publications

2021

1. Probabilistic Predictions of SIS Epidemics on Networks Based on Population-Level Observations Zerenner, Tanja, Di Lauro, Francesco, Dashti, Masoumeh, Berthouze, Luc, and Kiss, Istvan Z. arXiv:2111.05369 [math, q-bio, stat] 2021 [Abs]

   We predict the future course of ongoing susceptible-infected-susceptible (SIS) epidemics on regular, Erd\H{o}s-R\’{e}nyi and Barab\’asi-Albert networks. It is known that the contact network influences the spread of an epidemic within a population. Therefore, observations of an epidemic, in this case at the population-level, contain information about the underlying network. This information, in turn, is useful for predicting the future course of an ongoing epidemic. To exploit this in a prediction framework, the exact high-dimensional stochastic model of an SIS epidemic on a network is approximated by a lower-dimensional surrogate model. The surrogate model is based on a birth-and-death process; the effect of the underlying network is described by a parametric model for the birth rates. We demonstrate empirically that the surrogate model captures the intrinsic stochasticity of the epidemic once it reaches a point from which it will not die out. Bayesian parameter inference allows for uncertainty about the model parameters and the class of the underlying network to be incorporated directly into probabilistic predictions. An evaluation of a number of scenarios shows that in most cases the resulting prediction intervals adequately quantify the prediction uncertainty. As long as the population-level data is available over a long-enough period, even if not sampled frequently, the model leads to excellent predictions where the underlying network is correctly identified and prediction uncertainty mainly reflects the intrinsic stochasticity of the spreading epidemic. For predictions inferred from shorter observational periods, uncertainty about parameters and network class dominate prediction uncertainty. The proposed method relies on minimal data and is numerically efficient, which makes it attractive either as a standalone inference and prediction scheme or in conjunction with other methods.

2. The Impact of Contact Structure and Mixing on Control Measures and Disease-Induced Herd Immunity in Epidemic Models: A Mean-Field Model Perspective Di Lauro, Francesco, Berthouze, Luc, Dorey, Matthew D., Miller, Joel C., and Kiss, István Z. Bulletin of Mathematical Biology 2021 [Abs] [DOI]

   The contact structure of a population plays an important role in transmission of infection. Many ‘structured models’ capture aspects of the contact pattern through an underlying network or a mixing matrix. An important observation in unstructured models of a disease that confers immunity is that once a fraction 1-1/\mathcalR_0 has been infected, the residual susceptible population can no longer sustain an epidemic. A recent observation of some structured models is that this threshold can be crossed with a smaller fraction of infected individuals, because the disease acts like a targeted vaccine, preferentially immunising higher-risk individuals who play a greater role in transmission. Therefore, a limited ‘first wave’ may leave behind a residual population that cannot support a second wave once interventions are lifted. In this paper, we set out to investigate this more systematically. While networks offer a flexible framework to model contact patterns explicitly, they suffer from several shortcomings: (i) high-fidelity network models require a large amount of data which can be difficult to harvest, and (ii) very few, if any, theoretical contact network models offer the flexibility to tune different contact network properties within the same framework. Therefore, we opt to systematically analyse a number of well-known mean-field models. These are computationally efficient and provide good flexibility in varying contact network properties such as heterogeneity in the number contacts, clustering and household structure or differentiating between local and global contacts. In particular, we consider the question of herd immunity under several scenarios. When modelling interventions as changes in transmission rates, we confirm that in networks with significant degree heterogeneity, the first wave of the epidemic confers herd immunity with significantly fewer infections than equivalent models with less or no degree heterogeneity. However, if modelling the intervention as a change in the contact network, then this effect may become much more subtle. Indeed, modifying the structure disproportionately can shield highly connected nodes from becoming infected during the first wave and therefore make the second wave more substantial. We strengthen this finding by using an age-structured compartmental model parameterised with real data and comparing lockdown periods implemented either as a global scaling of the mixing matrix or age-specific structural changes. Overall, we find that results regarding (disease-induced) herd immunity levels are strongly dependent on the model, the duration of the lockdown and how the lockdown is implemented in the model.

3. Inference of Brain Networks with Approximate Bayesian Computation – Assessing Face Validity with an Example Application in Parkinsonism West, Timothy O., Berthouze, Luc, Farmer, Simon F., Cagnan, Hayriye, and Litvak, Vladimir NeuroImage 2021 [Abs] [DOI]

   This paper describes and validates a novel framework using the Approximate Bayesian Computation (ABC) algorithm for parameter estimation and model selection in models of mesoscale brain network activity. We provide a proof of principle, first pass validation of this framework using a set of neural mass models of the cortico-basal ganglia thalamic circuit inverted upon spectral features from experimental in vivo recordings. This optimization scheme relaxes an assumption of fixed-form posteriors (i.e. the Laplace approximation) taken in previous approaches to inverse modelling of spectral features. This enables the exploration of model dynamics beyond that approximated from local linearity assumptions and so fit to explicit, numerical solutions of the underlying non-linear system of equations. In this first paper, we establish a face validation of the optimization procedures in terms of: (i) the ability to approximate posterior densities over parameters that are plausible given the known causes of the data; (ii) the ability of the model comparison procedures to yield posterior model probabilities that can identify the model structure known to generate the data; and (iii) the robustness of these procedures to local minima in the face of different starting conditions. Finally, as an illustrative application we show (iv) that model comparison can yield plausible conclusions given the known neurobiology of the cortico-basal ganglia-thalamic circuit in Parkinsonism. These results lay the groundwork for future studies utilizing highly nonlinear or brittle models that can explain time dependent dynamics, such as oscillatory bursts, in terms of the underlying neural circuits.

2020

1. PDE limits of stochastic SIS epidemics on networks Di Lauro, F., Croix, J.-C., Berthouze, L., and Kiss, I. Z. Journal of Complex Networks 2020 [Abs] [DOI]

   Stochastic epidemic models on networks are inherently high-dimensional and the resulting exact models are intractable numerically even for modest network sizes. Mean-field models provide an alternative but can only capture average quantities, thus offering little or no information about variability in the outcome of the exact process. In this article, we conjecture and numerically demonstrate that it is possible to construct partial differential equation (PDE)-limits of the exact stochastic susceptible-infected-susceptible epidemics on Regular, Erdős–Rényi, Barabási–Albert networks and lattices. To do this, we first approximate the exact stochastic process at population level by a Birth-and-Death process (BD) (with a state space of \Ø(N)\ rather than \Ø(2^N)\\) whose coefficients are determined numerically from Gillespie simulations of the exact epidemic on explicit networks. We numerically demonstrate that the coefficients of the resulting BD process are density-dependent, a crucial condition for the existence of a PDE limit. Extensive numerical tests for Regular, Erdős–Rényi, Barabási–Albert networks and lattices show excellent agreement between the outcome of simulations and the numerical solution of the Fokker–Planck equations. Apart from a significant reduction in dimensionality, the PDE also provides the means to derive the epidemic outbreak threshold linking network and disease dynamics parameters, albeit in an implicit way. Perhaps more importantly, it enables the formulation and numerical evaluation of likelihoods for epidemic and network inference as illustrated in a fully worked out example.

