Subtractive, divisive and non-monotonic gain control in feedforward nets linearized by noise and delays

The control of input-to-output mappings, or gain control, is one of the main strategies used by neural networks for the processing and gating of information. Using a spiking neural network model, we studied the gain control induced by a form of inhibitory feedforward circuitry—also known as “open-loop feedback”—, which has been experimentally observed in a cerebellum-like structure in weakly electric fish. We found, both analytically and numerically, that this network displays three different regimes of gain control: subtractive, divisive, and non-monotonic. Subtractive gain control was obtained when noise is very low in the network. Also, it was possible to change from divisive to non-monotonic gain control by simply modulating the strength of the feedforward inhibition, which may be achieved via long-term synaptic plasticity. The particular case of divisive gain control has been previously observed in vivo in weakly electric fish. These gain control regimes were robust to the presence of temporal delays in the inhibitory feedforward pathway, which were found to linearize the input-to-output mappings (or f-I curves) via a novel variability-increasing mechanism. Our findings highlight the feedforward-induced gain control analyzed here as a highly versatile mechanism of information gating in the brain

Emergence of Resonances in Neural Systems: The Interplay between Adaptive Threshold and Short-Term Synaptic Plasticity

In this work we study the detection of weak stimuli by spiking (integrate-and-fire) neurons in the presence of certain level of noisy background neural activity. Our study has focused in the realistic assumption that the synapses in the network present activity-dependent processes, such as short-term synaptic depression and facilitation. Employing mean-field techniques as well as numerical simulations, we found that there are two possible noise levels which optimize signal transmission. This new finding is in contrast with the classical theory of stochastic resonance which is able to predict only one optimal level of noise. We found that the complex interplay between adaptive neuron threshold and activity-dependent synaptic mechanisms is responsible for this new phenomenology. Our main results are confirmed by employing a more realistic FitzHugh-Nagumo neuron model, which displays threshold variability, as well as by considering more realistic stochastic synaptic models and realistic signals such as poissonian spike trains

Learning Contrast-Invariant Cancellation of Redundant Signals in Neural Systems

Cancellation of redundant information is a highly desirable feature of sensory systems, since it would potentially lead to a more efficient detection of novel information. However, biologically plausible mechanisms responsible for such selective cancellation, and especially those robust to realistic variations in the intensity of the redundant signals, are mostly unknown. In this work, we study, via in vivo experimental recordings and computational models, the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli. We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system. Our model, which incorporates heterogeneously-delayed feedback, bursting dynamics and burst-induced STDP, is in agreement with our in vivo observations. In addition, the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway, and provides a strong prediction on the parallel fiber potentiation time scale. Finally, our model predicts the existence of an optimal learning contrast around 15% contrast levels, which are commonly experienced by interacting fish

Irregular Dynamics in Up and Down Cortical States

Complex coherent dynamics is present in a wide variety of neural systems. A typical example is the voltage transitions between up and down states observed in cortical areas in the brain. In this work, we study this phenomenon via a biologically motivated stochastic model of up and down transitions. The model is constituted by a simple bistable rate dynamics, where the synaptic current is modulated by short-term synaptic processes which introduce stochasticity and temporal correlations. A complete analysis of our model, both with mean-field approaches and numerical simulations, shows the appearance of complex transitions between high (up) and low (down) neural activity states, driven by the synaptic noise, with permanence times in the up state distributed according to a power-law. We show that the experimentally observed large fluctuation in up and down permanence times can be explained as the result of sufficiently noisy dynamical synapses with sufficiently large recovery times. Static synapses cannot account for this behavior, nor can dynamical synapses in the absence of noise

Crecimiento vegetativo y reproductivo de dos CVS. de manzanas sobre distintos portainjertos, en un huerto de la VII Region.

Resumen (Spanish, English)61 p.El objetivo de este ensayo fue evaluar el crecimiento vegetativo (brotes, troncos y hojas de dardos y brotes) y de frutos de manzano (Malus sylvestris L. Mill. var. Domestica Borkh.), cv. Royal Gala sobre tres diferentes portainjertos: MM111, M7, MM106 y cv. Red Chief sobre MM106, durante la temporada 2001/2002, en un huerto determinado de la VII Región. El sistema de conducción que poseen los árboles es mixto entre eje central y axis vertical. También se determinó el desarrollo foliar de la copa, deshojando completamente 5 árboles de cada combinación, inmediatamente después de cosecha. Los resultados obtenidos, confirman que la dinámica de crecimiento del fruto es de tipo sigmoidea simple, presentando su mayor tasa de crecimiento a finales de la temporada. En lo referente a la tasa de crecimiento, todas las combinaciones variedad/portainjerto, presentaron comportamientos similares; sin embargo el cv. Royal Gala sobre todos los portainjertos estudiados presentó mayores tasas de crecimiento que el cv. Red Chief sobre MM106. Los dardos sin fruto presentaron mayor despliegue foliar que los dardos con frutos, estabilizándose el área foliar de ambos a principios de la temporada. El área foliar de brotes mostró un comportamiento similar al crecimiento del brote. En lo concerniente al desarrollo foliar total por árbol, el cv. Royal Gala sobre MM111 obtuvo 5,7 kg de peso fresco, 2,1 de peso seco para un total de 10.728 hojas; el área foliar fue de 21,5 m2, mientras que el IAF por planta fue de 3,09; sobre M7 3,5 kg de peso fresco, 1,2 de peso seco, 6.888 hojas, 13,23 m2 de área foliar, con un IAF por planta de 2,14; en MM106 se obtuvo 2,44 Kg de peso fresco, 0,91 de peso seco, 4.966 hojas, 8,3 m2 de área foliar y un IAF por planta de 1,43. El cultivar Red Chief sobre MM106 obtuvo 1,8 Kg de peso fresco, 0,14 de peso seco, 3.348 hojas, 4,8 m2 de área foliar y 1,14 de IAF por planta. Al observar los resultados de IAF obtenidos en las distintas combinaciones variedad/patrón son inferiores a los valores obtenidos en otras investigaciones similares