Reelin immunoreactivity in dissociated cultures of the postnatal hippocampus

Reelin is an extracellular matrix glycoprotein expressed in different nerve cell populations in the developing, early postnatal and adult central nervous system. During histogenesis of the neocortex and hippocampus, reelin is present in Cajal-Retzius cells and other early neurons and contributes to correct layering of these regions. During early postnatal life, pioneer neurons disappear and reelin expression establishes in a subpopulation of cortical and hippocampal GABAergic interneurons, where it is maintained throughout adult life. We studied the developmental distribution pattern of reelin in dissociated cultures obtained from the early postnatal hippocampus to verify whether or not such a maturation phenomenon is maintained in vitro. Reelin is expressed both in Cajal-Retzius cells and multipolar and pyramidal neurons in younger cultures. The density of reelin-positive Cajal-Retzius cells dropped drastically by about 84% in 4-week-old cultures. Multipolar and pyramidal neurons containing reelin represented 12% of the total cell population in younger cultures and decreased by about 25% after 3 to 4 weeks of cultivation. Their density was significantly lower in cultures of the same age treated with glutamate receptor antagonists. These reelin-positive multipolar and pyramidal neurons were heterogeneous, including a larger amount of non-GABAergic, and 30-40% of GABAergic neurons. Cells double labeled for reelin and the GABA synthesizing enzyme glutamic acid decarboxylase represented about 4% of the total neuron population in culture and their density remained constant with age. It is thus possible that the decrease in the total reelin population may selectively be of importance to the larger non-GABAergic fraction of reelin cells. This study shows that reelin-expressing neurons are maintained in dissociated cultures of the neonatal hippocampus and their distribution and age-dependent changes in density resemble those of the early postnatal hippocampus in vivo.

Activation of metabotropic glutamate 5 and NMDA receptors underlies the induction of persistent bursting and associated long-lasting changes in CA3 recurrent connections.

The aim of this study was to describe the induction and expression mechanisms of a persistent bursting activity in a horizontal slice preparation of the rat limbic system that includes the ventral part of the hippocampus and the entorhinal cortex. Disinhibition of this preparation by bicuculline led to interictal-like bursts in the CA3 region that triggered synchronous activity in the entorhinal cortex. Washout of bicuculline after a 1 hr application resulted in a maintained production of hippocampal bursts that continued to spread to the entorhinal cortex. Separation of CA3 from the entorhinal cortex caused the activity in the latter to become asynchronous with CA3 activity in the presence of bicuculline and disappear after washout; however, in CA3, neither the induction of bursting nor its persistence were affected. Associated with the CA3 persistent bursting, a strengthening of recurrent collateral excitatory input to CA3 pyramidal cells and a decreased input to CA3 interneurons was found. Both the induction of the persistent bursting and the changes in synaptic strength were prevented by antagonists of metabotropic glutamate 5 (mGlu5) or NMDA receptors or protein synthesis inhibitors and did not occur in slices from mGlu5 receptor knock-out mice. The above findings suggest potential synaptic mechanisms by which the hippocampus switches to a persistent interictal bursting mode that may support a spread of interictal-like bursting to surrounding temporal lobe regions.

Modulatory Effects of Eschscholzia californica Alkaloids on Recombinant GABAA Receptors.

The California poppy (Eschscholzia californica Cham.) contains a variety of natural compounds including several alkaloids found exclusively in this plant. Because of the sedative, anxiolytic, and analgesic effects, this herb is currently sold in pharmacies in many countries. However, our understanding of these biological effects at the molecular level is still lacking. Alkaloids detected in E. californica could be hypothesized to act at GABAA receptors, which are widely expressed in the brain mainly at the inhibitory interneurons. Electrophysiological studies on a recombinant α 1 β 2 γ 2 GABAA receptor showed no effect of N-methyllaurotetanine at concentrations lower than 30 μM. However, (S)-reticuline behaved as positive allosteric modulator at the α 3, α 5, and α 6 isoforms of GABAA receptors. The depressant properties of aerial parts of E. californica are assigned to chloride-current modulation by (S)-reticuline at the α 3 β 2 γ 2 and α 5 β 2 γ 2 GABAA receptors. Interestingly, α 1, α 3, and α 5 were not significantly affected by (R)-reticuline, 1,2-tetrahydroreticuline, codeine, and morphine-suspected (S)-reticuline metabolites in the rodent brain.

