Manuel A. Sánchez-Montañés

Local and Global Gating of Synaptic Plasticity

Mechanisms influencing learning in neural networks are usually investigated on either a local or a global scale. The former relates to synaptic processes, the latter to unspecific modulatory systems. Here we study the interaction of a local learning rule that evaluates coincidences of pre- and postsynaptic action potentials and a global modulatory mechanism, such as the action of the basal forebrain onto cortical neurons. The simulations demonstrate that the interaction of these mechanisms leads to a learning rule supporting fast learning rates, stability, and flexibility. Furthermore, the simulations generate two experimentally testable predictions on the dependence of backpropagating action potential on basal forebrain activity and the relative timing of the activity of inhibitory and excitatory neurons in the neocortex.

Fisher information and optimal odor sensors

Abstract We discuss how the Fisher information matrix (FIM) may be used as part of an optimization procedure for selecting odor sensors within a population so as to maximize the accuracy with which the overall sensory system may estimate the stimulus. While the same approach may be equally applied to any sensory system that exploits a population coding of the stimulus in order to optimize its performance, we demonstrate how this technique may be used to analyze the performance of both biological and artificial olfactory systems. Thus one application of this method is the optimal design of arrays of artificial olfactory sensors.

Learning sensory maps with real-world stimuli in real time using a biophysically realistic learning rule

We present a real-time model of learning in the auditory cortex that is trained using real-world stimuli. The system consists of a peripheral and a central cortical network of spiking neurons. The synapses formed by peripheral neurons on the central ones are subject to synaptic plasticity. We implemented a biophysically realistic learning rule that depends on the precise temporal relation of pre- and postsynaptic action potentials. We demonstrate that this biologically realistic real-time neuronal system forms stable receptive fields that accurately reflect the spectral content of the input signals and that the size of these representations can be biased by global signals acting on the local learning mechanism. In addition, we show that this learning mechanism shows fast acquisition and is robust in the presence of large imbalances in the probability of occurrence of individual stimuli and noise.

A Central pattern generator to control a pyloric-based system

Abstract. A central pattern generator (CPG) is built to control a mechanical device (plant) inspired by the pyloric chamber of the lobster. Conductance-based models are used to construct the neurons of the CPG. The plant has an associated function that measures the amount of food flowing through it per unit of time. We search for the best set of solutions that give a high positive flow of food in the maximization function. The plant is symmetric and the model neurons are identical to avoid any bias in the space of solutions. We find that the solution is not unique and that three neurons are sufficient to produce positive flow. We propose an effective principle for CPGs (effective on-off connectivity) and a few predictions to be corroborated in the pyloric system of the lobster.

Imaging hypothalamic activity using diffusion weighted magnetic resonance imaging in the mouse and human brain

Hypothalamic appetite regulation is a vital homeostatic process underlying global energy balance in animals and humans, its disturbances resulting in feeding disorders with high morbidity and mortality. The objective evaluation of appetite remains difficult, very often restricted to indirect measurements of food intake and body weight. We report here, the direct, non-invasive visualization of hypothalamic activation by fasting using diffusion weighted magnetic resonance imaging, in the mouse brain as well as in a preliminary study in the human brain. The brain of fed or fasted mice or humans were imaged at 7 or 1.5 Tesla, respectively, by diffusion weighted magnetic resonance imaging using a complete range of b values (10<b<2000s.mm(-2)). The diffusion weighted image data sets were registered and analyzed pixel by pixel using a biexponential model of diffusion, or a model-free Linear Discriminant Analysis approach. Biexponential fittings revealed statistically significant increases in the slow diffusion parameters of the model, consistent with a neurocellular swelling response in the fasted hypothalamus. Increased resolution approaches allowed the detection of increases in the diffusion parameters within the Arcuate Nucleus, Ventromedial Nucleus and Dorsomedial Nucleus. Independently, Linear Discriminant Analysis was able to classify successfully the diffusion data sets from mice and humans between fed and fasted states. Present results are consistent with increased glutamatergic neurotransmission during orexigenic firing, a process resulting in increased ionic accumulation and concomitant osmotic neurocellular swelling. This swelling response is spatially extendable through surrounding astrocytic networks until it becomes MRI detectable. Present findings open new avenues for the direct, non-invasive, evaluation of appetite disorders and other hypothalamic pathologies helping potentially in the development of the corresponding therapies.

