Glial calcium signaling and neuron-glia communication

The existence of bidirectional signaling between astrocytes and neurons has revealed an important active role of astrocytes in the physiology of the nervous system. As a consequence, there is a new concept of the synaptic physiology-"the tripartite synapse", where astrocytes exchange information with the pre- and postsynaptic elements and participate as dynamic regulatory elements in neurotransmission. The control of the Ca2+ excitability in astrocytes is a key element in this loop of information exchange. The ability of astrocytes to respond to neuronal activity and discriminate between the activity of different synapses, the modulation of the astrocytic cellular excitability by the synaptic activity, and the expression of cellular intrinsic properties indicate that astrocytes are endowed with cellular computational characteristics that process synaptic information. Therefore, we propose that astrocytes can be considered as cellular elements involved in the information processing by the nervous system.     

Gap junctions

Electrical coupling through gap junctions constitutes a mode of signal transmission between neurons (electrical synaptic transmission). Originally discovered in invertebrates and in lower vertebrates, electrical synapses have recently been reported in immature and adult mammalian nervous systems. This has renewed the interest in understanding the role of electrical synapses in neural circuit function and signal processing. The present review focuses on the role of gap junctions in shaping the dynamics of neural networks by forming electrical synapses between neurons. Electrical synapses have been shown to be important elements in coincidence detection mechanisms and they can produce complex input-output functions when arranged in combination with chemical synapses. We postulate that these synapses may also be important in redefining neuronal compartments, associating anatomically distinct cellular structures into functional units. The original view of electrical synapses as static connecting elements in neural circuits has been revised and a considerable amount of evidence suggests that electrical synapses substantially affect the dynamics of neural circuits.     

Synaptic relationships in the plexiform layers of carp retina

Summary The synaptic contacts made by carp retinal neurons were studied with electron microscopic techniques. Three kinds of contacts are described: (1) a conventional synapse in which an accumulation of agranular vesicles is found on the presynaptic side along with membrane densification of both pre- and postsynaptic elements; (2) a ribbon synapse in which a presynaptic ribbon surrounded by a halo of agranular vesicles faces two postsynaptic elements; and (3) close apposition of plasma membranes without any vesicle accumulation or membrane densification.In the external plexiform layer, conventional synapses between horizontal cells are described. Horizontal cells possess dense-core vesicles about 1,000 Å in diameter. Membranes of adjacent horizontal cells of the same type (external, intermediate or internal) are found closely apposed over broad regions.In the inner plexiform layer ribbon synapses occur only in bipolar cell terminals. The postsynaptic elements opposite the ribbon may be two amacrine processes or one amacrine process and one ganglion cell dendrite. Amacrine processes make conventional synaptic contacts onto bipolar terminals, other amacrine processes, amacrine cell bodies, ganglion cell dendrites and bodies. Amacrine cells possess dense-core vesicles. Ganglion cells are never presynaptic elements. Serial synapses between amacrine processes and reciprocal synapses between amacrine processes and bipolar terminals are described. The inner plexiform layer contains a large number of myelinated fibers which terminate near the layer of amacrine cells.This work was supported by an N.I.H. grant NB 05404-05 and a Fight for Sight grant G-396 to P.W. and N.I.H. grant NB 05336 to J.E.D. The authors wish to thank Mrs. P. Sheppard and Miss B. Hecker for able technical assistance. P.W. is grateful to Dr. G. K. Smelser, Department of Ophthalmology, Columbia University, for the use of his electron microscope facilities.     

Stochasticity,Bistability and the Wisdom of Crowds: A Model for Associative Learning in Genetic Regulatory Networks

It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory networks are multistable, raising the possibility that even single cells, in the absence of a nervous system, are capable of learning associations. Here we study a standard genetic regulatory network model with bistable elements and stochastic dynamics. We demonstrate that such a genetic regulatory network model is capable of learning multiple, general, overlapping associations. The capacity of the network, defined as the number of associations that can be simultaneously stored and retrieved, is proportional to the square root of the number of bistable elements in the genetic regulatory network. Moreover, we compute the capacity of a clonal population of cells, such as in a colony of bacteria or a tissue, to store associations. We show that even if the cells do not interact, the capacity of the population to store associations substantially exceeds that of a single cell and is proportional to the number of bistable elements. Thus, we show that even single cells are endowed with the computational power to learn associations, a power that is substantially enhanced when these cells form a population.     

Computational methods for ultrastructural analysis of synaptic complexes

Electron microscopy (EM) provided fundamental insights about the ultrastructure of neuronal synapses. The large amount of information present in the contemporary EM datasets precludes a thorough assessment by visual inspection alone, thus requiring computational methods for the analysis of the data. Here, I review image processing software methods ranging from membrane tracing in large volume datasets to high resolution structures of synaptic complexes. Particular attention is payed to molecular level analysis provided by recent cryo-electron microscopy and tomography methods.     

Optimal learning with excitatory and inhibitory synapses

Characterizing the relation between weight structure and input/output statistics is fundamental for understanding the computational capabilities of neural circuits. In this work, I study the problem of storing associations between analog signals in the presence of correlations, using methods from statistical mechanics. I characterize the typical learning performance in terms of the power spectrum of random input and output processes. I show that optimal synaptic weight configurations reach a capacity of 0.5 for any fraction of excitatory to inhibitory weights and have a peculiar synaptic distribution with a finite fraction of silent synapses. I further provide a link between typical learning performance and principal components analysis in single cases. These results may shed light on the synaptic profile of brain circuits, such as cerebellar structures, that are thought to engage in processing time-dependent signals and performing on-line prediction.     

