Person:
Calvo Tapia, Carlos

First Name

Carlos

Last Name

Calvo Tapia

URI

https://hdl.handle.net/20.500.14352/74305

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Ciencias Biológicas

Department

Biodiversidad, Ecología y Evolución

Area

Matemática Aplicada

Identifiers

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Now showing 1 - 10 of 10

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons
(Bulletin of Mathematical Biology, 2018) Tyukin, Ivan; Gorban, Alexander N.; Calvo Tapia, Carlos; Makarova, Julia; Makarov Slizneva, Valeriy
Codifying memories is one of the fundamental problems of modern Neuroscience. The functional mechanisms behind this phenomenon remain largely unknown. Experimental evidence suggests that some of the memory functions are performed by stratified brain structures such as the hippocampus. In this particular case, single neurons in the CA1 region receive a highly multidimensional input from the CA3 area, which is a hub for information processing. We thus assess the implication of the abundance of neuronal signalling routes converging onto single cells on the information processing. We show that single neurons can selectively detect and learn arbitrary information items, given that they operate in high dimensions. The argument is based on stochastic separation theorems and the concentration of measure phenomena. We demonstrate that a simple enough functional neuronal model is capable of explaining: (i) the extreme selectivity of single neurons to the information content, (ii) simultaneous separation of several uncorrelated stimuli or informational items from a large set, and (iii) dynamic learning of new items by associating them with already “known” ones. These results constitute a basis for organization of complex memories in ensembles of single neurons. Moreover, they show that no a priori assumptions on the structural organization of neuronal ensembles are necessary for explaining basic concepts of static and dynamic memories.
Static internal representation of dynamic situations reveals time compaction in human cognition
(Journal of Advanced Research, 2020) Villacorta-Atienza, José Antonio; Calvo Tapia, Carlos; Díez-Hermano, Sergio; Sánchez Jiménez, Abel; Lobov, Sergei; Krilova, Nadia; Murciano Cespedosa, Antonio; López-Tolsa, Gabriela E.; Pellón, Ricardo; Makarov Slizneva, Valeriy
Introduction: The human brain has evolved under the constraint of survival in complex dynamic situations. It makes fast and reliable decisions based on internal representations of the environment. Whereas neural mechanisms involved in the internal representation of space are becoming known, entire spatiotemporal cognition remains a challenge. Growing experimental evidence suggests that brain mechanisms devoted to spatial cognition may also participate in spatiotemporal information processing. Objectives: The time compaction hypothesis postulates that the brain represents both static and dynamic situations as purely static maps. Such an internal reduction of the external complexity allows humans to process time-changing situations in real-time efficiently. According to time compaction, there may be a deep inner similarity between the representation of conventional static and dynamic visual stimuli. Here, we test the hypothesis and report the first experimental evidence of time compaction in humans. Methods: We engaged human subjects in a discrimination-learning task consisting in the classification of static and dynamic visual stimuli. When there was a hidden correspondence between static and dynamic stimuli due to time compaction, the learning performance was expected to be modulated. We studied such a modulation experimentally and by a computational model. Results: The collected data validated the predicted learning modulation and confirmed that time compaction is a salient cognitive strategy adopted by the human brain to process time-changing situations. Mathematical modelling supported the finding. We also revealed that men are more prone to exploit time compaction in accordance with the context of the hypothesis as a cognitive basis for survival. Conclusions: The static internal representation of dynamic situations is a human cognitive mechanism involved in decision-making and strategy planning to cope with time-changing environments. The finding opens a new venue to understand how humans efficiently interact with our dynamic world and thrive in nature.
Basic principles drive self-organization of brain-like connectivity structure
(Communications in Nonlinear Science and Numerical Simulation, 2020) Calvo Tapia, Carlos; Makarov Slizneva, Valeriy; van Leeuwen, Cees
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.
Cognitive Neural Network Driving DoF-Scalable Limbs in Time-Evolving Situations
(2018) Calvo Tapia, Carlos; Villacorta Atienza, José Antonio; Kastalskiy, Innokentiy; Díez Hermano, Sergio; Sánchez Jiménez, Abel; Makarov Slizneva, Valeriy
Object handling and manipulation are vital skills for humans and autonomous humanoid robots. The fundamental bases of how our brain solves such tasks remain largely unknown. Here we develop a novel approach that addresses the problem of limb movements in time-evolving situations at an abstract cognitive level. We exploit the concept of generalized cognitive maps constructed in the so-called handspace by a neural network simulating a wave simultaneously exploring different subject actions independently on the number of objects in the workspace. We show that the approach is scalable to limbs with minimalistic and redundant numbers of degrees of freedom (DoF). It also allows biasing the effort of reaching a target among different DoF.
¿Es la biomimética el futuro de la robótica?
(Boletín del Ilustre Colegio Oficial de Doctores y Licenciados en Filosofía y Letras y en Ciencias, 2016) Makarov Slizneva, Valeriy; Villacorta Atienza, José Antonio; Calvo Tapia, Carlos
Antes de que a principios del siglo XX el concepto de robot fuera introducido en nuestra cultura tal y como hoy lo concebimos, el hombre llevaba mucho tiempo persiguiendo el sueño de crear ‘humanos artificiales’. Autómatas capaces de tocar instrumentos, bailar o incluso jugar al ajedrez fueron desarrollados para el divertimento y asombro del público. Sin embargo, la tecnificación de nuestra sociedad nos ha llevado, más que a desear, a demandar robots capaces de realizar eficazmente numerosas tareas y sustituirnos así en situaciones tediosas, pesadas o peligrosas. Los espectaculares avances tecnológicos producidos en la segunda mitad del pasado siglo condujeron a una nueva era de conocimiento en la que la computación se erigía como una nueva piedra filosofal. Si bien así fue para numerosas disciplinas científicas, hoy en día la robótica no ha alcanzado las cotas que entonces se esperaban, y todavía no existen robots capaces de realizar siquiera las tareas más sencillas de forma realmente autónoma, como por ejemplo ir a la cocina, recoger platos y lavarlos.
El GPS dinámico del cerebro nos acerca al diseño de robots inteligentes
(Red.escubre, 2015) Makarov Slizneva, Valeriy; Calvo Tapia, Carlos; Villacorta Atienza, José Antonio
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.
Universal principles justify the existence of concept cells
(Scientific Reports, 2020) Calvo Tapia, Carlos; Tyukin, Ivan Y.; Makarov Slizneva, Valeriy
The widespread consensus argues that the emergence of abstract concepts in the human brain, such as a “table”, requires complex, perfectly orchestrated interaction of myriads of neurons. However, this is not what converging experimental evidence suggests. Single neurons, the so-called concept cells (CCs), may be responsible for complex tasks performed by humans. This finding, with deep implications for neuroscience and theory of neural networks, has no solid theoretical grounds so far. Our recent advances in stochastic separability of highdimensional data have provided the basis to validate the existence of CCs. Here, starting from a few first principles, we layout biophysical foundations showing that ccs are not only possible but highly likely in brain structures such as the hippocampus. Three fundamental conditions, fulfilled by the human brain, ensure high cognitive functionality of single cells: a hierarchical feedforward organization of large laminar neuronal strata, a suprathreshold number of synaptic entries to principal neurons in the strata, and a magnitude of synaptic plasticity adequate for each neuronal stratum. We illustrate the approach on a simple example of acquiring “musical memory” and show how the concept of musical notes can emerge.
Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations
(Frontiers in Neurorobotics, 2020) Calvo Tapia, Carlos; Villacorta Atienza, José Antonio; Díez Hermano, Sergio; Khoruzkho, Maxim; Lobov, Sergey; Potapov, Ivan; Sánchez Jiménez, Abel; Makarov Slizneva, Valeriy
Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot.
Limb Movement in Dynamic Situations Based on Generalized Cognitive Maps
(Mathematical Modelling of Natural Phenomena, 2017) Villacorta Atienza, José Antonio; Calvo Tapia, Carlos; Lobov, S.; Makarov Slizneva, Valeriy
The fundamental bases of how our brain solves different tasks of object manipulation remain largely unknown. Here we consider the problem of the limb movement in dynamic situations on an abstract cognitive level and propose a novel approach relying on: i) transformation of the problem from the limb workspace to the so-called hand-space, and ii) construction of a generalized cognitive map (GCM) in the hand-space. The GCM provides a trajectory that can be followed by the limb, which ensures an efficient collision-free movement and target catching in the workspace. Our numerical simulations confirm the approach feasibility but also reveal the problem complexity. We then validate the GCM-based solutions in real-life scenarios. We show that a GCM-equipped humanoid robot can catch a fly ball in a similar way as a human subject does. The static nature of the GCMs enables learning and automation of sophisticated cognitive behaviors exhibited by humans.
Fast social-like learning of complex behaviors based on motor motifs
(Physical Review E, 2018) Calvo Tapia, Carlos; Tyukin, Ivan Y.; Makarov Slizneva, Valeriy
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.