Hierarchical neural network with high storage capacity

A recent method used to optimize biased neural networks with low levels of activity is applied to a hierarchical model. As a consequence, the performance of the system is strongly enhanced. The steps to achieve optimization are analyzed in detail.

Noise and periodic modulations in neural excitable medium

We have analyzed the interplay between noise and periodic modulations in a mean field model of a neural excitable medium. For this purpose, we have considered two types of modulations, namely, variations of the resistance and oscillations of the threshold. In both cases, stochastic resonance is present, irrespective of whether the system is monostable or bistable.

TrkB and TrkC signaling are required for maturation and synaptogenesis of hippocampal connections

Recent studies have suggested a role for neurotrophins in the growth and refinement of neural connections, in dendritic growth, and in activity-dependent adult plasticity. To unravel the role of endogenous neurotrophins in the development of neural connections in the CNS, we studied the ontogeny of hippocampal afferents intrkB (¿/¿) and trkC (¿/¿) mice. Injections of lipophilic tracers in the entorhinal cortex and hippocampus of newborn mutant mice showed that the ingrowth of entorhinal and commissural/associational afferents to the hippocampus was not affected by these mutations. Similarly, injections of biocytin in postnatal mutant mice (P10¿P16) did not reveal major differences in the topographic patterns of hippocampal connections. In contrast, quantification of biocytin-filled axons showed that commissural and entorhinal afferents have a reduced number of axon collaterals (21¿49%) and decreased densities of axonal varicosities (8¿17%) in both trkB (¿/¿) and trkC (¿/¿) mice. In addition, electron microscopic analyses showed thattrkB (¿/¿) and trkC (¿/¿) mice have lower densities of synaptic contacts and important structural alterations of presynaptic boutons, such as decreased density of synaptic vesicles. Finally, immunocytochemical studies revealed a reduced expression of the synaptic-associated proteins responsible for synaptic vesicle exocytosis and neurotransmitter release (v-SNAREs and t-SNAREs), especially in trkB (¿/¿) mice. We conclude that neither trkB nor trkC genes are essential for the ingrowth or layer-specific targeting of hippocampal connections, although the lack of these receptors results in reduced axonal arborization and synaptic density, which indicates a role for TrkB and TrkC receptors in the developmental regulation of synaptic inputs in the CNS in vivo. The data also suggest that the genes encoding for synaptic proteins may be targets of TrkB and TrkC signaling pathways.

Emerging functions of myelin associated proteins during development neuronal plasticity and and neurpdegeneration.

Adult mammalian central nervous system (CNS) axons have a limited regrowth capacity following injury. Myelin-associated inhibitors (MAIs) limit axonal outgrowth and their blockage improves the regeneration of damaged fiber tracts. Three of these proteins, Nogo-A, MAG and OMgp, share two common neuronal receptors: NgR1, together with its co-receptors (p75(NTR), TROY and Lingo-1), and the recently described paired immunoglobulin-like receptor B (PirB). These proteins impair neuronal regeneration by limiting axonal sprouting. Some of the elements involved in the myelin inhibitory pathways may still be unknown, but the discovery that blocking both PirB and NgR1 activities leads to near-complete release from myelin inhibition, sheds light on one of the most competitive and intense fields of neuroregeneration study during in recent decades. In parallel with the identification and characterization of the roles and functions of these inhibitory molecules in axonal regeneration, data gathered in the field strongly suggest that most of these proteins have roles other than axonal growth inhibition. The discovery of a new group of interacting partners for myelin-associated receptors and ligands, as well as functional studies within or outside the CNS environment, highlights the potential new physiological roles for these proteins in processes such as development, neuronal homeostasis, plasticity and neurodegeneration.

Sensorimotor Plasticity after Music-Supported Therapy in Chronic Stroke Patients Revealed by Transcranial Magnetic Stimulation

BACKGROUND: Several recently developed therapies targeting motor disabilities in stroke sufferers have shown to be more effective than standard neurorehabilitation approaches. In this context, several basic studies demonstrated that music training produces rapid neuroplastic changes in motor-related brain areas. Music-supported therapy has been recently developed as a new motor rehabilitation intervention. METHODS AND RESULTS: In order to explore the plasticity effects of music-supported therapy, this therapeutic intervention was applied to twenty chronic stroke patients. Before and after the music-supported therapy, transcranial magnetic stimulation was applied for the assessment of excitability changes in the motor cortex and a 3D movement analyzer was used for the assessment of motor performance parameters such as velocity, acceleration and smoothness in a set of diadochokinetic movement tasks. Our results suggest that the music-supported therapy produces changes in cortical plasticity leading the improvement of the subjects' motor performance. CONCLUSION: Our findings represent the first evidence of the neurophysiological changes induced by this therapy in chronic stroke patients, and their link with the amelioration of motor performance. Further studies are needed to confirm our observations.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme

Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential—a phenomenon referred to as “phase-of-firing coding” (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactions—only then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents (~10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.

