Publications by Claudio Mattiussi

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

The significant increase in the available computational power that took place in recent decades has been accompanied by a growing interest in the application of the evolutionary approach to the synthesis of many kinds of systems and, in particular, to the ...

In this paper, we describe the artificial evolution of adaptive neural controllers for an outdoor mobile robot equipped with a mobile camera. The robot can dynamically select the gazing direction by moving the body and/or the camera. The neural control sys ...

Artiﬁcial neural networks, electronic circuits, and gene networks are some examples of systems that can be modeled as networks, that is, as collections of interconnected nodes. In this paper we introduce the concept of terminal graph (t-graph for short), w ...

A large class of systems of biological and technological relevance can be described as analog networks, that is, collections of dynamical devices interconnected by links of varying strength. Some examples of analog networks are genetic regulatory networks, ...

The effective reverse engineering of biochemical networks is one of the great challenges of systems biology. The contribution of this paper is two-fold: 1) We introduce a new method for reverse engineering genetic regulatory networks from gene expression d ...

We describe a method for the online classification of sleep/wake states based on cardiorespiratory signals produced by wearable sensors. The method was conceived in view of its applicability to a wearable sleepiness monitoring device. The method uses a Fas ...

Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advantage. Recent studies suggest that neuromodulatory dynamics are a key factor in reg ...

The evolution of artificial neural networks (ANNs) is often used to tackle difficult control problems. There are different approaches to the encoding of neural networks in artificial genomes. Analog Genetic Encoding (AGE) is a new implicit method derived f ...

Artificial neural networks are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and ...

In this paper, we suggest a new approach for reverse engineering gene regulatory networks, which consists of using a reconstruction process that is similar to the evolutionary process that created these networks. The aim is to integrate prior knowledge int ...

Neuromodulation is thought to be one of the underlying principles of learning and memory in biological neural networks. Recent experiments have shown that neuroevolutionary methods benefit from neuromodulation in simple grid-world problems. In this paper w ...

In this paper we describe a new class of representations for real-valued parameters called Center of Mass Encoding (CoME). CoME is based on variable length strings, it is self-adaptive, and it permits the choice of the degree of redundancy of the genotype- ...

The output of reverse engineering methods for biological networks is often not a single network prediction, but an ensemble of networks that are consistent with the experimentally measured data. In this paper, we consider the problem of combining the infor ...

This paper describes a new kind of genetic representation called analog genetic encoding (AGE). The representation is aimed at the evolutionary synthesis and reverse engineering of circuits and networks such as analog electronic circuits, neural networks, ...

memory in biological neural networks. Similarly, artificial neural networks could benefit from modulatory dynamics when facing certain types of learning problem. Here we test this hypothesis by introducing modulatory neurons to enhance or dampen neural pla ...

We describe a method to classify online sleep/wake states of humans based on cardiorespiratory signals for wearable applications. The method is designed to be embedded in a portable microcontroller device and to cope with the resulting tight power and weig ...

Autonomous control of ultra-light indoor microflyers is a difficult and largely unsolved task because of the strong limitations on the kind of sensors that can be embedded. We propose a new approach for altitude control of a 10-gram microflyer, where altit ...

A method for the genetic representation of a network (100), the network having one or more devices (20, 30, 70, 80), each device comprising at least one terminal (21, 22, 23; 71, 72) connected to at least one other terminal (21, 22, 23; 71, 72, 61) by a li ...

Reverse engineering methods are typically first tested on simulated data from in silico networks, for systematic and efficient performance assessment, before an application to real biological networks. In this paper we present a method for generating biolo ...

Actigraphy for long-term sleep/wake monitoring fails to correctly classify situations where the subject displays low activity, but is awake. In this paper we propose a new algorithm which uses both accelerometer and cardio-respiratory signals to overcome t ...

Claudio Mattiussi

Chat with Graph Search

Evolutionary synthesis of analog networks

Active Vision and Receptive Field Development in Evolutionary Robots

Beyond Graphs: A New Synthesis

The Age of Analog Networks

Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks

Sleep and Wake Classification With ECG and Respiratory Effort Signals

Evolving Neuromodulatory Topologies for Reinforcement Learning-like Problems

Neuroevolution with Analog Genetic Encoding

Neuroevolution: from architectures to learning

Replaying the Evolutionary Tape: Biomimetic Reverse Engineering of Gene Networks

Evolvability of Neuromodulated Learning for Robots

Center of Mass Encoding: A self-adaptive representation with adjustable redundancy for real-valued parameters

Combining Multiple Results of a Reverse Engineering Algorithm: Application to the DREAM Five Gene Network Challenge

Analog Genetic Encoding for the Evolution of Circuits and Networks

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward- based Scenarios

Adaptive Sleep/Wake Classification Based on Cardiorespiratory Signals for Wearable Devices

Vision-based Altitude and Pitch Estimation for Ultra-light Indoor Microflyers

Method and device for the genetic representation and evolution of networks

Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods

Improving Actigraph Sleep/Wake Classification with Cardio-Respiratory Signals