Loss of Nogo-A-expressing neurons in a rat model of Parkinson's disease

The myelin-associated protein Nogo-A is among the most potent neurite growth inhibitors in the adult CNS. Recently, Nogo-A expression was demonstrated in a number of neuronal subpopulations of the adult and developing CNS but at present, little is known about the expression of Nogo-A in the nigrostriatal system, a brain structure severely affected in Parkinson's disease (PD). The present study sought to characterize the expression pattern of Nogo-A immunoreactive (ir) cells in the adult ventral mesencephalon of control rats and in the 6-hydroxydopamine (6-OHDA) rat model of PD. Immunohistochemical analyses of normal adult rat brain showed a distinct expression of Nogo-A in the ventral mesencephalon, with the highest level in the substantia nigra pars compacta (SNc) where it co-localized with dopaminergic neurons. Analyses conducted 1week and 1 month after unilateral striatal injections of 6-OHDA disclosed a severe loss of the number of Nogo-A-ir cells in the SNc. Notably, at 1week after treatment, more dopaminergic neurons expressing Nogo-A were affected by the 6-OHDA toxicity than Nogo-A-negative dopaminergic neurons. However, at later time points more of the surviving dopaminergic neurons expressed Nogo-A. In the striatum, both small and large Nogo-A-positive cells were detected. The large cells were identified as cholinergic interneurons. Our results suggest yet unidentified functions of Nogo-A in the CNS beyond the inhibition of axonal regeneration and plasticity, and may indicate a role for Nogo-A in PD.

THE ULTRASTRUCTURAL ORGANIZATION OF THE HYPOGLOSSAL NUCLEUS IN THE RAT (SYNAPTOLOGY, CRANIAL NERVES)

An ultrastructural study of the hypoglossal nucleus (XII) in the rat has revealed two distinct neuronal populations. Hypoglossal motoneurons comprised the largest population of neurons in XII and were identified following injection of horseradish peroxidase (HRP) into the tongue. Motoneurons were large (25-50(mu)m), multipolar in shape and distributed throughout XII. The nucleus was large, round and centrally located, and the cytoplasm was characterized by dense lamellar arrays of rough endoplasmic reticulum. In contrast, a second population of small (10-18(mu)m), round to oval shaped neurons was found restricted to the ventral and dorsolateral regions of XII. The nucleus was markedly invaginated and eccentric, the cytoplasm scant and filled with free ribosomes, and the absence of lamellar arrays of rough endoplasmic reticulum was conspicuous. Neurons of this type were never found to contain HRP reaction product. These results demonstrate that the hypoglossal nucleus does not consist solely of motoneurons, but includes a distinctly separate, presumably non-motoneuronal pool. Arguments are presented in favor of this second neuron population being interneurons. The functional significance of these findings in relation to tongue control is discussed. ^

Neocortical hyperexcitability defect in a mutant mouse model of spike-wave epilepsy, {\it stargazer}

Single-locus mutations in mice can express epileptic phenotypes and provide critical insights into the naturally occurring defects that alter excitability and mediate synchronization in the central nervous system (CNS). One such recessive mutation (on chromosome (Chr) 15), stargazer(stg/stg) expresses frequent bilateral 6-7 cycles per second (c/sec) spike-wave seizures associated with behavioral arrest, and provides a valuable opportunity to examine the inherited lesion associated with spike-wave synchronization.^ The existence of distinct and heterogeneous defects mediating spike-wave discharge (SWD) generation has been demonstrated by the presence of multiple genetic loci expressing generalized spike-wave activity and the differential effects of pharmacological agents on SWDs in different spike-wave epilepsy models. Attempts at understanding the different basic mechanisms underlying spike-wave synchronization have focused on $\gamma$-aminobutyric acid (GABA) receptor-, low threshold T-type Ca$\sp{2+}$ channel-, and N-methyl-D-aspartate receptor (NMDA-R)-mediated transmission. It is believed that defects in these modes of transmission can mediate the conversion of normal oscillations in a trisynaptic circuit, which includes the neocortex, reticular nucleus and thalamus, into spike-wave activity. However, the underlying lesions involved in spike-wave synchronization have not been clearly identified.^ The purpose of this research project was to locate and characterize a distinct neuronal hyperexcitability defect favoring spike-wave synchronization in the stargazer brain. One experimental approach for anatomically locating areas of synchronization and hyperexcitability involved an attempt to map patterns of hypersynchronous activity with antibodies to activity-induced proteins.^ A second approach to characterizing the neuronal defect involved examining the neuronal responses in the mutant following application of pharmacological agents with well known sites of action.^ In order to test the hypothesis that an NMDA receptor mediated hyperexcitability defect exists in stargazer neocortex, extracellular field recordings were used to examine the effects of CPP and MK-801 on coronal neocortical brain slices of stargazer and wild type perfused with 0 Mg$\sp{2+}$ artificial cerebral spinal fluid (aCSF).^ To study how NMDA receptor antagonists might promote increased excitability in stargazer neocortex, two basic hypotheses were tested: (1) NMDA receptor antagonists directly activate deep layer principal pyramidal cells in the neocortex of stargazer, presumably by opening NMDA receptor channels altered by the stg mutation; and (2) NMDA receptor antagonists disinhibit the neocortical network by blocking recurrent excitatory synaptic inputs onto inhibitory interneurons in the deep layers of stargazer neocortex.^ In order to test whether CPP might disinhibit the 0 Mg$\sp{2+}$ bursting network in the mutant by acting on inhibitory interneurons, the inhibitory inputs were pharmacologically removed by application of GABA receptor antagonists to the cortical network, and the effects of CPP under 0 Mg$\sp{2+}$aCSF perfusion in layer V of stg/stg were then compared with those found in +/+ neocortex using in vitro extracellular field recordings. (Abstract shortened by UMI.) ^