Sensorimotor Plasticity after Music-Supported Therapy in Chronic Stroke Patients Revealed by Transcranial Magnetic Stimulation

BACKGROUND: Several recently developed therapies targeting motor disabilities in stroke sufferers have shown to be more effective than standard neurorehabilitation approaches. In this context, several basic studies demonstrated that music training produces rapid neuroplastic changes in motor-related brain areas. Music-supported therapy has been recently developed as a new motor rehabilitation intervention. METHODS AND RESULTS: In order to explore the plasticity effects of music-supported therapy, this therapeutic intervention was applied to twenty chronic stroke patients. Before and after the music-supported therapy, transcranial magnetic stimulation was applied for the assessment of excitability changes in the motor cortex and a 3D movement analyzer was used for the assessment of motor performance parameters such as velocity, acceleration and smoothness in a set of diadochokinetic movement tasks. Our results suggest that the music-supported therapy produces changes in cortical plasticity leading the improvement of the subjects' motor performance. CONCLUSION: Our findings represent the first evidence of the neurophysiological changes induced by this therapy in chronic stroke patients, and their link with the amelioration of motor performance. Further studies are needed to confirm our observations.

Forecasting tourism demand to Catalonia: neural networks vs. time series models

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourismdemand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time seriesmethods at a regional level. Seasonality and volatility are important features of tourism data, which makes it a particularly favourable context in which to compare the forecasting performance of linear models to that of nonlinear alternative approaches. Pre-processed official statistical data of overnight stays and tourist arrivals fromall the different countries of origin to Catalonia from 2001 to 2009 is used in the study. When comparing the forecasting accuracy of the different techniques for different time horizons, autoregressive integrated moving average models outperform self-exciting threshold autoregressions and artificial neural network models, especially for shorter horizons. These results suggest that the there is a trade-off between the degree of pre-processing and the accuracy of the forecasts obtained with neural networks, which are more suitable in the presence of nonlinearity in the data. In spite of the significant differences between countries, which can be explained by different patterns of consumer behaviour,we also find that forecasts of tourist arrivals aremore accurate than forecasts of overnight stays.

Sensorimotor Plasticity after Music-Supported Therapy in Chronic Stroke Patients Revealed by Transcranial Magnetic Stimulation

BACKGROUND: Several recently developed therapies targeting motor disabilities in stroke sufferers have shown to be more effective than standard neurorehabilitation approaches. In this context, several basic studies demonstrated that music training produces rapid neuroplastic changes in motor-related brain areas. Music-supported therapy has been recently developed as a new motor rehabilitation intervention. METHODS AND RESULTS: In order to explore the plasticity effects of music-supported therapy, this therapeutic intervention was applied to twenty chronic stroke patients. Before and after the music-supported therapy, transcranial magnetic stimulation was applied for the assessment of excitability changes in the motor cortex and a 3D movement analyzer was used for the assessment of motor performance parameters such as velocity, acceleration and smoothness in a set of diadochokinetic movement tasks. Our results suggest that the music-supported therapy produces changes in cortical plasticity leading the improvement of the subjects' motor performance. CONCLUSION: Our findings represent the first evidence of the neurophysiological changes induced by this therapy in chronic stroke patients, and their link with the amelioration of motor performance. Further studies are needed to confirm our observations.

Collaborative explicit plasticity framework: a conceptual scheme for the generation of plastic and group-aware user interfaces

The advent of new advances in mobile computing has changed the manner we do our daily work, even enabling us to perform collaborative activities. However, current groupware approaches do not offer an integrating and efficient solution that jointly tackles the flexibility and heterogeneity inherent to mobility as well as the awareness aspects intrinsic to collaborative environments. Issues related to the diversity of contexts of use are collected under the term plasticity. A great amount of tools have emerged offering a solution to some of these issues, although always focused on individual scenarios. We are working on reusing and specializing some already existing plasticity tools to the groupware design. The aim is to offer the benefits from plasticity and awareness jointly, trying to reach a real collaboration and a deeper understanding of multi-environment groupware scenarios. In particular, this paper presents a conceptual framework aimed at being a reference for the generation of plastic User Interfaces for collaborative environments in a systematic and comprehensive way. Starting from a previous conceptual framework for individual environments, inspired on the model-based approach, we introduce specific components and considerations related to groupware.

Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.

Modelling bird species richness with neural networks for forest landscape management in NE Spain

Para preservar la biodiversidad de los ecosistemas forestales de la Europa mediterránea en escenarios actuales y futuros de cambio global mediante una gestión forestal sostenible es necesario determinar cómo influye el medio ambiente y las propias características de los bosques sobre la biodiversidad que éstos albergan. Con este propósito, se analizó la influencia de diferentes factores ambientales y de estructura y composición del bosque sobre la riqueza de aves forestales a escala 1 × 1 km en Cataluña (NE de España). Se construyeron modelos univariantes y multivariantes de redes neuronales para respectivamente explorar la respuesta individual a las variables y obtener un modelo parsimonioso (ecológicamente interpretable) y preciso. La superficie de bosque (con una fracción de cabida cubierta superior a 5%), la fracción de cabida cubierta media, la temperatura anual y la precipitación estival medias fueron los mejores predictores de la riqueza de aves forestales. La red neuronal multivariante obtenida tuvo una buena capacidad de generalización salvo en las localidades con una mayor riqueza. Además, los bosques con diferentes grados de apertura del dosel arbóreo, más maduros y más diversos en cuanto a su composición de especies arbóreas se asociaron de forma positiva con una mayor riqueza de aves forestales. Finalmente, se proporcionan directrices de gestión para la planificación forestal que permitan promover la diversidad ornítica en esta región de la Europa mediterránea.

Wnt-3a and Wnt-3 differently stimulate proliferation and neurogenesis of spinal neural precursors and promote neurite outgrowth by canonical signaling

Wnt factors regulate neural stem cell development and neuronal connectivity. Here we investigated whether Wnt-3a and Wnt-3, expressed in the developing spinal cord, regulate proliferation and the neuronal differentiation of spinal cord neural precursors (SCNP). Wnt-3a promoted a sustained increase of SCNP proliferation, whereas Wnt-3 enhanced SCNP proliferation transiently and increased neurogenesis through β-catenin signaling. Consistent with this, Wnt-3a and Wnt-3 differently regulate the expression of Cyclin-dependent kinase inhibitors. Furthermore, Wnt-3a and Wnt-3 stimulated neurite outgrowth in SCNP-derived neurons through ß-catenin and TCF4-dependent transcription. GSK-3ß inhibitors mimicked Wnt signaling and promoted neurite outgrowth in established cultures. We conclude that Wnt-3a and Wnt-3 signal through the canonical Wnt/β-catenin pathway to regulate different aspects of SCNP development. These findings may be of therapeutic interest for the treatment of neurodegenerative diseases and nerve injury.

Synaptic depression and slow oscillatory activity in a biophysical network model of the cerebral cortex

Short-term synaptic depression (STD) is a form of synaptic plasticity that has a large impact on network computations. Experimental results suggest that STD is modulated by cortical activity, decreasing with activity in the network and increasing during silent states. Here, we explored different activity-modulation protocols in a biophysical network model for which the model displayed less STD when the network was active than when it was silent, in agreement with experimental results. Furthermore, we studied how trains of synaptic potentials had lesser decay during periods of activity (UP states) than during silent periods (DOWN states), providing new experimental predictions. We next tackled the inverse question of what is the impact of modifying STD parameters on the emergent activity of the network, a question difficult to answer experimentally. We found that synaptic depression of cortical connections had a critical role to determine the regime of rhythmic cortical activity. While low STD resulted in an emergent rhythmic activity with short UP states and long DOWN states, increasing STD resulted in longer and more frequent UP states interleaved with short silent periods. A still higher synaptic depression set the network into a non-oscillatory firing regime where DOWN states no longer occurred. The speed of propagation of UP states along the network was not found to be modulated by STD during the oscillatory regime; it remained relatively stable over a range of values of STD. Overall, we found that the mutual interactions between synaptic depression and ongoing network activity are critical to determine the mechanisms that modulate cortical emergent patterns.