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Los sistemas neuronales biológicos responden a estímulos de una forma rápida y eficiente en el movimiento motor del cuerpo, comparado con los sistemas robóticos clásicos, los cuales requieren una capacidad de computación mucho más elevada. Una de las claves de estos sistemas es la codificación de la información en el dominio pulsante. Las neuronas se comunican por eventos con pequeños pulsos de corrientes producidas por intercambio de iones entre las dendritas y los axones de las mismas. La configuración en redes de neuronas permite no sólo el procesado de la información sensorial y su procesamiento en el dominio pulsante, sino también la propia actuación sobre los músculos en el formato pulsante. Este trabajo presenta la aplicación de un modelo de control motor basado en el procesado de pulsos, incluyendo la propia actuación sobre motores en el contexto de los pulsos. Se ha desarrollado un sistema de control en lazo cerrado por pulsos, denominado spikebased PID controller para FPGA, el cual se ha integrado en el esqueleto de un robot bioinspirado, BioRob X5 del CITEC de la Universidad de Bielefeld, para su uso en el desarrollo de modelos bioinspirados. El Robot, de más de 1m de largo, permite controlar las posiciones de las articulaciones usando control por pulsos y con un consumo menor de 1A para todos los grados de libertad funcionando al mismo tiempo. Compared to classic robotics, biological nervous systems respond to stimulus in a fast and efficient way regarding to the body motor movement. Classic robotic systems usually require higher computational capacity. One of the main keys of biological systems respect to robotic machines is the way the information is codded and transmitted. They use spikes. A neuron is the “basic” element that form biological nervous systems. Neurons communicate in an event-driven way through small current pulses (spikes) produced when ions are interchanged between dendrites and axons of different neurons. When neurons are arranged in networks, they allow not only the sensory information processing, but they also allow the actuation over the muscles in a spiking way. This paper presents the application of a motor control model based on spike processing, including the motor actuation in the spike domain. A close-loop control system, called spike-PID controller, has been developed for FPGA. This controller has been embedded into a bioinspired robot, called BioRob X5, at CITEC of the University of Bielefeld during a “Salvador de Madariaga” grant for a research visit in the july-september 2018 term. The robot, longer than 1 meter tall, allows the joint position control through spiking signals with a power consumption bellow 1A for the 4 DoF working at the same time. Ministerio de Educación y Ciencia (España)/FEDER. Proyecto COFNET TEC2016-77785-P

Neuronal signals are usually characterized in terms of their discharge rate, a description inadequate to account for the complex temporal organization of spike trains. Complex temporal properties, which are characteristic of neuronal systems, can only be described with the appropriate, complex mathematical tools. Here, I apply high order structure functions to the analysis of neuronal signals recorded from parkinsonian patients during functional neurosurgery, recovering multifractal properties. To achieve an accurate model of such multifractality is critical for understanding the basal ganglia, since other non-linear properties, such as entropy, depend on the fractal properties of complex systems. I propose a new approach to the study of neuronal signals: to study spiking activity in terms of the velocity of spikes, defining it as the inverse function of the instantaneous frequency. I introduce a neural field model that includes a non-linear gradient field, representing neuronal excitability, and a diffusive term to consider the physical properties of the electric field. Multifractality is present in the model for a range of diffusion coefficients, and multifractal temporal properties are mirrored into space. The model reproduces the behavior of human basal ganglia neurons and shows that it is like that of turbulent fluids. The results obtained from the model predict that passive electric properties of neuronal activity, including ephaptic coupling, are far more relevant to the human brain than what is usually considered: passive electric properties determine the temporal and spatial organization of neuronal activity in the neural tissue.

Compartmental models are the theoretical tool of choice for understanding single neuron computations. However, many models are incomplete, built ad hoc and require tuning for each novel condition rendering them of limited usability. Here, we present T2N, a powerful interface to control NEURON with Matlab and TREES toolbox, which supports generating models stable over a broad range of reconstructed and synthetic morphologies. We illustrate this for a novel, highly detailed active model of dentate granule cells (GCs) replicating a wide palette of experiments from various labs. By implementing known differences in ion channel composition and morphology, our model reproduces data from mouse or rat, mature or adult-born GCs as well as pharmacological interventions and epileptic conditions. This work sets a new benchmark for detailed compartmental modeling. T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions. We discuss possible T2N application in degeneracy studies. Fil: Beining, Marcel. Ernst Strungmann Institute; Alemania. Frankfurt Institute for Advanced Studies; Alemania. Goethe Universitat Frankfurt; Alemania Fil: Mongiat, Lucas Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina Fil: Schwarzacher, Stephan Wolfgang. Goethe Universitat Frankfurt; Alemania Fil: Cuntz, Hermann. Frankfurt Institute for Advanced Studies; Alemania. Ernst Strungmann Institute; Alemania Fil: Jedlicka, Peter. Goethe Universitat Frankfurt; Alemania

Both in vivo and in vitro recordings indicate that neuronal membrane potentials can make spontaneous transitions between distinct up and down states. At the network level, populations of neurons have been observed to make these transitions synchronously. Although synaptic activity and intrinsic neuron properties play an important role, the precise nature of the processes responsible for these phenomena is not known. Using a computational model we explore the interplay between intrinsic neuronal properties and synaptic fluctuations. Model neurons of the integrate-and-fire type were extended by adding a nonlinear membrane current. Networks of these neurons exhibit large amplitude synchronous spontaneous fluctuations that make the neurons jump between up and down states, thereby producing bimodal membrane potential distributions. The effect of sensory stimulation on network responses depends on whether the stimulus is applied during an up state or deeply inside a down state. External noise can be varied to modulate the network continuously between two extreme regimes in which it remains permanently in either the up or the down state.

In this paper, a new computer software package, Yalzer, is introduced for simulating single-compartmental model of neurons. Passive or excitable membranes with voltage-gated ion channels can be modeled, and current clamp and voltage clamp experiments can be simulated. In the Yalzer, first-order differential equations used to define the dynamics of the gate variables and the membrane potential are solved by two separate integration methods with variable time steps: forward Euler and exponential Euler methods. Outputs of the simulation are shown on a spreadsheet template for allowing flexible data manipulation and can be graphically displayed. The user can define the model in detail, and examine the excitability of the model and the dynamics of voltage-gated ion channels. The software package addresses to ones who want to run simple simulations of neurons without need to any programming language skills or expensive software. It can also be used for educational purposes. (C) 2003 Elsevier Ireland Ltd. All rights reserved.