A cortical attractor network with Martinotti cells driven by facilitating synapses

The population of pyramidal cells significantly outnumbers the inhibitory interneurons in the neocortex, while at the same time the diversity of interneuron types is much more pronounced. One acknowledged key role of inhibition is to control the rate and patterning of pyramidal cell firing via negative feedback, but most likely the diversity of inhibitory pathways is matched by a corresponding diversity of functional roles. An important distinguishing feature of cortical interneurons is the variability of the short-term plasticity properties of synapses received from pyramidal cells. The Martinotti cell type has recently come under scrutiny due to the distinctly facilitating nature of the synapses they receive from pyramidal cells. This distinguishes these neurons from basket cells and other inhibitory interneurons typically targeted by depressing synapses. A key aspect of the work reported here has been to pinpoint the role of this variability. We first set out to reproduce quantitatively based on in vitro data the di-synaptic inhibitory microcircuit connecting two pyramidal cells via one or a few Martinotti cells. In a second step, we embedded this microcircuit in a previously developed attractor memory network model of neocortical layers 2/3. This model network demonstrated that basket cells with their characteristic depressing synapses are the first to discharge when the network enters an attractor state and that Martinotti cells respond with a delay, thereby shifting the excitation-inhibition balance and acting to terminate the attractor state. A parameter sensitivity analysis suggested that Martinotti cells might, in fact, play a dominant role in setting the attractor dwell time and thus cortical speed of processing, with cellular adaptation and synaptic depression having a less prominent role than previously thought.     

Learning flexible sensori-motor mappings in a complex network

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem.     

A neural circuit model forming semantic network with exception using spike-timing-dependent plasticity of inhibitory synapses

We propose a neural circuit model forming a semantic network with exceptions using the spike-timing-dependent plasticity (STDP) of inhibitory synapses. To evaluate the proposed model, we conducted nine types of computer simulation by combining the three STDP rules for inhibitory synapses and the three spike pairing rules. The simulation results obtained with the STDP rule for inhibitory synapses by Haas et al. [Haas, J.S., Nowotny, T., Abarbanel, H.D.I., 2006, Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. J. Neurophysiol. 96, 3305–3313] are successful, whereas, the other results are unsuccessful. The results and examinations suggested that the inhibitory connection from the concept linked with an exceptional feature to the general feature is necessary for forming a semantic network with an exception.     

Technologies competing with biomethanation

Summary Biomethanation is an appropriate renewable energy technology because (1) it utilizes three of the energy resources for new and renewable energy technologies (residue, waste and crop energy), (2) it allows decentralized production units, and (3) it minimally affects the country's imports bill. Moreover methane is one of the energy forms that allows the greatest number of possible end uses and therefore the competitiveness of biomethanation appears to be the best even though the efficiency for its conversion of biomass to energy is only 30 to 40%.

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Resumen La biometaniación es una tecnología de obtención de energía renovable adecuada ya que (1) usa tres fuentes de energía de las utilizables en tecnologías energéticas, ya sean nuevas o renovables: residuos, desechos y energía vegetal (2) permite la descentralización de las unidades de producción (3) afecta en menor grado la balanza de importaciones. Hay que tener en cuenta que además el metano es uno de los productos energéticos con más multiplicidades de uso, de forma que la biometanación aparece como la tecnología más adecuada aún cuando su eficiencia en la conversión de biomasa a energía sea de 30–40%.

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Résumé La biométhanisation est une technologie appropriée de production d'une énergie renouvelable. En effet elle utilise trois des ressources énergétiques disponibles pour ce type de technologie, elle permet l'utilisation d'unités de production décentralisées et c'est elle qui affecte le moins la facture d'importation du pays concerné. De plus le gaz méthane produit est une des formes d'énergie qui permet le plus grand nombre possible d'utilisations. C'est pourquoi la compétitivité de la biométhanisation semble la meilleure même si le rendement de sa conversion de la biomasse en énergie est seulement de 30 à 40%.

