Abstract
The ability of neural networks to perform pattern recognition, classification and associative memory, is essential to applications such as image and speech recognition, natural language understanding, decision making etc. In spiking neural networks (SNNs), information is encoded as sparsely distributed train of spikes, which allows learning through the spike-timing dependent plasticity (STDP) property. SNNs can potentially achieve very large scale implementation and distributed learning due to the inherent asynchronous and sparse inter-neuron communications. In this work, we develop an efficient, scalable and flexible SNN simulator, which supports learning through STDP. The simulator is ideal for biologically inspired neuron models for computation but not for biologically realistic models. Bayesian neuron model for SNNs that is capable of online and fully-distributed STDP learning is introduced. The function of the simulator is validated using two networks representing two different applications from unsupervised feature extraction to inference based sentence construction.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 International Joint Conference on Neural Networks, IJCNN 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1044-1051 |
| Number of pages | 8 |
| Volume | 2016-October |
| ISBN (Electronic) | 9781509006199 |
| DOIs | |
| State | Published - Oct 31 2016 |
| Event | 2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada Duration: Jul 24 2016 → Jul 29 2016 |
Other
| Other | 2016 International Joint Conference on Neural Networks, IJCNN 2016 |
|---|---|
| Country | Canada |
| City | Vancouver |
| Period | 7/24/16 → 7/29/16 |
Fingerprint
ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
Simulation of Bayesian learning and inference on distributed stochastic spiking neural networks. / Ahmed, Khadeer; Shrestha, Amar; Qiu, Qinru.
2016 International Joint Conference on Neural Networks, IJCNN 2016. Vol. 2016-October Institute of Electrical and Electronics Engineers Inc., 2016. p. 1044-1051 7727313.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Simulation of Bayesian learning and inference on distributed stochastic spiking neural networks
AU - Ahmed, Khadeer
AU - Shrestha, Amar
AU - Qiu, Qinru
PY - 2016/10/31
Y1 - 2016/10/31
N2 - The ability of neural networks to perform pattern recognition, classification and associative memory, is essential to applications such as image and speech recognition, natural language understanding, decision making etc. In spiking neural networks (SNNs), information is encoded as sparsely distributed train of spikes, which allows learning through the spike-timing dependent plasticity (STDP) property. SNNs can potentially achieve very large scale implementation and distributed learning due to the inherent asynchronous and sparse inter-neuron communications. In this work, we develop an efficient, scalable and flexible SNN simulator, which supports learning through STDP. The simulator is ideal for biologically inspired neuron models for computation but not for biologically realistic models. Bayesian neuron model for SNNs that is capable of online and fully-distributed STDP learning is introduced. The function of the simulator is validated using two networks representing two different applications from unsupervised feature extraction to inference based sentence construction.
AB - The ability of neural networks to perform pattern recognition, classification and associative memory, is essential to applications such as image and speech recognition, natural language understanding, decision making etc. In spiking neural networks (SNNs), information is encoded as sparsely distributed train of spikes, which allows learning through the spike-timing dependent plasticity (STDP) property. SNNs can potentially achieve very large scale implementation and distributed learning due to the inherent asynchronous and sparse inter-neuron communications. In this work, we develop an efficient, scalable and flexible SNN simulator, which supports learning through STDP. The simulator is ideal for biologically inspired neuron models for computation but not for biologically realistic models. Bayesian neuron model for SNNs that is capable of online and fully-distributed STDP learning is introduced. The function of the simulator is validated using two networks representing two different applications from unsupervised feature extraction to inference based sentence construction.
UR - http://www.scopus.com/inward/record.url?scp=85007154529&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007154529&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2016.7727313
DO - 10.1109/IJCNN.2016.7727313
M3 - Conference contribution
AN - SCOPUS:85007154529
VL - 2016-October
SP - 1044
EP - 1051
BT - 2016 International Joint Conference on Neural Networks, IJCNN 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -