Comparing Evolutionary Strategy Algorithms for Training Spiking Neural Networks
نویسندگان
چکیده
منابع مشابه
Comparing Evolutionary Strategy Algorithms for Training Spiking Neural Networks
Spiking Neural Networks are considered as the third generation of Artificial Neural Networks, these neural networks naturally process spatio-temporal information. Spiking Neural Networks have been used in several fields and application areas; pattern recognition among them. For dealing with supervised pattern recognition task a gradientdescent based learning rule (Spike-prop) has been developed...
متن کاملAdversarial Training for Probabilistic Spiking Neural Networks
Classifiers trained using conventional empirical risk minimization or maximum likelihood methods are known to suffer dramatic performance degradations when tested over examples adversarially selected based on knowledge of the classifier’s decision rule. Due to the prominence of Artificial Neural Networks (ANNs) as classifiers, their sensitivity to adversarial examples, as well as robust trainin...
متن کاملTraining Deep Spiking Neural Networks Using Backpropagation
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signa...
متن کاملFeature Salience for Neural Networks: Comparing Algorithms
One of the key problems in the field of telemedicine is the prediction of the patient’s health state change based on incoming non-invasively measured vital data. Artificial Neural Networks (ANN) are a powerful statistical modeling tool suitable for this problem. Feature salience algorithms for ANN provide information about feature importance and help selecting relevant input variables. Looking ...
متن کاملSpatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
Compared with artificial neural networks (ANNs), spiking neural networks (SNNs) are promising to explore the brain-like behaviors since the spikes could encode more spatiotemporal information. Although existing schemes including pretraining from ANN or direct training based on backpropagation (BP) make the supervised training of SNNs possible, these methods only exploit the networks’ spatial do...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Research in Computing Science
سال: 2015
ISSN: 1870-4069
DOI: 10.13053/rcs-96-1-1