Multi-bump solutions in dynamic neural fields: analysis and applications

Tese de doutoramento em Ciências (ramo de conhecimento em Matemática)The work described here has the goal of providing new mathematical results about the formation of spatio-temporal patterns in dynamic neural fields (DNFs) that can be applied and tested in the domains of cognitive modelling and cognitive robotics. Specifically, the conditions for the existence and stability of multiple localized excitations in one-dimensional fields with external input were analysed. These multi-bump solutions represent the core of an original dynamic field model of fast sequence learning that was developed and subsequently tested in a real-world robotics experiment. While the existence and the stability of different types of patterns in DNFs have been addressed in many theoretical studies in the past, little attention has been paid thus far on the initial and input conditions that guarantee the evolution of these patterns. Following Laing et al. (2002), we apply a connectivity function with oscillatory rather than monotonic decay to study analytically and numerically the formation of multiple regions of excitation when several localized inputs are applied simultaneously or sequentially to the field. For the existence and stability proofs, we extend the ideas of Amari’s original work on pattern formation in fields with connectivity functions of lateral inhibition type. Based on the mathematical results, a novel model of multi-item memory of sequential events is proposed that exploits the processing mechanism of self-sustained activity in recurrently connected neural populations modelled by DNFs. A threshold accommodation dynamics is applied to establish a stable multi-bump solution with a gradient of excitation that represents in its relative activation strengths the temporal order and the relative timing of sequence elements. In line with findings in neurophysiological studies with monkeys, this memory representation pre-activates to varying degrees corresponding neural populations in a decision field. The competitive dynamics of this field allows recalling all sequence elements in the correct order and with the correct timing. The working memory model was extended to integrate also the sequence learning part in the modelling. Neural populations in a perceptual field represent in their selfsustained activation patterns the sensory cue (e.g., colour) that defines the sequence. The challenge for many modelling approaches to represent repeated elements is autonomously solved by the field dynamics since repeated sensory cues automatically activate different neuronal subpopulations. The memory of previous sequence demonstrations also preshapes the perceptual field. This preshaping mechanisms affects the time course of suprathreshold population activity and is thus fundamental to adjust the relative activation strengths of the memory gradient in successive sequence demonstrations. The numerical simulation show that the purely activation based learning principles implemented in the model are able to acquire and represent the order and timing of a sequence in just very few demonstration-executing cycle. To directly test the assumptions about the time course of population activity in the various interconnected field layers and to verify the model predictions, we conducted a robotics experiment. The learning model was integrated in the dynamic field based control architecture of the humanoid robot ARoS. In the experiment, ARoS had to learn a short musical sequence from human demonstrations to subsequently execute the piece of music on a keyboard. The successful results of the real-time robotics implementation are discussed in relation to theoretical ideas and experimental findings about sequencing and timing in humans and other animals.O objetivo deste trabalho é fornecer novos resultados matemáticos sobre a formação de padrões espaço-temporais em campos dinâmicos neuronais (DNFs) que podem ser aplicados e testados nos domínios da modelação cognitiva e robótica cognitiva. Em particular, foram analisadas as condições para a existência e a estabilidade de múltiplas regiões localmente excitadas num campo unidimensional com uma entrada externa. Estas múltiplas regiões representam o base de um modelo de campos dinâmicos que foi desenvolvido para aprendizagem de sequências e posteriormente analisado numa experiência robótica em ambiente real. Embora haja vários estudos sobre a existência e a estabilidade dos diferentes tipos de padrôes em DNFs, pouca atenção tem sido dada sobre as condições iniciais e de entrada que garantem a evolução desses padrões. Tendo por base Laing et al. (2002), aplicou-se uma função de conectividade com decaimento oscilatório em vez de um decaimento monótono permitindo o estudo analítico e numérico da formação de múltiplas regiões de excitação quando várias entradas localizadas são aplicadas num campo dinâmico simultaneamente ou sequencialmente. Nas demonstrações da existência e estabilidade, foram estendidas as ideias do trabalho original de Amari em formação de padrões em campos dinâmicos com funções de conectividade do tipo inibição lateral. Tendo por base os resultados matemáticos, foi elaborado um novo modelo de memória de sequências de eventos que explora o mecanismo de processamento da atividade auto-sustentada em populações neuronais com ligações recorrentes modeladas por DNFs. Uma solução multi-picos estável com um gradiente de excitação é obtida a partir de uma dinâmica de adaptação do limiar de ativação do campo. Este gradiente representa, na sua força de ativação, a ordem temporal e o tempo relativo entre os elementos de sequência. De acordo com resultados obtidos em estudos neurofisiológicos com macacos, esta representação de memória pré-ativa, em níveis diferentes, as populações neuronais correspondentes no campo de decisão. A dinâmica competitiva deste campo permite recordar todos os elementos da sequência na ordem correta e com os intervalos de tempo corretos. O modelo de memória de trabalho foi estendido para integrar também a parte de aprendizagem da sequência na modelagem. Os padrões de ativação auto-sustentadas das populações neuronais no campo percetual representam o estímulo sensorial (por exemplo, cor) que define a sequência. O desafio para muitas abordagens de modelização de representar elementos repetidos é autonomamente resolvido pela dinâmica do campo que, de forma automática, representa elementos repetidos em diferentes subpopulações neuronais. A memória da sequência posteriormente demonstrada também pré-ativa o campo percetual. Este mecanismo de pré ativação afeta o tempo que a população demora a ficar ativa e é, portanto, essencial para ajustar as forças de ativação relativas do gradiente de memória em demonstrações sucessivas da sequência. As simulações numéricas mostram que os princípios de aprendizagem baseados puramente em ativação que foram implementados no modelo são capazes de adquirir e representar a ordem e o tempo de uma sequência ao fim de poucas demonstrações. Para testar diretamente os pressupostos sobre as evoluções temporais de atividade da população nos diferentes campos interligados e verificar as previsões do modelo, foi realizada uma experiência robótica. O modelo de aprendizagem foi integrado numa arquitetura dinâmica no robô humanoide ARoS. Na experiência o ARoS teve de aprender uma curta sequência musical, a partir de demonstrações executadas por um humano e, posteriormente, reproduzi-la num teclado. A experiência de implementação robótica em tempo real foi bem sucedida. Os respetivos resultados são discutidos em comparação com as ideias teóricas e resultados experimentais obtidos em experiências sobre sequências e tempo em seres humanos e outros animais.FCT Grant SFRH/BD/42375/2007, financed by POPH-QREN-Type 4.1-Advanced Training, co-funded by the European Social Fund and national funds from MEC