2. Network Inference from Population-Level Observation of Epidemics Di Lauro, F., Croix, J.-C., Dashti, M., Berthouze, L., and Kiss, I. Z. Scientific Reports 2020 [Abs] [DOI]

   Using the continuous-time susceptible-infected-susceptible (SIS) model on networks, we investigate the problem of inferring the class of the underlying network when epidemic data is only available at population-level (i.e., the number of infected individuals at a finite set of discrete times of a single realisation of the epidemic), the only information likely to be available in real world settings. To tackle this, epidemics on networks are approximated by a Birth-and-Death process which keeps track of the number of infected nodes at population level. The rates of this surrogate model encode both the structure of the underlying network and disease dynamics. We use extensive simulations over Regular, Erdős–Rényi and Barabási–Albert networks to build network class-specific priors for these rates. We then use Bayesian model selection to recover the most likely underlying network class, based only on a single realisation of the epidemic. We show that the proposed methodology yields good results on both synthetic and real-world networks.

3. Temporal ordering of input modulates connectivity formation in a developmental neuronal network model of the cortex Hartley, Caroline, Farmer, Simon, and Berthouze, Luc PLOS ONE 2020 [Abs] [DOI] [Code]

   Preterm infant brain activity is discontinuous; bursts of activity recorded using EEG (electroencephalography), thought to be driven by subcortical regions, display scale free properties and exhibit a complex temporal ordering known as long-range temporal correlations (LRTCs). During brain development, activity-dependent mechanisms are essential for synaptic connectivity formation, and abolishing burst activity in animal models leads to weak disorganised synaptic connectivity. Moreover, synaptic pruning shares similar mechanisms to spike-timing dependent plasticity (STDP), suggesting that the timing of activity may play a critical role in connectivity formation. We investigated, in a computational model of leaky integrate-and-fire neurones, whether the temporal ordering of burst activity within an external driving input could modulate connectivity formation in the network. Connectivity evolved across the course of simulations using an approach analogous to STDP, from networks with initial random connectivity. Small-world connectivity and hub neurones emerged in the network structure—characteristic properties of mature brain networks. Notably, driving the network with an external input which exhibited LRTCs in the temporal ordering of burst activity facilitated the emergence of these network properties, increasing the speed with which they emerged compared with when the network was driven by the same input with the bursts randomly ordered in time. Moreover, the emergence of small-world properties was dependent on the strength of the LRTCs. These results suggest that the temporal ordering of burst activity could play an important role in synaptic connectivity formation and the emergence of small-world topology in the developing brain.

4. The impact of network properties and mixing on control measures and disease-induced herd immunity in epidemic models: a mean-field model perspective Di Lauro, Francesco, Berthouze, Luc, Dorey, Matthew D., Miller, Joel C., and Kiss, István Z. arXiv:2007.06975 [physics, q-bio] 2020 [Abs] [arXiv]

   The contact structure of a population plays an important role in transmission of infection. Many “structured models” capture aspects of the contact structure through an underlying network or a mixing matrix. An important observation in such models, is that once a fraction \1-1/}mathcal{R}_0 has been infected, the residual susceptible population can no longer sustain an epidemic. A recent observation of some structured models is that this threshold can be crossed with a smaller fraction of infected individuals, because the disease acts like a targeted vaccine, preferentially immunizing higher-risk individuals who play a greater role in transmission. Therefore, a limited “first wave” may leave behind a residual population that cannot support a second wave once interventions are lifted. In this paper, we systematically analyse a number of mean-field models for networks and other structured populations to address issues relevant to the Covid-19 pandemic. In particular, we consider herd-immunity under several scenarios. We confirm that, in networks with high degree heterogeneity, the first wave confers herd-immunity with significantly fewer infections than equivalent models with lower degree heterogeneity. However, if modelling the intervention as a change in the contact network, then this effect might become more subtle. Indeed, modifying the structure can shield highly connected nodes from becoming infected during the first wave and make the second wave more substantial. We confirm this finding by using an age-structured compartmental model parameterised with real data and comparing lockdown periods implemented either as a global scaling of the mixing matrix or age-specific structural changes. We find that results regarding herd immunity levels are strongly dependent on the model, the duration of lockdown and how lockdown is implemented.

2019

1. Mechanistic Inference of Brain Network Dynamics with Approximate Bayesian Computation West, Timothy O., Berthouze, Luc, Farmer, Simon F., Cagnan, Hayriye, and Litvak, Vladimir bioRxiv 2019 [Abs] [DOI]

   \textlessp\textgreaterBrain networks and the dynamics that unfold upon them are of vital importance at many scales of systems neuroscience. The parameterization of generative models from empirical data (inverse modelling) has become of great utility to theoretical understanding in this domain. However, it has become difficult to infer mechanisms of spontaneous neural activity with existing tools due to the adoption of simplifying model assumptions that appeal to steady-state approximations of neural dynamics. In this work we describe a framework employing Approximate Bayesian Computation (ABC) for the inversion of neural models that can be used to flexibly model any statistical feature of empirically recorded data without assuming any specific form of dynamics. Further, we demonstrate how Bayesian model comparison can be applied to ABC fitted models to facilitate the selection of competing dynamical hypotheses. This work provides a validation of this approach in the context of a Parkinsonian electrophysiology, using a set of multisite recordings from Parkinsonian rats to demonstrate convergence, consistency, and uniqueness of parameter estimation; face validity of the model comparison procedures; and its utility in answering biologically relevant problems. From the validation and example applications presented here we conclude that the proposed framework is likely to be of value to a wide range of researchers aiming to explain how brain dynamics emerge from neural circuits.\textless/p\textgreater

2. Inferring Functional Connectivity From Time-Series of Events in Large Scale Network Deployments Messager, Antoine, Parisis, George, Kiss, Istvan Z., Harper, Robert, Tee, Phil, and Berthouze, Luc IEEE Transactions on Network and Service Management 2019 [Abs] [arXiv] [DOI]

   To respond rapidly and accurately to network and service outages, network operators must deal with a large number of events resulting from the interaction of various services operating on complex, heterogeneous and evolving networks. In this paper, we introduce the concept of functional connectivity as an alternative approach to monitoring those events. Commonly used in the study of brain dynamics, functional connectivity is defined in terms of the presence of statistical dependencies between nodes. Although a number of techniques exist to infer functional connectivity in brain networks, their straightforward application to commercial network deployments is severely challenged by: (a) non-stationarity of the functional connectivity, (b) sparsity of the time-series of events, and (c) absence of an explicit model describing how events propagate through the network or indeed whether they propagate. Thus, in this paper, we present a novel inference approach whereby two nodes are defined as forming a functional edge if they emit substantially more coincident or short-lagged events than would be expected if they were statistically independent. The output of the method is an undirected weighted graph, where the weight of an edge between two nodes denotes the strength of the statistical dependence between them. We develop a model of time-varying functional connectivity whose parameters are determined by maximising the model’s predictive power from one time window to the next. We assess the accuracy, efficiency and scalability of our method on two real datasets of network events spanning multiple months and on synthetic data for which ground truth is available. We compare our method against both a general-purpose time-varying network inference method and network management specific causal inference technique and discuss its merits in terms of sensitivity, accuracy and, importantly, scalability.

