis of temporal as well as spatial correlations is both theoretically advantageous and biologically plausible. We describe an experiment aimed at testing their hypothesis in human object-recognition learning. We investigated recognition performance of faces previously presented in sequences. These sequences consisted of five views of five different people's faces, presented in orderly sequence from left to right profile in 45° steps. According to the temporal-association hypothesis, the visual system should associate the images together and represent them as different views of the same person's face, although in truth they are images of different people's faces. In a same/different task, subjects were asked to say whether two faces seen from different viewpoints were views of the same person or not. In accordance with theory, discrimination errors increased for those faces seen earlier in the same sequence as compared with those faces which were not ( p<0.05).

Quaternion quantum neurocomputing

2020 ◽  
pp. 2040001
Author(s):  
Eduardo Bayro-Corrochano ◽  
Samuel Solis-Gamboa
Keyword(s):  
Neural Network ◽  
Geometric Algebra ◽  
Neuron Model ◽  
Quaternion Algebra ◽  
Quantum States ◽  
Robust Performance ◽  
Quantum Neural Network ◽  
Quantum Operator ◽  
New Type

Since the introduction of quaternion by Hamilton in 1843, quaternions have been used in a lot of applications. One of the most interesting qualities is that we can use quaternions to carry out rotations and operate on other quaternions; this characteristic of the quaternions inspired us to investigate how the quantum states and quantum operator work in the field of quaternions and how we can use it to construct a quantum neural network. This new type of quantum neural network (QNN) is developed in the quaternion algebra framework that is isomorphic to the rotor algebra [Formula: see text] of the geometric algebra and is based on the so-called qubit neuron model. The quaternion quantum neural network (QQNN) is tested and shows robust performance.

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