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Bayesian network modeling of the consensus between experts: an application to neuron classification

Neuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their three-dimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology.

Differential roles of Ca2+/calmodulin-dependent kinases in posttetanic potentiation at input selective glutamatergic pathways

The electrosensory lateral line lobe (ELL) of the electric fish Apteronotus leptorhynchus is a layered medullary region receiving electroreceptor input that terminates on basal dendrites of interneurons and projection (pyramidal) cells. The molecular layer of the ELL contains two distinct glutamatergic feedback pathways that terminate on the proximal (ventral molecular layer, VML) and distal (dorsal molecular layer) apical dendrites of pyramidal cells. Western blot analysis with an antibody directed against mammalian Ca2+/calmodulin-dependent kinase 2, α subunit (CaMK2α) recognized a protein of identical size in the brain of A. leptorhynchus. Immunohistochemistry demonstrated that CaMK2 α expression in the ELL was restricted to fibers and terminals in the VML. Posttetanic potentiation (PTP) could be readily elicited in pyramidal cells by stimulation of either VML or DML in brain slices of the ELL. PTP in the VML was blocked by extracellular application of a CaMK2 antagonist (KN62) while intracellular application of KN62 or a CaMK2 inhibitory peptide had no effect, consistent with the presynaptic localization of CaMK2 α in VML. PTP in the dorsal molecular layer was not affected by extracellular application of KN62. Anti-Hebbian plasticity has also been demonstrated in the VML, but was not affected by KN62. These results demonstrate that, while PTP can occur independent of CaMK2, it is, in some synapses, dependent on this kinase.

Impaired long-term potentiation induction in dentate gyrus of calretinin-deficient mice

Calretinin (Cr) is a Ca2+ binding protein present in various populations of neurons distributed in the central and peripheral nervous systems. We have generated Cr-deficient (Cr−/−) mice by gene targeting and have investigated the associated phenotype. Cr−/− mice were viable, and a large number of morphological, biochemical, and behavioral parameters were found unaffected. In the normal mouse hippocampus, Cr is expressed in a widely distributed subset of GABAergic interneurons and in hilar mossy cells of the dentate gyrus. Because both types of cells are part of local pathways innervating dentate granule cells and/or pyramidal neurons, we have explored in Cr−/− mice the synaptic transmission between the perforant pathway and granule cells and at the Schaffer commissural input to CA1 pyramidal neurons. Cr−/− mice showed no alteration in basal synaptic transmission, but long-term potentiation (LTP) was impaired in the dentate gyrus. Normal LTP could be restored in the presence of the GABAA receptor antagonist bicuculline, suggesting that in Cr−/− dentate gyrus an excess of γ-aminobutyric acid (GABA) release interferes with LTP induction. Synaptic transmission and LTP were normal in CA1 area, which contains only few Cr-positive GABAergic interneurons. Cr−/− mice performed normally in spatial memory task. These results suggest that expression of Cr contributes to the control of synaptic plasticity in mouse dentate gyrus by indirectly regulating the activity of GABAergic interneurons, and that Cr−/− mice represent a useful tool to understand the role of dentate LTP in learning and memory.

Operative GABAergic inhibition in hippocampal CA1 pyramidal neurons in experimental epilepsy

Patch–clamp recordings of CA1 interneurons and pyramidal cells were performed in hippocampal slices from kainate- or pilocarpine-treated rat models of temporal lobe epilepsy. We report that γ-aminobutyric acid (GABA)ergic inhibition in pyramidal neurons is still functional in temporal lobe epilepsy because: (i) the frequency of spontaneous GABAergic currents is similar to that of control and (ii) focal electrical stimulation of interneurons evokes a hyperpolarization that prevents the generation of action potentials. In paired recordings of interneurons and pyramidal cells, synchronous interictal activities were recorded. Furthermore, large network-driven GABAergic inhibitory postsynaptic currents were present in pyramidal cells during interictal discharges. The duration of these interictal discharges was increased by the GABA type A antagonist bicuculline. We conclude that GABAergic inhibition is still present and functional in these experimental models and that the principal defect of inhibition does not lie in a complete disconnection of GABAergic interneurons from their glutamatergic inputs.