Toward a formal theory for computing machines made out of whatever physics offers: extended version

Approaching limitations of digital computing technologies have spurred research in neuromorphic and other unconventional approaches to computing. Here we argue that if we want to systematically engineer computing systems that are based on unconventional physical effects, we need guidance from a formal theory that is different from the symbolic-algorithmic theory of today's computer science textbooks. We propose a general strategy for developing such a theory, and within that general view, a specific approach that we call "fluent computing". In contrast to Turing, who modeled computing processes from a top-down perspective as symbolic reasoning, we adopt the scientific paradigm of physics and model physical computing systems bottom-up by formalizing what can ultimately be measured in any physical substrate. This leads to an understanding of computing as the structuring of processes, while classical models of computing systems describe the processing of structures.Comment: 76 pages. This is an extended version of a perspective article with the same title that will appear in Nature Communications soon after this manuscript goes public on arxi

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems: Proceedings

Proceedings of the 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems, which took place in Dresden, Germany, 26 – 28 May 2010.:Welcome Address ........................ Page I Table of Contents ........................ Page III Symposium Committees .............. Page IV Special Thanks ............................. Page V Conference program (incl. page numbers of papers) ................... Page VI Conference papers Invited talks ................................ Page 1 Regular Papers ........................... Page 14 Wednesday, May 26th, 2010 ......... Page 15 Thursday, May 27th, 2010 .......... Page 110 Friday, May 28th, 2010 ............... Page 210 Author index ............................... Page XII

The Processing of Emotional Sentences by Young and Older Adults: A Visual World Eye-movement Study

Carminati MN, Knoeferle P. The Processing of Emotional Sentences by Young and Older Adults: A Visual World Eye-movement Study. Presented at the Architectures and Mechanisms of Language and Processing (AMLaP), Riva del Garda, Italy