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Found 9 papers, 3 papers with code
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 neuromorphic hardware.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Sensory complexity and global gain in a DCNN codetermine optimal arousal state
By leveraging the full observability of our model, we reconcile conflicting findings from previous studies on sensory processing, by showing that both linear as well inverted-U-shaped gain profiles emerge in the interaction of hierarchical sensory processing and global arousal changes.
An image representation based convolutional network for DNA classification
The folding structure of the DNA molecule combined with helper molecules, also referred to as the chromatin, is highly relevant for the functional properties of DNA.
Gating out sensory noise in a spike-based Long Short-Term Memory network
These adaptive spiking neurons implement an adaptive form of sigma-delta coding to convert internally computed analog activation values to spike-trains.
A Deep Predictive Coding Network for Learning Latent Representations
In this paper, we describe an algorithm to build a deep generative model using predictive coding that can be used to infer latent representations about the stimuli received from external environment.
Efficient Computation in Adaptive Artificial Spiking Neural Networks
Adaptive spike-time coding additionally allows for the dynamic control of neural coding precision: we show how a simple model of arousal in AdSNNs further halves the average required firing rate and this notion naturally extends to other forms of attention.
Conditional Time Series Forecasting with Convolutional Neural Networks
The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning is performed by applying multiple convolutional filters in parallel to separate time series which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series.
Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting
When a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed.
