Jaldert O Rombouts

• PhD
• Senior Researcher at Vicarious AI

Many theories propose that top-down attentional signals control processing in sensory cortices by modulating neural activity. But who controls the controller? Here we investigate how a biologically plausible neural reinforcement learning scheme can create higher order representations and top-down attentional signals. The learning scheme trains neur...

Intelligence is our ability to learn appropriate responses to new stimuli and situations. Neurons in association cortex are thought to be essential for this ability. During learning these neurons become tuned to relevant features and start to represent them with persistent activity during memory delays. This learning process is not well understood....

Spiking neural networks are characterised by the spiking neu-ron models they use and how these spiking neurons process information communicated through spikes – the neural code. We demonstrate a plau-sible spiking neural network based on Spike Response Models and predic-tive spike-coding. When combined with a plausible reinforcement learning strate...

Working memory is a key component of intelligence that the brain implements as persistent neural activations. How do persistent neurons learn to store information, and how can they be made to forget this information once it is no longer relevant? When animals learn episodic tasks, neurons in prefrontal cortex learn to represent task ends. We show t...

Humans and animals have the ability to perform very precise movements to obtain rewards. For instance, it is no problem at all to pick up a mug of coffee from your desk while you are working. Unfortunately, it is unknown how exactly the non-linear mapping between sensory inputs (e.g. your mug on the retina) and the correct motor actions (e.g. a set...

How does the brain learn to map multi-dimensional sensory inputs to multi-dimensional motor outputs when it can only observe single rewards for the coordinated outputs of the whole network of neurons that make up the brain? We introduce Multi-AGREL, a novel, biologically plausible multi-layer neural network model for multi-dimensional reinforcement...

A key function of brains is undoubtedly the abstraction and maintenance of information from the environment for later use. Neurons in association cortex play an important role in this process: by learning these neurons become tuned to relevant features and represent the information that is required later as a persistent elevation of their activity....

Almost all animal behaviors can be seen as sequences of actions towards achieving certain goals. How the association cortices learn to link sensory stimuli to a correct sequence of motor responses is not well understood, especially when only a correct sequence of responses is rewarding. We present a biologically plausible neuronal network model th...

Recent experimental work has suggested that the neural firing rate can be interpreted as a fractional derivative, at least when signal variation induces neural adaptation. 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 kern...

Recent experimental work has suggested that the neural firing rate can be interpreted as a fractional derivative, at least when signal variation in-duces neural adaptation. 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 ker...

A simple hybrid movie recommender system is described that combines content based and collaborative modelling and pro-vides an explanation for increased user acceptance. The sys-tem uses rating data from the Netflix database which is linked to content information from the Internet Movie Database. In order to provide the user with insight into the r...

In the context of the SnackBot project at Carnegie Mellon's Robotics Institute the feasibility of using simple soft-biometric features to recognize persons at various distances and orientations with respect to a static robot in an indoor office setting was investigated. The features that were investigated were person height and several color featur...