Representation of dynamic situations in the Robot’s Mind for Human-like interaction with the environment: autonomous navigation

The Compact Internal Representation (CIR) the- ory has been recently proposed as a cognitive basis for robots and artificial agents which interact with dynamic complex environments. This paper discusses the conceptual basis of the CIR and the explanations provided by the theory for the way in which humans interact with the world. The conclusions have been tested in robot navigation experiments, proving the feasibility of autonomous robots capable to navigate by interacting with time-changing environments in a human-like manner.Depto. de Análisis Matemático y Matemática AplicadaDepto. de Biodiversidad, Ecología y EvoluciónFac. de Ciencias MatemáticasTRUEpu

Dark solitons and their head-on collisions in Bose-Einstein condensates

The evolution and collision of dark solitary waves (solitons) appearing in cigar-shaped Bose-Einstein condensates with repulsive atom-atom interaction are here considered using a Boussinesq-Korteweg-de Vries description. We provide theoretical predictions and computer experiment evidence about their phase shifts or change of trajectories, in the space-time plot, corresponding upon collisions. Details are also given about a suggested experiment that could assess their genuine solitonic natureNatural Science Foundation of ChinaTrans-Century Training Program for the Talents of the Ministry of Education of ChinaSpanish Ministry of Science and TechnologyDepto. de Análisis Matemático y Matemática AplicadaFac. de Ciencias MatemáticasTRUEpu

Basic principles drive self-organization of brain-like connectivity structure

The brain can be considered as a system that dynamically optimizes the structure of anatomical connections based on the efficiency requirements of functional connectivity. To illustrate the power of this principle in organizing the complexity of brain architecture, we portray the functional connectivity as diffusion on the current network structure. The diffusion drives adaptive rewiring, resulting in changes to the network to enhance its efficiency. This dynamic evolution of the network structure generates, and thus explains, modular small-worlds with rich club effects, features commonly observed in neural anatomy. Taking wiring length and propagating waves into account leads to the morphogenesis of more specific neural structures that are stalwarts of the detailed brain functional anatomy, such as parallelism, divergence, convergence, super-rings, and super-chains. By showing how such structures emerge, largely independently of their specific biological realization, we offer a new conjecture on how natural and artificial brain-like structures can be physically implemented.Depto. de Análisis Matemático y Matemática AplicadaInstituto de Matemática Interdisciplinar (IMI)TRUEpu

Prediction-for-CompAction: navigation in social environments using generalized cognitive maps

The ultimate navigation efficiency of mobile robots in human environments will depend on how we will appraise them: merely as impersonal machines or as human-like agents. In the latter case, an agent may take advantage of the cooperative collision avoidance, given that it possesses recursive cognition, i.e., the agent’s decisions depend on the decisions made by humans that in turn depend on the agent’s decisions. To deal with this high-level cognitive skill, we propose a neural network architecture implementing Prediction-for-CompAction paradigm. The network predicts possible human–agent collisions and compacts the time dimension by projecting a given dynamic situation into a static map. Thereby emerging compact cognitive map can be readily used as a “dynamic GPS” for planning actions or mental evaluation of the convenience of cooperation in a given context. We provide numerical evidence that cooperation yields additional room for more efficient navigation in cluttered pedestrian flows, and the agent can choose path to the target significantly shorter than a robot treated by humans as a functional machine. Moreover, the navigation safety, i.e., the chances to avoid accidental collisions, increases under cooperation. Remarkably, these benefits yield no additional load to the mean society effort. Thus, the proposed strategy is socially compliant, and the humanoid agent can behave as “one of us.”Depto. de Análisis Matemático y Matemática AplicadaFac. de Ciencias MatemáticasInstituto de Matemática Interdisciplinar (IMI)TRUEpu