3. Network Inference from Population-Level Observation of Epidemics Di Lauro, F., Croix, J.-C., Dashti, M., Berthouze, L., and Kiss, I. Z. arXiv:1906.10966 [physics, q-bio] 2019 [Abs] [arXiv]

   The network paradigm is widely accepted as the gold standard in modelling complex systems such as epidemics or neuronal activity in the brain; however, in most cases, the exact nature of the network on which such dynamics unfold is unknown. This has motivated a significant amount of work on network inference. Whilst a large body of work is concerned with inferring network structure based on detailed node-level temporal data, in this work we tackle the more challenging scenario of inferring the family of the underlying network when only population-level temporal incidence data are available. A key obstacle is the forbiddingly high dimensionality of the resulting stochastic epidemic model. To tackle this, we approximate the susceptible-infected-susceptible (SIS) model on networks by a Birth-Death process, whose rates encode the structure of the underlying network and disease dynamics. Using systematic simulations, we propose a parsimonious (three-parameter) model of these rates and show that different wellknown network families map onto distinct regions of the parameter space of this model. This result provides an a priori characterisation of different network families. Then, given populationlevel temporal epidemic data, we employ a Bayesian classifier to derive posterior distributions over diferent network families. We show that the proposed methodology yields excellent results when tested on synthetic and real-world networks. Our framework extends readily to many network families and spreading processes and it could provide a new benchmark in network inference from population-level data.

4. Functional Topology Inference from Network Events Messager, A., Parisis, G., Kiss, I. Z., Harper, R., Tee, P., and Berthouze, L. In 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) 2019 [Abs]

   In this paper we present a novel approach for inferring functional connectivity within a large-scale network from time series of emitted node events. We do so under the following constraints: (a) non-stationarity of the underlying connectivity, (b) sparsity of the time-series of events, and (c) absence of an explicit model describing how events propagate through the network. We develop an inference method whose output is an undirected weighted network, where the weight of an edge between two nodes denotes the probability of these nodes being functionally connected. Two nodes are assumed to be functionally connected if they show significantly more coincident or short-lagged events than randomly picked pairs of nodes with similar levels of activity. We develop a model of time-varying connectivity whose parameters are determined by maximising the model’s predictive power from one time window to the next. We assess the accuracy, efficiency and scalability of our method on a real dataset of network events spanning multiple months.

5. Epidemic threshold in pairwise models for clustered networks: closures and fast correlations Barnard, Rosanna C., Berthouze, Luc, Simon, Péter L., and Kiss, István Z. Journal of Mathematical Biology 2019 [Abs] [DOI]

   The epidemic threshold is probably the most studied quantity in the modelling of epidemics on networks. For a large class of networks and dynamics, it is well studied and understood. However, it is less so for clustered networks where theoretical results are mostly limited to idealised networks. In this paper we focus on a class of models known as pairwise models where, to our knowledge, no analytical result for the epidemic threshold exists. We show that by exploiting the presence of fast variables and using some standard techniques from perturbation theory we are able to obtain the epidemic threshold analytically. We validate this new threshold by comparing it to the threshold based on the numerical solution of the full system. The agreement is found to be excellent over a wide range of values of the clustering coefficient, transmission rate and average degree of the network. Interestingly, we find that the analytical form of the threshold depends on the choice of closure, highlighting the importance of model selection when dealing with real-world epidemics. Nevertheless, we expect that our method will extend to other systems in which fast variables are present.

6. A new method for the robust characterisation of pairwise statistical dependency between point processes Messager, Antoine, Georgiou, Nicos, and Berthouze, Luc 2019 [Abs] [arXiv]

   The robust detection of statistical dependencies between the components of a complex system is a key step in gaining a network-based understanding of the system. Because of their simplicity and low computation cost, pairwise statistics are commonly used in a variety of fields. Those approaches, however, typically suffer from one or more limitations such as lack of confidence intervals requiring reliance on surrogate data, sensitivity to binning, sparsity of the signals, or short duration of the records. In this paper we develop a method for assessing pairwise dependencies in point processes that overcomes these challenges. Given two point processes \X and \Y each emitting a given number of events \m and \n in a fixed period of time \T we derive exact analytical expressions for the expected value and standard deviation of the number of pairs events \X_i,Y_j separated by a delay of less than \τ\ one should expect to observe if \X and \Y were i.i.d. uniform random variables. We prove that this statistic is normally distributed in the limit of large \T which enables the definition of a Z-score characterising the likelihood of the observed number of coincident events happening by chance. We numerically confirm the analytical results and show that the property of normality is robust in a wide range of experimental conditions. We then experimentally demonstrate the predictive power of the method using a noisy version of the common shock model. Our results show that our approach has excellent behaviour even in scenarios with low event density and/or when the recordings are short.

7. Mapping Structural Diversity in Networks Sharing a Given Degree Distribution and Global Clustering: Adaptive Resolution Grid Search Evolution with Diophantine Equation-Based Mutations Overbury, Peter, Kiss, István Z., and Berthouze, Luc 2019 [DOI]

2018

1. Relating Vertex and Global Graph Entropy in Randomly Generated Graphs Tee, Philip, Parisis, George, Berthouze, Luc, and Wakeman, Ian Entropy 2018 [DOI]
2. Braindevbursts: Code For Temporal Ordering Of Input Modulates Connectivity Formation In A Developmental Neuronal Network Model Of The Cortex Hartley, Caroline, and Berthouze, Luc 2018 [Abs] [DOI]

   Code for paper: "Temporal ordering of input modulates connectivity formation in a developmental neuronal network model of the cortex" by Caroline Hartley, Simon Farmer and Luc Berthouze, available as bioRxiv 355800; doi: https://doi.org/10.1101/355800.