     

Neuronal network with modifiable synapses: decoding of composite sensory messages under unsupervised and permanent learning

Afferent sensory fibers carry usually messages which combine several primitive entities. A model of neural structure under unsupervised and permanent learning is proposed, which can detect the primary independent entities mixed in the afferent message. The model is a network of neurons mutually interconnected by means of inhibitory modifiable synapses with efficacies locally controlled by a specific conjunction law of pre- and post-synaptic activities.     

A neural network model of the inferior colliculus with modifiable lateral inhibitory synapses for human echolocation

 We propose a neural network model of the inferior colliculus (IC) for human echolocation. Neuronal mechanisms for human echolocation were investigated by simulating the model. The model consists of the neural networks of the central nucleus (ICc) and external nucleus (ICx) of the inferior colliculus. The neurons of the ICc receive interaural sound stimuli via multiple contralateral delay lines and a single ipsilateral delay line. The neurons of the ICc send output signals to the neurons of the ICx in a convergent manner. We stimulated the ICc with pairs of a direct sound (a sonar sound) and an echo sound (the reflection from an object). Information about the distance between the model and the object is expressed by the delay time of the echo sound with respect to the direct sound. The results presented here show that neurons of the ICc responsive to interaural onset time differences contribute to the creation of an auditory distance map in the ICx. We trained the model with various pairs of direct-echo sounds and modified synaptic connection strengths of the networks according to the Hebbian rule. It is shown that self-organized long-term depression of lateral inhibitory synaptic connections plays an important role in enhancing echolocation skills. Received: 26 November 2000 / Accepted in revised form: 16 October 2001     

Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses

Identification of complex molecular networks underlying common human phenotypes is a major challenge of modern genetics. In this study, we develop a method for network-based analysis of genetic associations (NETBAG). We use NETBAG to identify a large biological network of genes affected by rare de novo CNVs in autism. The genes forming the network are primarily related to synapse development, axon targeting, and neuron motility. The identified network is strongly related to genes previously implicated in autism and intellectual disability phenotypes. Our results are also consistent with the hypothesis that significantly stronger functional perturbations are required to trigger the autistic phenotype in females compared to males. Overall, the presented analysis of de novo variants supports the hypothesis that perturbed synaptogenesis is at the heart of autism. More generally, our study provides proof of the principle that networks underlying complex human phenotypes can be identified by a network-based functional analysis of rare genetic variants.     

Interrelated modification of excitatory and inhibitory synapses in three-layer olivary-cerebellar neural network

The model of three-layer olivary-cerebellar neural network with modifiable excitatory and inhibitory connections between diverse elements is suggested. The same Hebbian modification rules are proposed for Purkinje cells, granule (input) cells, and deep cerebellar nuclei (output) cells. The inverse calcium-dependent modification rules for these cells and hippocampal/neocortical neurones or Golgi cells are conceivably the result of the involvement of cGMP and cAMP in postsynaptic processes. The sign of simultaneous modification of excitatory and inhibitory inputs to a cell is opposite and determined by the variations in pre- and/or postsynaptic cell activity. Modification of excitatory transmission between parallel fibers and Purkinje cells, mossy fibers and granule cells, and mossy fibers and deep cerebellar nuclei cells essentially depends on inhibition effected by stellate/basket cells, Golgi cells and Purkinje cells, respectively. The character of interrelated modifications of diverse synapses in all three layers of the network is influenced by olivary cell activity. In the absence (presence) of a signal from inferior olive, the long-term potentiation (depression) in the efficacy of a synapse between input mossy fiber and output cell can be induced. The results of the suggested model are in accordance with known experimental data.     

