Search Results for author: Sander M. Bohte

Found 11 papers, 0 papers with code

Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time

no code implementations20 Dec 2021 Bojian Yin, Federico Corradi, Sander M. Bohte

When combined with a novel dynamic spiking neuron model, the Liquid-Time-Constant neuron, we show that SNNs trained with FPTT outperform online BPTT approximations, and approach or exceed offline BPTT accuracy on temporal classification tasks.

Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks

no code implementations12 Mar 2021 Bojian Yin, Federico Corradi, Sander M. Bohte

Inspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful models of neural computation, and also with the aim of extracting biological neurons' energy efficiency; the performance of such networks however has remained lacking compared to classical artificial neural networks (ANNs).

Audio Classification domain classification +2

DeepAGREL: Biologically plausible deep learning via direct reinforcement

no code implementations25 Sep 2019 Isabella Pozzi, Sander M. Bohte, Pieter R. Roelfsema

While much recent work has focused on biologically plausible variants of error-backpropagation, learning in the brain seems to mostly adhere to a reinforcement learning paradigm; biologically plausible neural reinforcement learning frameworks, however, were limited to shallow networks learning from compact and abstract sensory representations.

Image Classification reinforcement-learning +1

MuPNet: Multi-modal Predictive Coding Network for Place Recognition by Unsupervised Learning of Joint Visuo-Tactile Latent Representations

no code implementations16 Sep 2019 Oliver Struckmeier, Kshitij Tiwari, Shirin Dora, Martin J. Pearson, Sander M. Bohte, Cyriel MA Pennartz, Ville Kyrki

Extracting and binding salient information from different sensory modalities to determine common features in the environment is a significant challenge in robotics.

LocalNorm: Robust Image Classification through Dynamically Regularized Normalization

no code implementations18 Feb 2019 Bojian Yin, Siebren Schaafsma, Henk Corporaal, H. Steven Scholte, Sander M. Bohte

While modern convolutional neural networks achieve outstanding accuracy on many image classification tasks, they are, compared to humans, much more sensitive to image degradation.

Classification General Classification +1

Generalisation in fully-connected neural networks for time series forecasting

no code implementations14 Feb 2019 Anastasia Borovykh, Cornelis W. Oosterlee, Sander M. Bohte

In this paper we study the generalization capabilities of fully-connected neural networks trained in the context of time series forecasting.

Learning Theory Time Series +1

Pricing options and computing implied volatilities using neural networks

no code implementations25 Jan 2019 Shuaiqiang Liu, Cornelis W. Oosterlee, Sander M. Bohte

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods.

Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

no code implementations7 Sep 2016 Davide Zambrano, Sander M. Bohte

It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate.

Open-Ended Question Answering

Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model

no code implementations NeurIPS 2012 Sander M. Bohte

Neural adaptation underlies the ability of neurons to maximize encoded information over a wide dynamic range of input stimuli.

Neurally Plausible Reinforcement Learning of Working Memory Tasks

no code implementations NeurIPS 2012 Jaldert Rombouts, Pieter Roelfsema, Sander M. Bohte

Neurons in association cortex play an important role in this process: during learning these neurons become tuned to relevant features and represent the information that is required later as a persistent elevation of their activity.

Decision Making reinforcement-learning +1

Fractionally Predictive Spiking Neurons

no code implementations NeurIPS 2010 Jaldert Rombouts, Sander M. Bohte

Here, we show that the actual neural spike-train itself can be considered as the fractional derivative, provided that the neural signal is approximated by a sum of power-law kernels.

Cannot find the paper you are looking for? You can Submit a new open access paper.