3. Propagation of beta/gamma rhythms in the cortico-basal ganglia circuits of the parkinsonian rat West, Timothy O., Berthouze, Luc, Halliday, David M., Litvak, Vladimir, Sharott, Andrew, Magill, Peter J., and Farmer, Simon F. Journal of Neurophysiology 2018 [Abs] [DOI]

   Much of the motor impairment associated with Parkinson’s disease is thought to arise from pathological activity in the networks formed by the basal ganglia (BG) and motor cortex. To evaluate several hypotheses proposed to explain the emergence of pathological oscillations in parkinsonism, we investigated changes to the directed connectivity in BG networks following dopamine depletion. We recorded local field potentials (LFPs) in the cortex and basal ganglia of rats rendered parkinsonian by injection of 6-hydroxydopamine (6-OHDA) and in dopamine-intact controls. We performed systematic analyses of the networks using a novel tool for estimation of directed interactions (nonparametric directionality, NPD). We used a "conditioned" version of the NPD analysis that reveals the dependence of the correlation between two signals on a third reference signal. We find evidence of the dopamine dependency of both low-beta (14-20 Hz) and high-beta/low-gamma (20-40 Hz) directed network interactions. Notably, 6-OHDA lesions were associated with enhancement of the cortical "hyperdirect" connection to the subthalamic nucleus (STN) and its feedback to the cortex and striatum. We find that pathological beta synchronization resulting from 6-OHDA lesioning is widely distributed across the network and cannot be located to any individual structure. Furthermore, we provide evidence that high-beta/gamma oscillations propagate through the striatum in a pathway that is independent of STN. Rhythms at high beta/gamma show susceptibility to conditioning that indicates a hierarchical organization compared with those at low beta. These results further inform our understanding of the substrates for pathological rhythms in salient brain networks in parkinsonism. NEW & NOTEWORTHY We present a novel analysis of electrophysiological recordings in the cortico-basal ganglia network with the aim of evaluating several hypotheses concerning the origins of abnormal brain rhythms associated with Parkinson’s disease. We present evidence for changes in the directed connections within the network following chronic dopamine depletion in rodents. These findings speak to the plausibility of a "short-circuiting" of the network that gives rise to the conditions from which pathological synchronization may arise.

4. Network events in a large commercial network: What can we learn? Messager, Antoine, Parisis, George, Harper, Robert, Tee, Philip, Kiss, Istvan Z., and Berthouze, Luc In NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium 2018 [DOI]
5. Edge-Based Compartmental Modelling of an SIR Epidemic on a Dual-Layer Static–Dynamic Multiplex Network with Tunable Clustering Barnard, Rosanna C., Kiss, Istvan Z., Berthouze, Luc, and Miller, Joel C. Bulletin of Mathematical Biology 2018 [Abs] [DOI]

   The duration, type and structure of connections between individuals in real-world populations play a crucial role in how diseases invade and spread. Here, we incorporate the aforementioned heterogeneities into a model by considering a dual-layer static–dynamic multiplex network. The static network layer affords tunable clustering and describes an individual’s permanent community structure. The dynamic network layer describes the transient connections an individual makes with members of the wider population by imposing constant edge rewiring. We follow the edge-based compartmental modelling approach to derive equations describing the evolution of a susceptible–infected–recovered epidemic spreading through this multiplex network of individuals. We derive the basic reproduction number, measuring the expected number of new infectious cases caused by a single infectious individual in an otherwise susceptible population. We validate model equations by showing convergence to pre-existing edge-based compartmental model equations in limiting cases and by comparison with stochastically simulated epidemics. We explore the effects of altering model parameters and multiplex network attributes on resultant epidemic dynamics. We validate the basic reproduction number by plotting its value against associated final epidemic sizes measured from simulation and predicted by model equations for a number of set-ups. Further, we explore the effect of varying individual model parameters on the basic reproduction number. We conclude with a discussion of the significance and interpretation of the model and its relation to existing research literature. We highlight intrinsic limitations and potential extensions of the present model and outline future research considerations, both experimental and theoretical.

2017

1. A genetic algorithm-based approach to mapping the diversity of networks sharing a given degree distribution and global clustering Overbury, Peter, Kiss, Istvan Z., and Berthouze, Luc 2017 [DOI]
2. Clustered Arrangement of Inhibitory Neurons Can Lead to Oscillatory Dynamics in a Model of Activity-Dependent Structural Plasticity Barnard, Rosanna C., Kiss, Istvan Z., Farmer, Simon F., and Berthouze, Luc 2017 [DOI]
3. Mapping Out Emerging Network Structures in Dynamic Network Models Coupled with Epidemics Kiss, István Z., Berthouze, Luc, Miller, Joel C., and Simon, Péter L. 2017 [DOI]

2016

1. Generation and analysis of networks with a prescribed degree sequence and subgraph family: higher-order structure matters Ritchie, Martin, Berthouze, Luc, and Kiss, Istvan Z. Journal of Complex Networks 2016 [DOI]
2. Beyond clustering: mean-field dynamics on networks with arbitrary subgraph composition Ritchie, Martin, Berthouze, Luc, and Kiss, Istvan Z. Journal of Mathematical Biology 2016 [Abs] [DOI] [Code]

   Clustering is the propensity of nodes that share a common neighbour to be connected. It is ubiquitous in many networks but poses many modelling challenges. Clustering typically manifests itself by a higher than expected frequency of triangles, and this has led to the principle of constructing networks from such building blocks. This approach has been generalised to networks being constructed from a set of more exotic subgraphs. As long as these are fully connected, it is then possible to derive mean-field models that approximate epidemic dynamics well. However, there are virtually no results for non-fully connected subgraphs. In this paper, we provide a general and automated approach to deriving a set of ordinary differential equations, or mean-field model, that describes, to a high degree of accuracy, the expected values of system-level quantities, such as the prevalence of infection. Our approach offers a previously unattainable degree of control over the arrangement of subgraphs and network characteristics such as classical node degree, variance and clustering. The combination of these features makes it possible to generate families of networks with different subgraph compositions while keeping classical network metrics constant. Using our approach, we show that higher-order structure realised either through the introduction of loops of different sizes or by generating networks based on different subgraphs but with identical degree distribution and clustering, leads to non-negligible differences in epidemic dynamics.

3. Oscillating epidemics in a dynamic network model: stochastic and mean-field analysis Szabó-Solticzky, András, Berthouze, Luc, Kiss, Istvan Z., and Simon, Péter L. Journal of Mathematical Biology 2016 [Abs] [DOI]

   An adaptive network model using SIS epidemic propagation with link-type-dependent link activation and deletion is considered. Bifurcation analysis of the pairwise ODE approximation and the network-based stochastic simulation is carried out, showing that three typical behaviours may occur; namely, oscillations can be observed besides disease-free or endemic steady states. The oscillatory behaviour in the stochastic simulations is studied using Fourier analysis, as well as through analysing the exact master equations of the stochastic model. By going beyond simply comparing simulation results to mean-field models, our approach yields deeper insights into the observed phenomena and help better understand and map out the limitations of mean-field models.