Fast social-like learning of complex behaviors based on motor motifs

Social learning is widely observed in many species. Less experienced agents copy successful behaviors exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown. Here we assume that a complex behavior can be decomposed into a sequence of n motor motifs. Then a neural network capable of activating motor motifs in a given sequence can drive an agent. To account for (n − 1)! possible sequences of motifs in a neural network, we employ the winnerless competition approach. We then consider a teacher-learner situation: one agent exhibits a complex movement, while another one aims at mimicking the teacher’s behavior. Despite the huge variety of possible motif sequences we show that the learner, equipped with the provided learning model, can rewire “on the fly” its synaptic couplings in no more than (n − 1) learning cycles and converge exponentially to the durations of the teacher’s motifs. We validate the learning model on mobile robots. Experimental results show that the learner is indeed capable of copying the teacher’s behavior composed of six motor motifs in a few learning cycles. The reported mechanism of learning is general and can be used for replicating different functions, including, for example, sound patterns or speech.FIS2014-57090-PDepto. de Análisis Matemático y Matemática AplicadaInstituto de Matemática Interdisciplinar (IMI)TRUEpu

High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality

High-dimensional data and high-dimensional representations of reality are inherent features of modern Artificial Intelligence systems and applications of machine learning. The well-known phenomenon of the “curse of dimensionality” states: many problems become exponentially difficult in high dimensions. Recently, the other side of the coin, the “blessing of dimensionality”, has attracted much attention. It turns out that generic high-dimensional datasets exhibit fairly simple geometric properties. Thus, there is a fundamental tradeoff between complexity and simplicity in high dimensional spaces. Here we present a brief explanatory review of recent ideas, results and hypotheses about the blessing of dimensionality and related simplifying effects relevant to machine learning and neuroscience.Ministerio de Economía y Competitividad (MINECO)Ministry of Science and Higher Education of the Russian FederationInnovate UKUniversity of LeicesterDepto. de Análisis Matemático y Matemática AplicadaFac. de Ciencias MatemáticasTRUEpu

Reaction-Diffusion-Like Systems for Event Representation and Beyond

Proceedings of the 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems, which took place in Dresden, Germany, 26 – 28 May 2010.Understanding of how the brain makes an effective compact internal representation (CIR) of time evolving situations in a given landscape is a challenging problem. Before proceeding to such a task we here discuss a model where immobile obstacles are placed in an arena. We show how an internal representation differs from the apparently similar diffusion case.European CommissionDepto. de Análisis Matemático y Matemática AplicadaInstituto Pluridisciplinar (IP)TRUEpu

Compact Internal Representation of Dynamic Environments: Simple Memory Structures for Complex Situations

In this chapter the novel concept of Compact Internal Representation (CIR) is introduced as a generalization of the internal representation extensively used in literature as a base for cognition and consciousness. CIR is suitable to represent dynamic environments and their potential interactions with the agent as static (time-independent) structures, suitable to be stored, managed, compared and recovered by memory. In this work the application of CIR as the cognitive core of moving autonomous artificial agents is presented in the context of collision avoidance against dynamical obstacles. The structure that emerges, even if not directly related to the insect neurobiology, is quite simple and could enhance the capabilities of the computational model already presented in the previous chapter, in view of its robotic implementation.Depto. de Análisis Matemático y Matemática AplicadaInstituto Pluridisciplinar (IP)TRUEpu

Spatial memory based on an STDP driven neural network

We propose a model of spatial memory implemented in a Spiking Neural Network (SNN) and test it on a robot moving in an environment with neutral and harmful regions. Neurons in the SNN play the role of place cells, and their population dynamics determines the robot movements. We show that STDP rearranges the couplings in the SNN and forms spatial memory similar to cognitive maps associated with the negative experience. Then, the robot learns to avoid harmful zones.Depto. de Análisis Matemático y Matemática AplicadaFac. de Ciencias MatemáticasFALSEpu

El GPS dinámico del cerebro nos acerca al diseño de robots inteligentes

Enseñar a un robot a jugar al ajedrez es incomparablemente más fácil que conseguir que sea capaz de jugar al fútbol o moverse entre la muchedumbre de una céntrica calle de Madrid. Diseñar un robot inteligente, capaz de imitar nuestras habilidades sensoriales y motoras, pasa por comprender cómo entiende el cerebro nuestra realidad, tan cambiante y compleja.Depto. de Biodiversidad, Ecología y EvoluciónFac. de Óptica y OptometríaFALSEpu