Recurrent excitatory postsynaptic potentials induced by synchronized fast cortical oscillations

Gamma frequency (about 20–70 Hz) oscillations occur during novel sensory stimulation, with tight synchrony over distances of at least 7 mm. Synchronization in the visual system has been proposed to reflect coactivation of different parts of the visual field by a single spatially extended object. We have shown that intracortical mechanisms, including spike doublet firing by interneurons, can account for tight long-range synchrony. Here we show that synchronous gamma oscillations in two sites also can cause long-lasting (>1 hr) potentiation of recurrent excitatory synapses. Synchronous oscillations lasting >400 ms in hippocampal area CA1 are associated with an increase in both excitatory postsynaptic potential (EPSP) amplitude and action potential afterhyperpolarization size. The resulting EPSPs stabilize and synchronize a prolonged beta frequency (about 10–25 Hz) oscillation. The changes in EPSP size are not expressed during non-oscillatory behavior but reappear during subsequent gamma-oscillatory events. We propose that oscillation-induced EPSPs serve as a substrate for memory, whose expression either enhances or blocks synchronization of spatially separated sites. This phenomenon thus provides a dynamical mechanism for storage and retrieval of stimulus-specific neuronal assemblies.

Horizontal cells reveal cone type-specific adaptation in primate retina

The human cone visual system maintains contrast sensitivity over a wide range of ambient illumination, a property known as light adaptation. The first stage in light adaptation is believed to take place at the first neural step in vision, within the long, middle, and short wavelength sensitive cone photoreceptors. To determine the properties of adaptation in primate outer retina, we measured cone signals in second-order interneurons, the horizontal cells, of the macaque monkey. Horizontal cells provide a unique site for studying early adaptational mechanisms; they are but one synapse away from the photoreceptors, and each horizontal cell receives excitatory inputs from many cones. Light adaptation occurred over the entire range of light levels evaluated, a luminance range of 15–1,850 trolands. Adaptation was demonstrated to be independent in each cone type and to be spatially restricted. Thus, in primates, a major source of sensitivity regulation occurs before summation of cone signals in the horizontal cell.

Postnatal mouse subventricular zone neuronal precursors can migrate and differentiate within multiple levels of the developing neuraxis

The mammalian subventricular zone (SVZ) of the lateral wall of the forebrain ventricle retains a population of proliferating neuronal precursors throughout life. Neuronal precursors born in the postnatal and adult SVZ migrate to the olfactory bulb where they differentiate into interneurons. Here we tested the potential of mouse postnatal SVZ precursors in the environment of the embryonic brain: (i) a ubiquitous genetic marker, (ii) a neuron-specific transgene, and (iii) a lipophilic-dye were used to follow the fate of postnatal day 5–10 SVZ cells grafted into embryonic mouse brain ventricles at day 15 of gestation. Graft-derived cells were found at multiple levels of the neuraxis, including septum, thalamus, hypothalamus, and in large numbers in the midbrain inferior colliculus. We observed no integration into the cortex. Neuronal differentiation of graft derived cells was demonstrated by double-staining with neuron-specific β-tubulin antibodies, expression of the neuron-specific transgene, and the dendritic arbors revealed by the lipophilic dye. We conclude that postnatal SVZ cells can migrate through and differentiate into neurons within multiple embryonic brain regions other than the olfactory bulb.

Importance of newly generated neurons in the adult olfactory bulb for odor discrimination

In adult rodents, neurons are continually generated in the subventricular zone of the forebrain, from where they migrate tangentially toward the olfactory bulb, the only known target for these neuronal precursors. Within the main olfactory bulb, they ascend radially into the granule and periglomerular cell layers, where they differentiate mainly into local interneurons. The functional consequences of this permanent generation and integration of new neurons into existing circuits are unknown. To address this question, we used neural cell adhesion molecule-deficient mice that have documented deficits in the migration of olfactory-bulb neuron precursors, leading to about 40% size reduction of this structure. Our anatomical study reveals that this reduction is restricted to the granule cell layer, a structure that contains exclusively γ-aminobutyric acid (GABA)ergic interneurons. Furthermore, mutant mice were subjected to experiments designed to examine the behavioral consequences of such anatomical alteration. We found that the specific reduction in the newly generated interneuron population resulted in an impairment of discrimination between odors. In contrast, both the detection thresholds for odors and short-term olfactory memory were unaltered, demonstrating that a critical number of bulbar granule cells is crucial only for odor discrimination but not for general olfactory functions.