Hypothalamic metabolic compartmentation during appetite regulation as revealed by magnetic resonance imaging and spectroscopy methods

We review the role of neuroglial compartmentation and transcellular neurotransmitter cycling during hypothalamic appetite regulation as detected by Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) methods. We address first the neurochemical basis of neuroendocrine regulation in the hypothalamus and the orexigenic and anorexigenic feed-back loops that control appetite. Then we examine the main MRI and MRS strategies that have been used to investigate appetite regulation. Manganese-enhanced magnetic resonance imaging (MEMRI), Blood oxygenation level-dependent contrast (BOLD), and Diffusion-weighted magnetic resonance imaging (DWI) have revealed Mn2+ accumulations, augmented oxygen consumptions, and astrocytic swelling in the hypothalamus under fasting conditions, respectively. High field 1H magnetic resonance in vivo, showed increased hypothalamic myo-inositol concentrations as compared to other cerebral structures. 1H and 13C high resolution magic angle spinning (HRMAS) revealed increased neuroglial oxidative and glycolytic metabolism, as well as increased hypothalamic glutamatergic and GABAergic neurotransmissions under orexigenic stimulation. We propose here an integrative interpretation of all these findings suggesting that the neuroendocrine regulation of appetite is supported by important ionic and metabolic transcellular fluxes which begin at the tripartite orexigenic clefts and become extended spatially in the hypothalamus through astrocytic networks becoming eventually MRI and MRS detectable.

Towards a more realistic evaluation: testing the ability to predict future tastes of matrix factorization-based recommenders

The use of temporal dynamic terms in Matrix Factorization (MF) models of recommendation have been proposed as a means to obtain better accuracy in rating prediction task. However, the way such models have been tested may not be a realistic setting for recommendation. In this paper, we evaluated rating prediction and top-N recommendation tasks using a MF model with and without temporal dynamic terms under two evaluation settings. Our experiments show that the addition of dynamic parameters do not necessarily yield to better results on these tasks when a more strict time-aware separation of train/test data is performed, and moreover, results may vary notably when different evaluation schemes are used.

Why do olfactory neurons have unspecific receptive fields

Biological olfactory neurons are deployed as a population, most responding to a large variety of chemical compounds, that is, they possess unspecific receptive fields. The question of whether this unspecificity results from some physical constraint placed upon chemical transduction, or on the other hand, is beneficial to system performance is unclear. In this paper we employ the notion of Fisher information to address this question by quantifying how both the distribution and the tunings of the receptive fields within olfactory receptor populations affect the optimal estimation performance of the system. Our results show that overlapping sensory neuron tunings that respond to common chemical compounds have better estimation performance than perfectly specific tunings. Our results suggest two phenomena that might represent general principles of organization within biological sensory systems responding to multiple stimuli: maximization of the diversity of tunings and homogeneity in the distribution of these different receptive fields across the stimulus space (independent of the statistics of the input stimuli). Our model predicts that a local randomized mechanism controlling receptor specificities generates optimal multidimensional stimulus estimation, for which there is some experimental evidence from the biology.

A biologically inspired visual system for an autonomous robot

Abstract We have implemented an artificial visual system that takes advantage of known properties of biological systems to achieve segmentation and recognition of simple images. The use of biologically plausible mechanisms makes the system inherit a series of features that are present in biological systems, such as flexibility, robustness and adaptability. The implementation of the model on an autonomous robot has proved its reliability and robustness in real environments and shows the relevance of this kind of approach.

Spatio-temporal information in an artificial olfactory mucosa

Deploying chemosensor arrays in close proximity to stationary phases imposes stimulus-dependent spatio-temporal dynamics on their response and leads to improvements in complex odour discrimination. These spatio-temporal dynamics need to be taken into account explicitly when considering the detection performance of this new odour sensing technology, termed an artificial olfactory mucosa. For this purpose, we develop here a new measure of spatio-temporal information that combined with an analytical model of the artificial mucosa, chemosensor and noise dynamics completely characterizes the discrimination capability of the system. This spatio-temporal information measure allows us to quantify the contribution of both space and time to discrimination performance and may be used as part of optimization studies or calculated directly from an artificial mucosa output. Our formal analysis shows that exploiting both space and time in the mucosa response always outperforms the use of space alone and is further demonstrated by comparing the spatial versus spatio-temporal information content of mucosa experimental data. Together, the combination of the spatio-temporal information measure and the analytical model can be applied to extract the general principles of the artificial mucosa design as well as to optimize the physical and operating parameters that determine discrimination performance.