4. The Parkinsonian Subthalamic Network: Measures of Power, Linear, and Non-linear Synchronization and their Relationship to L-DOPA Treatment and OFF State Motor Severity West, Timothy, Farmer, Simon, Berthouze, Luc, Jha, Ashwani, Beudel, Martijn, Foltynie, Thomas, Limousin, Patricia, Zrinzo, Ludvic, Brown, Peter, and Litvak, Vladimir Frontiers in Human Neuroscience 2016 [Abs] [DOI]

   In this paper we investigated the dopaminergic modulation of neuronal interactions occurring in the subthalamic nucleus (STN) during Parkinson’s disease (PD). We utilized linear measures of local and long range synchrony such as power and coherence, as well as Detrended Fluctuation Analysis for Phase Synchrony (DFA-PS)- a recently developed non-linear method that computes the extent of long tailed autocorrelations present in the phase interactions between two coupled signals. Through analysis of local field potentials (LFPs) taken from the STN we seek to determine changes in the neurodynamics that may underpin the pathophysiology of PD in a group of 12 patients who had undergone surgery for deep brain stimulation. We demonstrate up modulation of alpha-theta (5-12 Hz) band power in response to L-DOPA treatment, whilst low beta band power (15-20 Hz) band-power is suppressed. We also find evidence for significant local connectivity within the region surrounding STN although there was evidence for its modulation via administration of L-DOPA. Further to this we present evidence for a positive correlation between the phase ordering of bilateral STN interactions and the severity of bradykinetic and rigidity symptoms in PD. Although, the ability of non-linear measures to predict clinical state did not exceed standard measures such as beta power, these measures may help identify the connections which play a role in pathological dynamics.

2015

1. Using Novelty-Biased GA to Sample Diversity in Graphs Satisfying Constraints Overbury, Peter, and Berthouze, Luc In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion ’15 2015 [DOI]
2. Resting state MEG oscillations show long-range temporal correlations of phase synchrony that break down during finger movement Botcharova, Maria, Berthouze, Luc, Brookes, Matthew J., Barnes, Gareth R., and Farmer, Simon F. Frontiers in Physiology 2015 [Abs] [DOI]

   The capacity of the human brain to interpret and respond to multiple temporal scales in its surroundings suggests that its internal interactions must also be able to operate over a broad temporal range. In this paper, we utilize a recently introduced method for characterizing the rate of change of the phase difference between MEG signals and use it to study the temporal structure of the phase interactions between MEG recordings from the left and right motor cortices during rest and during a finger-tapping task. We use the Hilbert transform to estimate moment-to-moment fluctuations of the phase difference between signals. After confirming the presence of scale-invariance we estimate the Hurst exponent using detrended fluctuation analysis (DFA). An exponent of \textgreater0.5 is indicative of long-range temporal correlations (LRTCs) in the signal. We find that LRTCs are present in the α/μ and β frequency bands of resting state MEG data. We demonstrate that finger movement disrupts LRTCs correlations, producing a phase relationship with a structure similar to that of Gaussian white noise. The results are validated by applying the same analysis to data with Gaussian white noise phase difference, recordings from an empty scanner and phase-shuffled time series. We interpret the findings through comparison of the results with those we obtained from an earlier study during which we adopted this method to characterize phase relationships within a Kuramoto model of oscillators in its sub-critical, critical, and super-critical synchronization states. We find that the resting state MEG from left and right motor cortices shows moment-to-moment fluctuations of phase difference with a similar temporal structure to that of a system of Kuramoto oscillators just prior to its critical level of coupling, and that finger tapping moves the system away from this pre-critical state toward a more random state.

2014

1. Higher-order structure and epidemic dynamics in clustered networks Ritchie, Martin, Berthouze, Luc, House, Thomas, and Kiss, Istvan Z. Journal of Theoretical Biology 2014 [Abs] [DOI]

   Clustering is typically measured by the ratio of triangles to all triples regardless of whether open or closed. Generating clustered networks, and how clustering affects dynamics on networks, is reasonably well understood for certain classes of networks (Volz et al., 2011; Karrer and Newman, 2010), e.g. networks composed of lines and non-overlapping triangles. In this paper we show that it is possible to generate networks which, despite having the same degree distribution and equal clustering, exhibit different higher-order structure, specifically, overlapping triangles and other order-four (a closed network motif composed of four nodes) structures. To distinguish and quantify these additional structural features, we develop a new network metric capable of measuring order-four structure which, when used alongside traditional network metrics, allows us to more accurately describe a network׳s topology. Three network generation algorithms are considered: a modified configuration model and two rewiring algorithms. By generating homogeneous networks with equal clustering we study and quantify their structural differences, and using SIS (Susceptible-Infected-Susceptible) and SIR (Susceptible-Infected-Recovered) dynamics we investigate computationally how differences in higher-order structure impact on epidemic threshold, final epidemic or prevalence levels and time evolution of epidemics. Our results suggest that characterising and measuring higher-order network structure is needed to advance our understanding of the impact of network topology on dynamics unfolding on the networks.

2. Identification of Criticality in Neuronal Avalanches: II. A Theoretical and Empirical Investigation of the Driven Case Hartley, Caroline, Taylor, Timothy J., Kiss, Istvan Z., Farmer, Simon F., and Berthouze, Luc Journal of Mathematical Neuroscience 2014 [Abs] [DOI]

   The observation of apparent power laws in neuronal systems has led to the suggestion that the brain is at, or close to, a critical state and may be a self-organised critical system. Within the framework of self-organised criticality a separation of timescales is thought to be crucial for the observation of power-law dynamics and computational models are often constructed with this property. However, this is not necessarily a characteristic of physiological neural networks-external input does not only occur when the network is at rest/a steady state. In this paper we study a simple neuronal network model driven by a continuous external input (i.e. the model does not have an explicit separation of timescales from seeding the system only when in the quiescent state) and analytically tuned to operate in the region of a critical state (it reaches the critical regime exactly in the absence of input-the case studied in the companion paper to this article). The system displays avalanche dynamics in the form of cascades of neuronal firing separated by periods of silence. We observe partial scale-free behaviour in the distribution of avalanche size for low levels of external input. We analytically derive the distributions of waiting times and investigate their temporal behaviour in relation to different levels of external input, showing that the system’s dynamics can exhibit partial long-range temporal correlations. We further show that as the system approaches the critical state by two alternative ’routes’, different markers of criticality (partial scale-free behaviour and long-range temporal correlations) are displayed. This suggests that signatures of criticality exhibited by a particular system in close proximity to a critical state are dependent on the region in parameter space at which the system (currently) resides.

