7 Result(s)
within
Sander Bohte
Computer Systems Organization and Communication Networks
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Chapter and Conference Paper
Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout
Spiking neural networks (SNNs) have gained attention as models of sparse and event-driven communication of biological neurons, and as such have shown increasing promise for energy-efficient applications in neu...
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Chapter and Conference Paper
A Taxonomy of Recurrent Learning Rules
Backpropagation through time (BPTT) is the de facto standard for training recurrent neural networks (RNNs), but it is non-causal and non-local. Real-time recurrent learning is a causal alternative, but it is h...
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Chapter and Conference Paper
LocalNorm: Robust Image Classification Through Dynamically Regularized Normalization
While modern convolutional neural networks achieve outstanding accuracy on many image classification tasks, they are, once trained, much more sensitive to image degradation compared to humans. Much of this sen...
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Chapter and Conference Paper
Gating Sensory Noise in a Spiking Subtractive LSTM
Spiking neural networks are being investigated both as biologically plausible models of neural computation and also as a potentially more efficient type of neural network. Recurrent neural networks in the form...
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Chapter and Conference Paper
A Deep Predictive Coding Network for Inferring Hierarchical Causes Underlying Sensory Inputs
Predictive coding has been argued as a mechanism underlying sensory processing in the brain. In computational models of predictive coding, the brain is described as a machine that constructs and continuously ...
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Chapter and Conference Paper
Continuous-Time Spike-Based Reinforcement Learning for Working Memory Tasks
As the brain purportedly employs on-policy reinforcement learning compatible with SARSA learning, and most interesting cognitive tasks require some form of memory while taking place in continuous-time, recent ...
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Article
Open AccessMulti-agent Pareto appointment exchanging in hospital patient scheduling
We present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve...
