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Olivier Coenen

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4 papers

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NeurIPS Conference 2025 Conference Paper

PLEIADES: Building Temporal Kernels with Orthogonal Polynomials

Yan Ru Pei
Olivier Coenen

We introduce a class of neural networks named PLEIADES (PoLynomial Expansion In Adaptive Distributed Event-based Systems), which contains temporal convolution kernels generated from orthogonal polynomial basis functions. We focus on interfacing these networks with event-based data to perform online spatiotemporal classification and detection with low latency. By virtue of using structured temporal kernels and event-based data, we have the freedom to vary the sample rate of the data along with the discretization step-size of the network without additional finetuning. We experimented with three event-based benchmarks and obtained state-of-the-art results on all three by large margins with significantly smaller memory and compute costs. We achieved: 1) 99. 59% accuracy with 192K parameters on the DVS128 hand gesture recognition dataset and 100\% with a small additional output filter; 2) 99. 58% test accuracy with 277K parameters on the AIS 2024 eye tracking challenge; and 3) 0. 556 mAP with 576k parameters on the PROPHESEE 1 Megapixel Automotive Detection Dataset.

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NeurIPS Conference 2003 Conference Paper

Perception of the Structure of the Physical World Using Unknown Multimodal Sensors and Effectors

D. Philipona
J. k. O'regan
J. -p. Nadal
Olivier Coenen

Is there a way for an algorithm linked to an unknown body to infer by itself information about this body and the world it is in? Taking the case of space for example, is there a way for this algorithm to realize that its body is in a three dimensional world? Is it possible for this algorithm to discover how to move in a straight line? And more basically: do these questions make any sense at all given that the algorithm only has access to the very high-dimensional data consisting of its sensory inputs and motor outputs? We demonstrate in this article how these questions can be given a positive answer. We show that it is possible to make an algorithm that, by ana- lyzing the law that links its motor outputs to its sensory inputs, discovers information about the structure of the world regardless of the devices constituting the body it is linked to. We present results from simulations demonstrating a way to issue motor orders resulting in “fundamental” movements of the body as regards the structure of the physical world.

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NeurIPS Conference 1995 Conference Paper

A Dynamical Model of Context Dependencies for the Vestibulo-Ocular Reflex

Olivier Coenen
Terrence Sejnowski

The vestibulo-ocular reflex (VOR) stabilizes images on the retina during rapid head motions. The gain of the VOR (the ratio of eye to head rotation velocity) is typically around -1 when the eyes are focused on a distant target. However, to stabilize images accurately, the VOR gain must vary with context (eye position, eye vergence and head translation). We first describe a kinematic model of the VOR which relies solely on sensory information available from the semicircular canals (head rotation), the otoliths (head translation), and neural correlates of eye position and vergence angle. We then propose a dynamical model and compare it to the eye velocity responses measured in monkeys. The dynamical model repro(cid: 173) duces the observed amplitude and time course of the modulation of the VOR and suggests one way to combine the required neural signals within the cerebellum and the brain stem. It also makes predictions for the responses of neurons to multiple inputs (head rotation and translation, eye position, etc. ) in the oculomotor system.

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NeurIPS Conference 1992 Conference Paper

Biologically Plausible Local Learning Rules for the Adaptation of the Vestibulo-Ocular Reflex

Olivier Coenen
Terrence Sejnowski
Stephen Lisberger

The vestibulo-ocular reflex (VOR) is a compensatory eye movement that stabilizes images on the retina during head turns. Its magnitude, or gain, can be modified by visual experience during head movements. Possible learning mechanisms for this adaptation have been explored in a model of the oculomotor system based on anatomical and physiological con(cid: 173) straints. The local correlational learning rules in our model reproduce the adaptation and behavior of the VOR under certain parameter conditions. From these conditions, predictions for the time course of adaptation at the learning sites are made.

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