3. Markers of criticality in phase synchronization Botcharova, Maria, Farmer, Simon F., and Berthouze, Luc Frontiers in Systems Neuroscience 2014 [Abs] [DOI]

   The concept of the brain as a critical dynamical system is very attractive because systems close to criticality are thought to maximize their dynamic range of information processing and communication. To date, there have been two key experimental observations in support of this hypothesis: (i) neuronal avalanches with power law distribution of size and (ii) long-range temporal correlations (LRTCs) in the amplitude of neural oscillations. The case for how these maximize dynamic range of information processing and communication is still being made and because a significant substrate for information coding and transmission is neural synchrony it is of interest to link synchronization measures with those of criticality. We propose a framework for characterizing criticality in synchronization based on an analysis of the moment-to-moment fluctuations of phase synchrony in terms of the presence of LRTCs. This framework relies on an estimation of the rate of change of phase difference and a set of methods we have developed to detect LRTCs. We test this framework against two classical models of criticality (Ising and Kuramoto) and recently described variants of these models aimed to more closely represent human brain dynamics. From these simulations we determine the parameters at which these systems show evidence of LRTCs in phase synchronization. We demonstrate proof of principle by analysing pairs of human simultaneous EEG and EMG time series, suggesting that LRTCs of corticomuscular phase synchronization can be detected in the resting state and experimentally manipulated. The existence of LRTCs in fluctuations of phase synchronization suggests that these fluctuations are governed by non-local behavior, with all scales contributing to system behavior. This has important implications regarding the conditions under which one should expect to see LRTCs in phase synchronization. Specifically, brain resting states may exhibit LRTCs reflecting a state of readiness facilitating rapid task-dependent shifts toward and away from synchronous states that abolish LRTCs.

4. Impact of constrained rewiring on network structure and node dynamics Rattana, P., Berthouze, L., and Kiss, I. Z. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics 2014 [Abs] [DOI]

   In this paper, we study an adaptive spatial network. We consider a susceptible-infected-susceptible (SIS) epidemic on the network, with a link or contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic. We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labeling and no epidemic dynamics, we provide both analytic and semianalytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network’s initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence.

2013

1. A maximum likelihood based technique for validating detrended fluctuation analysis (ML-DFA) Botcharova, Maria, Farmer, Simon F., and Berthouze, Luc arXiv:1306.5075 [physics, q-bio] 2013 [Abs] [arXiv] [Code]

   Detrended Fluctuation Analysis (DFA) is widely used to assess the presence of long-range temporal correlations in time series. Signals with long-range temporal correlations are typically defined as having a power law decay in their autocorrelation function. The output of DFA is an exponent, which is the slope obtained by linear regression of a log-log fluctuation plot against window size. However, if this fluctuation plot is not linear, then the underlying signal is not self-similar, and the exponent has no meaning. There is currently no method for assessing the linearity of a DFA fluctuation plot. Here we present such a technique, called ML-DFA. We scale the DFA fluctuation plot to construct a likelihood function for a set of alternative models including polynomial, root, exponential, logarithmic and spline functions. We use this likelihood function to determine the maximum likelihood and thus to calculate values of the Akaike and Bayesian information criteria, which identify the best fit model when the number of parameters involved is taken into account and over-fitting is penalised. This ensures that, of the models that fit well, the least complicated is selected as the best fit. We apply ML-DFA to synthetic data from FARIMA processes and sine curves with DFA fluctuation plots whose form has been analytically determined, and to experimentally collected neurophysiological data. ML-DFA assesses whether the hypothesis of a linear fluctuation plot should be rejected, and thus whether the exponent can be considered meaningful. We argue that ML-DFA is essential to obtaining trustworthy results from DFA.

2. Identification of Criticality in Neuronal Avalanches: I. A Theoretical Investigation of the Non-driven Case Taylor, Timothy J., Hartley, Caroline, Simon, Péter L., Kiss, Istvan Z., and Berthouze, Luc Journal of Mathematical Neuroscience 2013 [Abs] [DOI]

   In this paper, we study a simple model of a purely excitatory neural network that, by construction, operates at a critical point. This model allows us to consider various markers of criticality and illustrate how they should perform in a finite-size system. By calculating the exact distribution of avalanche sizes, we are able to show that, over a limited range of avalanche sizes which we precisely identify, the distribution has scale free properties but is not a power law. This suggests that it would be inappropriate to dismiss a system as not being critical purely based on an inability to rigorously fit a power law distribution as has been recently advocated. In assessing whether a system, especially a finite-size one, is critical it is thus important to consider other possible markers. We illustrate one of these by showing the divergence of susceptibility as the critical point of the system is approached. Finally, we provide evidence that power laws may underlie other observables of the system that may be more amenable to robust experimental assessment.

3. Rectification of EMG in low force contractions improves detection of motor unit coherence in the beta-frequency band Ward, Nicholas J., Farmer, Simon F., Berthouze, Luc, and Halliday, David M. Journal of Neurophysiology 2013 [Abs] [DOI]

   Rectification of surface EMG before spectral analysis is a well-established preprocessing method used in the detection of motor unit firing patterns. A number of recent studies have called into question the need for rectification before spectral analysis, pointing out that there is no supporting experimental evidence to justify rectification. We present an analysis of 190 records from 13 subjects consisting of simultaneous recordings of paired single motor units and surface EMG from the extensor digitorum longus muscle during middle finger extension against gravity (unloaded condition) and against gravity plus inertial loading (loaded condition). We directly examine the hypothesis that rectified surface EMG is a better predictor of the frequency components of motor unit synchronization than the unrectified (or raw) EMG in the beta-frequency band (15-32 Hz). We use multivariate analysis and estimate the partial coherence between the paired single units using both rectified and unrectified surface EMG as a predictor. We use a residual partial correlation measure to quantify the difference between raw and rectified EMG as predictor and analyze unloaded and loaded conditions separately. The residual correlation for the unloaded condition is 22% with raw EMG and 3.5% with rectified EMG and for the loaded condition it is 5.2% with raw EMG and 1.4% with rectified EMG. We interpret these results as strong supporting experimental evidence in favor of using the preprocessing step of surface EMG rectification before spectral analysis.

2012

1. Modelling approaches for simple dynamic networks and applications to disease transmission models Kiss, I. Z., Berthouze, L., Taylor, T. J., and Simon, P. L. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 2012 [DOI]
2. Long-range temporal correlations in the EEG bursts of human preterm babies Hartley, Caroline, Berthouze, Luc, Mathieson, Sean R., Boylan, Geraldine B., Rennie, Janet M., Marlow, Neil, and Farmer, Simon F. PloS One 2012 [Abs] [DOI]

   The electrical activity in the very early human preterm brain, as recorded by scalp EEG, is mostly discontinuous and has bursts of high-frequency oscillatory activity nested within slow-wave depolarisations of high amplitude. The temporal organisation of the occurrence of these EEG bursts has not been previously investigated. We analysed the distribution of the EEG bursts in 11 very preterm (23-30 weeks gestational age) human babies through two estimates of the Hurst exponent. We found long-range temporal correlations (LRTCs) in the occurrence of these EEG bursts demonstrating that even in the very immature human brain, when the cerebral cortical structure is far from fully developed, there is non-trivial temporal structuring of electrical activity.

