D-Serine and Glycine Differentially Control Neurotransmission during Visual Cortex Critical Period.

N-methyl-D-aspartate receptors (NMDARs) play a central role in synaptic plasticity. Their activation requires the binding of both glutamate and d-serine or glycine as co-agonist. The prevalence of either co-agonist on NMDA-receptor function differs between brain regions and remains undetermined in the visual cortex (VC) at the critical period of postnatal development.

Input-specific learning rules at excitatory synapses onto hippocampal parvalbumin-expressing interneurons.

Hippocampal parvalbumin-expressing interneurons (PV INs) provide fast and reliable GABAergic signalling to principal cells and orchestrate hippocampal ensemble activities. Precise coordination of principal cell activity by PV INs relies in part on the efficacy of excitatory afferents that recruit them in the hippocampal network. Feed-forward (FF) inputs in particular from Schaffer collaterals influence spike timing precision in CA1 principal cells whereas local feedback (FB) inputs may contribute to pacemaker activities.

Vezatin is essential for dendritic spine morphogenesis and functional synaptic maturation.

Vezatin is an integral membrane protein associated with cell-cell adhesion complex and actin cytoskeleton. It is expressed in the developing and mature mammalian brain, but its neuronal function is unknown. Here, we show that Vezatin localizes in spines in mature mouse hippocampal neurons and codistributes with PSD95, a major scaffolding protein of the excitatory postsynaptic density.

Learning a generative model of images by factoring appearance and shape.

Computer vision has grown tremendously in the past two decades. Despite all efforts, existing attempts at matching parts of the human visual system's extraordinary ability to understand visual scenes lack either scope or power. By combining the advantages of general low-level generative models and powerful layer-based and hierarchical models, this work aims at being a first step toward richer, more flexible models of images.

Visual deprivation suppresses L5 pyramidal neuron excitability by preventing the induction of intrinsic plasticity.

In visual cortex monocular deprivation (MD) during a critical period (CP) reduces the ability of the deprived eye to activate cortex, but the underlying cellular plasticity mechanisms are incompletely understood. Here we show that MD reduces the intrinsic excitability of layer 5 (L5) pyramidal neurons and enhances long-term potentiation of intrinsic excitability (LTP-IE). Further, MD and LTP-IE induce reciprocal changes in K(v)2.

Serotoninergic fine-tuning of the excitation-inhibition balance in rat visual cortical networks.

Fundamental brain functions depend on a balance between excitation (E) and inhibition (I) that is highly adjusted to a 20-80% set point in layer 5 pyramidal neurons (L5PNs) of rat visual cortex. Dysregulations of both the E-I balance and the serotonergic system in neocortical networks lead to serious neuronal diseases including depression, schizophrenia, and epilepsy. However, no link between the activation of neuronal 5-hydroxytryptamine receptors (5-HTRs) and the cortical E-I balance has yet been reported.

[Acquiring new information in a neuronal network: from Hebb's concept to homeostatic plasticity].

Synaptic plasticity is the cellular mechanism underlying the phenomena of learning and memory. Much of the research on synaptic plasticity is based on the postulate of Hebb (1949) who proposed that, when a neuron repeatedly takes part in the activation of another neuron, the efficacy of the connections between these neurons is increased. Plasticity has been extensively studied, and often demonstrated through the processes of LTP (Long Term Potentiation) and LTD (Long Term Depression), which represent an increase and a decrease of the efficacy of long-term synaptic transmission.

Representational power of restricted boltzmann machines and deep belief networks.

Deep belief networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton, Osindero, and Teh (2006) along with a greedy layer-wise unsupervised learning algorithm. The building block of a DBN is a probabilistic model called a restricted Boltzmann machine (RBM), used to represent one layer of the model. Restricted Boltzmann machines are interesting because inference is easy in them and because they have been successfully used as building blocks for training deeper models.

Involvement of NR2A- or NR2B-containing N-methyl-D-aspartate receptors in the potentiation of cortical layer 5 pyramidal neurone inputs depends on the developmental stage.

In the cortex, N-methyl-D-aspartate receptors (NMDARs) play a critical role in the control of synaptic plasticity processes. We have previously shown in rat visual cortex that the application of a high-frequency stimulation (HFS) protocol used to induce long-term potentiation in layer 2/3 leads to a parallel potentiation of excitatory and inhibitory inputs received by cortical layer 5 pyramidal neurones without changing the excitation/inhibition balance of the pyramidal neurone, indicating a homeostatic control of this parameter. We show here that the blockade of NMDARs of the neuronal network prevents the potentiation of excitatory and inhibitory inputs, and this result leaves open to question the role of the NMDAR isoform involved in the induction of long-term potentiation, which is actually being strongly debated.

Homeostatic control of the excitation-inhibition balance in cortical layer 5 pyramidal neurons.

Homeostatic regulation in the brain is thought to be achieved through a control of the synaptic strength by close interactions between excitation and inhibition in cortical circuits. We recorded in a layer 5 pyramidal neuron of rat cortex the composite response to an electrical stimulation of various layers (2-3, 4 or 6). Decomposition of the global conductance change in its excitatory and inhibitory components permits a direct measurement of excitation-inhibition (E-I) balance.

Learning eigenfunctions links spectral embedding and kernel PCA.

In this letter, we show a direct relation between spectral embedding methods and kernel principal components analysis and how both are special cases of a more general learning problem: learning the principal eigenfunctions of an operator defined from a kernel and the unknown data-generating density. Whereas spectral embedding methods provided only coordinates for the training points, the analysis justifies a simple extension to out-of-sample examples (the Nyström formula) for multidimensional scaling (MDS), spectral clustering, Laplacian eigenmaps, locally linear embedding (LLE), and Isomap. The analysis provides, for all such spectral embedding methods, the definition of a loss function, whose empirical average is minimized by the traditional algorithms.