Oscillatory coexistence in a food chain model with competing predators

A food chain model with two predators feeding on a single prey in a chemostat is studied. Using a multiparameter bifurcation analysis, we find parameters values for which there is stable oscillatory coexistence of the predators. It is also shown how these coexistent states provide a transition between two possible states of competitive exclusion. It is shown that the competitive exclusion principle need not hold if one or more of the predators has oscillatory behavior in the absence of other predators.This work was partially supported by National Science Foundation Grant MCS 83-01881     

Quantitative proteomics and protein network analysis of hippocampal synapses of CaMKIIalpha mutant mice

Quantitative analysis of synaptic proteomes from specific brain regions is important for our understanding of the molecular basis of neuroplasticity and brain disorders. In the present study we have optimized comparative synaptic proteome analysis to quantitate proteins of the synaptic membrane fraction isolated from the hippocampus of wild type mice and 3'UTR-calcium/calmodulin-dependent kinase II alpha mutant mice. Synaptic proteins were solubilized in 0.85% RapiGest and digested with trypsin without prior dilution of the detergent, and the peptides from two groups of wild type mice and two groups of CaMKIIalpha 3'UTR mutants were tagged with iTRAQ reagents 114, 115, 116, and 117, respectively. The experiment was repeated once with independent biological replicates. Peptides were fractionated with tandem liquid chromatography and collected off-line onto MALDI metal plates. The first iTRAQ experiment was analyzed on an ABI 4700 proteomics analyzer, and the second experiment was analyzed on an ABI 4800 proteomics analyzer. Using the criteria that the proteins should be matched with at least three peptides with the highest CI% of a peptide at least 95%, 623 and 259 proteins were quantified by a 4800 proteomics analyzer and a 4700 proteomics analyzer, respectively, from which 249 proteins overlapped in the two experiments. There was a 3 fold decrease of calcium/calmodulin-dependent kinase II alpha in the synaptic membrane fraction of the 3'UTR mutant mice. No other major changes were observed, suggesting that the synapse protein constituents of the mutant mice were not substantially altered. A first draft of a synaptic protein interaction network has been constructed using commercial available software, and the synaptic proteins were organized into 10 (interconnecting) functional groups belonging to the pre- and postsynaptic compartments, e.g., receptors and ion channels, scaffolding proteins, cytoskeletal proteins, signaling proteins, adhesion molecules, and proteins of synaptic vesicles and those involved in membrane recycling.     

Tolloid gets Sizzled competing with Chordin

The enigma of Sizzled, a secreted Frizzled-related protein, has been resolved in a recent study from the De Robertis lab ( [in the January 13 issue of Cell]). Sizzled, although homologous to other Wnt antagonists, does not function as such, nor does it function within a Wnt signaling pathway. Remarkably it functions as an antagonist of BMP signaling, competing with Chordin for binding to its inhibitor a Tolloid-related metalloprotease. This competition protects Chordin from cleavage, thus allowing it to bind and limit BMP signaling.     

Coexistence of competing predators in a chemostat

An analysis is given of a mathematical model of two predators feeding on a single prey growing in the chemostat. In the case that one of the predators goes extinct, a global stability result is obtained. Under appropriate circumstances, a bifurcation theorem can be used to show that coexistence of the predators occurs in the form of a limit cycle.     

Learning of oscillatory correlated patterns in a cortical network by a STDP-based learning rule

In this paper, we propose an iterative learning rule that allows the imprinting of correlated oscillatory patterns in a model of the hippocampus able to work as an associative memory for oscillatory spatio-temporal patterns. We analyze the dynamics in the Fourier domain, showing how the network selectively amplify or distort the Fourier components of the input, in a manner which depends on the imprinted patterns. We also prove that the proposed iterative local rule converges to the pseudo-inverse rule generalized to oscillatory patterns.     

Interaction of competing fungi with fly larvae

Saprophytic fungi have degradative abilities and interspecific interactions which suggest that resource use and yield should increase as species number increases, but previous studies show the opposite. As a test of the possibility that invertebrate activity changes fungal resource use patterns, we grew coprophilous fungi on rabbit feces at the same initial density singly or in mixtures of 2, 4, or 6 species, with or without activity of larvalLycoriella mali (Diptera: Sciaridae). Fungi in mixtures without larvae caused less weight loss in one mixture, and greater weight loss in 2 mixtures than when growing alone; fungi in 4 of 6 mixtures produced fewer spores than when growing alone. Overall, without larvae, weight loss did not increase as number of fungal species increased. Larvae did not change the pattern of weight loss or proportions of spores caused by mixing fungal species. Numbers of larvae surviving to pupate rose as fungal species numbers increased; as a result, weight loss increased with fungal species number in cultures with larvae.     