3. Adaptive time-varying detrended fluctuation analysis Berthouze, Luc, and Farmer, Simon F. Journal of Neuroscience Methods 2012 [Abs] [DOI]

   Detrended fluctuation analysis (DFA) is a technique commonly used to assess and quantify the presence of long-range temporal correlations (LRTCs) in neurophysiological time series. Convergence of the method is asymptotic only and therefore its application assumes a constant scaling exponent. However, most neurophysiological data are likely to involve either spontaneous or experimentally induced scaling exponent changes. We present a novel extension of the DFA method that permits the characterisation of time-varying scaling exponents. The effectiveness of the methodology in recovering known changes in scaling exponents is demonstrated through its application to synthetic data. The dependence of the method on its free parameters is systematically explored. Finally, application of the methodology to neurophysiological data demonstrates that it provides experimenters with a way to identify previously un-recognised changes in the scaling exponent in the data. We suggest that this methodology will make it possible to go beyond a simple demonstration of the presence of scaling to an appreciation of how it may vary in response to either intrinsic changes or experimental perturbations.

4. Power-law distribution of phase-locking intervals does not imply critical interaction Botcharova, M., Farmer, S. F., and Berthouze, L. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics 2012 [Abs] [DOI]

   Neural synchronization plays a critical role in information processing, storage, and transmission. Characterizing the pattern of synchronization is therefore of great interest. It has recently been suggested that the brain displays broadband criticality based on two measures of synchronization, phase-locking intervals and global lability of synchronization, showing power-law statistics at the critical threshold in a classical model of synchronization. In this paper, we provide evidence that, within the limits of the model selection approach used to ascertain the presence of power-law statistics, the pooling of pairwise phase-locking intervals from a noncritically interacting system can produce a distribution that is similarly assessed as being power law. In contrast, the global lability of synchronization measure is shown to better discriminate critical from noncritical interaction.

2011

1. Detecting the presence of long-range temporal correlations in a time-varying measure of phase synchrony Botcharova, Maria, Farmer, Simon, and Berthouze, Luc BMC Neuroscience 2011 [DOI]
2. Adaptive time-varying detrended fluctuations analysis: a new method for characterizing time-varying scaling parameters in physiological time series Berthouze, Luc, and Farmer, Simon F BMC Neuroscience 2011 [DOI] [Code]
3. Design and validation of surface-marker clusters for the quantification of joint rotations in general movements in early infancy Berthouze, Luc, and Mayston, Margaret Journal of Biomechanics 2011 [Abs] [DOI]

   Lack of complexity in general movements in early infancy is an important marker of potential motor disorders of neurological origin, such as cerebral palsy. Quantitative approaches to characterising this complexity are hampered by experimental difficulties in recording from infants in their first few months of life. The aim of this study was to design and validate bespoke surface-marker clusters to facilitate data acquisition and enable full quantification of joint rotations. The clusters were validated by recording the controlled movements of a soft-body dummy doll simultaneously with an optical (Qualisys) and inertial (XSens) motion capture system. The angles estimated from the optical system were compared with those measured by the inertial system. We demonstrate that the surface-marker based approach compares well with the use of an inertial system to obtain "direct" readings of the rotations whilst alleviating the issues associated with the use of an optical motion capture system. We briefly report use of this technique in 1-5 month old infants. By enabling full quantification of joint rotation, use of the custom made markers could pave the way for early diagnosis of movement disorders.

2010

1. Transfer or no transfer: the key role of learning specificity Berthouze, Luc In Proceedings of the 10th International Conference on Epigenetic Robotics 2010
2. Role of three key developmental variables in the emergence of long-range temporal correlations in a network of spiking neurons without plasticity Ward, Nicholas, Farmer, Simon F, and Berthouze, Luc BMC Neuroscience 2010 [DOI]
3. Role of STDP and heterogeneity in the emergence of long-range temporal correlations and frequency scaling in networks of LIF neurons Corcoran, Thomas ‘Greg’, Farmer, Simon F, and Berthouze, Luc BMC Neuroscience 2010 [DOI]
4. Human EEG shows long-range temporal correlations of oscillation amplitude in Theta, Alpha and Beta bands across a wide age range Berthouze, Luc, James, Leon M., and Farmer, Simon F. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology 2010 [Abs] [DOI]

   OBJECTIVE: Long-range temporal correlations (LRTC) of EEG amplitude fluctuations in adults reveal power-law statistics and have been interpreted within the framework of self-organized criticality (SOC). In physical systems states of self-organized criticality showing power-law statistics take time to develop. In this paper we have sought evidence for the idea that brain development tends towards SOC through examining the hypothesis that during normal human development a power law behaviour of EEG oscillations is approached with increasing chronological age. METHODS: We examined EEGs from central and parietal electrodes in 36 subjects aged between 0 and 660months during performance of a steady wrist extension task with their dominant hand and applied spectral and detrended fluctuation analysis in 36 subjects to assess long-range temporal correlations of oscillation amplitude in the Theta, Alpha and Beta frequency bands. RESULTS: Our data indicate that at all subject ages power-law statistics dominate the records at Alpha, Beta and Theta frequencies. Small consistent effects of chronological age were detected for amplitude fluctuations at Theta and Beta frequencies. CONCLUSIONS: The data suggest that the scale-free nature of EEG LRTCs is a feature from early childhood through to maturity but that there are changes in the magnitude of these effects with age. SIGNIFICANCE: This study is the first to have explored long-range temporal correlations over a wide range of chronological age.

2008

1. Predicting the Timing of Critical Periods in Development Winks, Oliver E G, and Berthouze, Luc In Proceedings of the 8th International Conference on Epigenetic Robotics 2008 [Abs]

   Critical periods are specific periods in the development of a living organism during which there is an increased sensitivity to external perturbations. Such perturbations result in a developmental trajectory significantly different from what is considered the norm. This paper is concerned with the question of whether the presence and timing of a critical period can be predicted from the developmental profile without perturbation. Taking rate of change as a measure of this profile, we put forward the hypothesis that critical periods will occur when the rate of change is greatest. Using a simplified model of differential gene expression, cellular mechanics and evolution, we evolve organisms with different developmental profiles, test the presence of critical periods by systematically exposing the developing system to an external perturbation at different times between runs, and correlate the timing of these critical periods with the proposed developmental measure. We discuss the implications of our findings.