Feedback network controls photoreceptor output at the layer of first visual synapses in Drosophila

At the layer of first visual synapses, information from photoreceptors is processed and transmitted towards the brain. In fly compound eye, output from photoreceptors (R1-R6) that share the same visual field is pooled and transmitted via histaminergic synapses to two classes of interneuron, large monopolar cells (LMCs) and amacrine cells (ACs). The interneurons also feed back to photoreceptor terminals via numerous ligand-gated synapses, yet the significance of these connections has remained a mystery. We investigated the role of feedback synapses by comparing intracellular responses of photoreceptors and LMCs in wild-type Drosophila and in synaptic mutants, to light and current pulses and to naturalistic light stimuli. The recordings were further subjected to rigorous statistical and information-theoretical analysis. We show that the feedback synapses form a negative feedback loop that controls the speed and amplitude of photoreceptor responses and hence the quality of the transmitted signals. These results highlight the benefits of feedback synapses for neural information processing, and suggest that similar coding strategies could be used in other nervous systems.     

Dissecting tripartite synapses with STED microscopy

The concept of the tripartite synapse reflects the important role that astrocytic processes are thought to play in the function and regulation of neuronal synapses in the mammalian nervous system. However, many basic aspects regarding the dynamic interplay between pre- and postsynaptic neuronal structures and their astrocytic partners remain to be explored. A major experimental hurdle has been the small physical size of the relevant glial and synaptic structures, leaving them largely out of reach for conventional light microscopic approaches such as confocal and two-photon microscopy. Hence, most of what we know about the organization of the tripartite synapse is based on electron microscopy, which does not lend itself to investigating dynamic events and which cannot be carried out in parallel with functional assays. The development and application of superresolution microscopy for neuron–glia research is opening up exciting experimental opportunities in this regard. In this paper, we provide a basic explanation of the theory and operation of stimulated emission depletion (STED) microscopy, outlining the potential of this recent superresolution imaging modality for advancing our understanding of the morpho-functional interactions between astrocytes and neurons that regulate synaptic physiology.     

Fast-axon synapses of a crab leg muscle

Neuromuscular synapses of the "fast" excitatory axon supplying the main extensor muscle in the leg of the shore crab Pachygrapsus crassipes were studied with electrophysiological and electron-microscopic techniques. Electrical recording showed that many muscle fibers of the central region of the extensor muscle responded only to stimulation of the fast axon, and electron microscopy revealed many unitary subterminal axon branches. Maintained stimulation, even at a low frequency, resulted in depression of the excitatory junctional potentials (EJPs) set up by the fast axon but EJPs of different muscle fibers depressed at different rates, indicating some physiological heterogeneity among the fast-axon synapses. Focal recording at individual synaptic sites on the surfaces of the muscle fibers showed quantal contents ranging from 1.4 to 5.5 at different synapses; these values are relatively high in comparison with similar determinations made in the crayfish opener muscle. Synapse-bearing nerve terminals were generally relatively small in diameter and filiform, with many individual synaptic contact areas of uniform size averaging 0.6 micron2. All of the individual synapses had a presynaptic "dense body" at which synaptic vesicles clustered. If these structures represent release points for transmitter quanta, the initial high quantal content would have an ultrastructural basis. The mitochondial content of the nerve terminals, the synaptic vesicle population, and the specialized subsynaptic sarcoplasm were all much reduced in comparison with tonic axon synaptic regions in this and other crustaceans. The latter features may be correlated with the relatively infrequent use of this axon by the animal, and with rapid fatigue.