2. Bifurcation Angles in Ant Foraging Networks: A Trade-Off between Exploration and Exploitation? Berthouze, Luc, and Lorenzi, Alexander In From Animals to Animats 10 2008 [Abs] [DOI]

   The distribution of bifurcation angles found in ant foraging networks has been shown to give polarity to the networks so that nestbound ants reaching a bifurcation can choose the appropriate direction. In this paper, we use an individual-based model to test the hypothesis that this distribution is an emergent property of a population of foraging ants optimising the trade-off between exploitation of the existing network to maximise food intake and exploration of the environment to maximise the population’s ability to rapidly adapt to novel or changing environments. We identify a parameter regulating an ant’s drives to forage existing trails and explore uncovered areas of the environment as a collective variable controlling the distribution of bifurcation angles in the foraging network and we show that when the exploration-exploitation trade-off is realised, the resulting distribution exhibits the same informational characteristics as that found in the original study.

3. Assembly, tuning, and transfer of action systems in infants and robots Berthouze, Luc, and Goldfield, Eugene C. Infant and Child Development 2008 [DOI]

2007

1. Développement moteur : la robotique au secours de la psychobiologie du développement Berthouze, Luc, and Goldfield, Eugene C. Enfance 2007 [DOI]

2006

1. Passive compliance for a RC servo-controlled bouncing robot Meyer, Friedrich, Spröwitz, Alexander, and Berthouze, Luc Advanced Robotics 2006 [Abs] [DOI]

   A novel and low-cost passively compliant mechanism is described that can be used with RC servos to actuate legged robots in tasks involving high dynamic loads such as bouncing. Compliance is achieved by combining visco-elastic material and metal parts. Joint response to dynamic loads is evaluated using real-world experiments and force data are obtained from a Lagrangian analysis of the system. The experimental results demonstrate the applicative potential of this mechanism.

2. A Neural Model for Context-dependent Sequence Learning Berthouze, Luc, and Tijsseling, Adriaan Neural Processing Letters 2006 [DOI]
3. Stepwise Feature Selection by Cross Validation for EEG-based Brain Computer Interface Tanaka, K., Kurita, T., Meyer, F., Berthouze, L., and Kawabe, T. In The 2006 IEEE International Joint Conference on Neural Network Proceedings 2006 [DOI]

2005

1. Acquisition of a” mirror” system for speechreading Berthouze, Luc In Symposium on Imitation in Animals and Artifacts 2005
2. Epigenetic robotics: modelling cognitive development in robotic systems Berthouze, Luc, and Metta, Giorgio Cognitive Systems Research 2005 [DOI]

2004

1. Simple And Low-cost Compliant Leg-foot System Meyer, F., Sprowitz, A., Lungarella, M., and Berthouze, L. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) 2004 [Abs] [DOI]

   We describe a simple and low-cost humanoid leg design with compliant joints and springy feet. Mechanical compliance is achieved by combining visco-elastic material with metal. Joints and feet characteristics are evaluated by repeatedly dropping the system from a fixed height. Different joint configurations (silicone rubber, latex and brass) and foot compliance are examined, and additional data are obtained with a Lagrangian analysis of the system. We show that compliance not only reduces impact forces, but also induces smoother joint trajectories.

2. Robot Bouncing: On the Synergy Between Neural and Body-Environment Dynamics Lungarella, Max, and Berthouze, Luc 2004 [Abs] [DOI]

   The study of how infants strapped in a Jolly Jumper learn to bounce can help clarify how they explore different ways of exploiting the dynamics of their movements. In this paper, we describe and discuss a set of preliminary experiments performed with a bouncing humanoid robot and aimed at instantiating a few computational principles thought to underlie the development of motor skills. Our experiments show that a suitable choice of the coupling constants between hip, knee, and ankle joints, as well as of the strength of the sensory feedback, induces a reduction of movement variability, and leads to an increase in bouncing amplitude and movement stability. This result is attributed to the synergy between neural and body-environment dynamics.

3. Motor Skill Acquisition Under Environmental Perturbations: On the Necessity of Alternate Freezing and Freeing of Degrees of Freedom Berthouze, Luc, and Lungarella, Max Adaptive Behavior 2004 [DOI]

2003

1. Epigenetic robotics—modelling cognitive development in robotic systems Berthouze, Luc, and Ziemke, Tom Connection Science 2003 [DOI]

2002

1. Adaptivity via alternate freeing and freezing of degrees of freedom Lungarella, M., and Berthouze, L. In Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP ’02. 2002 [DOI]

2000

1. An episodic memory model using spiking neurons Berthouze, L. In SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. ’Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions’ (Cat. No.00CH37166) 2000 [DOI]

1998

1. Neural learning of embodied interaction dynamics Kuniyoshi, Yasuo, and Berthouze, Luc Neural Networks: The Official Journal of the International Neural Network Society 1998 [Abs]

   This paper presents our approach towards realizing a robot which can bootstrap itself towards higher complexity through embodied interaction dynamics with the environment including other agents. First, the elements of interaction dynamics are extracted from conceptual analysis of embodied interaction and its emergence, especially of behavioral imitation. Then three case studies are made, presenting our neural architecture and the robotic experiments on some of the important elements discussed above: self-exploration and entrainment, emergent coordination, and categorizing self-behavior. Finally, we propose that integrating all these elements will be an important step towards realizing the bootstrapping agent envisaged above.

1997

1. Deferred imitation of human head movements by an active stereo vision head Demiris, J., Rougeaux, S., Hayes, G.M., Berthouze, L., and Kuniyoshi, Y. In Proceedings 6th IEEE International Workshop on Robot and Human Communication. RO-MAN’97 SENDAI 1997 [Abs] [DOI]

   Designing a mechanism that will allow a robot to imitate the actions of a human, apart from being interesting f o r opening the possibilities for eficient social learning through observation and imitation, is challenging since at requires the integration of information from the visual, memory and motor systems. This paper deals with the implementation of a n imitation architecture on an active, stereo vision head, and describes our experiments on the deferred imitation of human head movements.

1996

1. Learning of oculo-motor control: a prelude to robotic imitation Berthouze, L., Bakker, P., and Kuniyoshi, Y. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS ’96 1996 [Abs] [DOI]

   In order t o allow robot agents t o adapt thew behavaour, a new approach - learnang b y amatataon - has been proposed, an whach a robot learns novel behavaours through anteractaons wath the envaronment and other agents I n thas paper we descrabe how our saghted agent, ESCHeR - who as epuapped wath dual foveated lenses a n d can control head, neck and e y e joants - develops fine oculo-motor control through anteractaon wath the enuaronment. ESCHeR successfully learns to coordanate pan, talt and vergence such that he can track braght movang objects and saccade rapzdly to new objects of anterest. I n developmental terms, ESCHeR can be consadered to have progressed from stage 1 t o siage 2 of amatataon learnang. A novel representataon of vasua1 scenes as then introduced, and at as dascussed how ESCHeR wall use thas to progress t o stage 3.

2. A camera neural model Berthouze, L., Chavand, F., and Barret, C. In 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929) 1996 [DOI]