The DrosDel deletion collection: a Drosophila genomewide chromosomal deficiency resource

We describe a second-generation deficiency kit for Drosophila melanogaster composed of molecularly mapped deletions on an isogenic background, covering ~77% of the Release 5.1 genome. Using a previously reported collection of FRT-bearing P-element insertions, we have generated 655 new deletions and verified a set of 209 deletion-bearing fly stocks. In addition to deletions, we demonstrate how the P elements may also be used to generate a set of custom inversions and duplications, particularly useful for balancing difficult regions of the genome carrying haplo-insufficient loci. We describe a simple computational resource that facilitates selection of appropriate elements for generating custom deletions. Finally, we provide a computational resource that facilitates selection of other mapped FRT-bearing elements that, when combined with the DrosDel collection, can theoretically generate over half a million precisely mapped deletions.     

Ecological stoichiometry and the shape of resource-based tradeoffs

Fitness tradeoffs are expected when organisms must allocate a limited resource between two or more traits. Despite the fact that most resources are made up of multiple elements or components, tradeoff models have traditionally been implicitly or explicitly based on a single currency. Here I propose a model for a resource-based tradeoff between two traits that share two resource elements, and suggest ecological stoichiometry as a critical mechanism controlling the shape of tradeoffs. The tradeoff curve in the model has different shapes depending on the elemental stoichiometry of the traits and the available resource. I further describe a conceptual framework that combines a tradeoff curve with a fitness function to predict an optimal combination of traits in stoichiometrically different environments. The model suggests that even though the fitness function is fixed, an optimal combination of traits can vary depending on the stoichiometry of both the traits and the supplied resource.     

Influence of active synaptic pools on the single synaptic event

The activity of the single synapse is the base of information processing and transmission in the brain as well as of important phenomena as the Long Term Potentiation which is the main mechanism for learning and memory. Although usually considered as independent events, the single quantum release gives variable postsynaptic responses which not only depend on the properties of the synapses but can be strongly influenced by the activity of other synapses. In the present paper we show the results of a series of computational experiments where pools of active synapses, in a compatible time window, influence the response of a single synapse of the considered pool. Moreover, our results show that the activity of the pool, by influencing the membrane potential, can be a significant factor in the NMDA unblocking from \(Mg^{2+}\) increasing the contribution of this receptor type to the Excitatory Post Synaptic Current. We consequently suggest that phenomena like the LTP, which depend on NMDA activation, can occur also in subthreshold conditions due to the integration of the dendritic synaptic activity.     

A phenomenological theory of spatially structured local synaptic connectivity

The structure of local synaptic circuits is the key to understanding cortical function and how neuronal functional modules such as cortical columns are formed. The central problem in deciphering cortical microcircuits is the quantification of synaptic connectivity between neuron pairs. I present a theoretical model that accounts for the axon and dendrite morphologies of pre- and postsynaptic cells and provides the average number of synaptic contacts formed between them as a function of their relative locations in three-dimensional space. An important aspect of the current approach is the representation of a complex structure of an axonal/dendritic arbor as a superposition of basic structures—synaptic clouds. Each cloud has three structural parameters that can be directly estimated from two-dimensional drawings of the underlying arbor. Using empirical data available in literature, I applied this theory to three morphologically different types of cell pairs. I found that, within a wide range of cell separations, the theory is in very good agreement with empirical data on (i) axonal–dendritic contacts of pyramidal cells and (ii) somatic synapses formed by the axons of inhibitory interneurons. Since for many types of neurons plane arborization drawings are available from literature, this theory can provide a practical means for quantitatively deriving local synaptic circuits based on the actual observed densities of specific types of neurons and their morphologies. It can also have significant implications for computational models of cortical networks by making it possible to wire up simulated neural networks in a realistic fashion.     

Enzymatic computing

The conformational dynamics of enzymes is a computational resource that fuses milieu signals in a nonlinear fashion. Response surface methodology can be used to elicit computational functionality from enzyme dynamics. We constructed a tabletop prototype to implement enzymatic signal processing in a device context and employed it in conjunction with malate dehydrogenase to perform the linearly inseparable exclusive-or operation. This shows that proteins can execute signal processing operations that are more complex than those performed by individual threshold elements. We view the experiments reported, though restricted to the two-variable case, as a stepping stone to computational networks that utilize the precise reproducibility of proteins, and the concomitant reproducibility of their nonlinear dynamics, to implement complex pattern transformations.     

Diurnal changes in the fine structure of photoreceptors in an abalone,Nordotis discus

Summary The ultrastructure of the synapses in the brain of the monogenean Gastrocotyle trachuri (Platyhelminthes) is described. The synapses consist of one presynaptic terminal separated by a uniformly wide synaptic cleft, from one or more postsynaptic elements. The presynaptic terminals are characterized by the presence of paramembranous dense projections and associated synaptic vesicles. The postsynaptic elements while possessing membrane densities, are usually devoid of vesicles.The structure of the synapses in the brain of Gastrocotyle is compared to synapses from other platyhelminths.     

Aberrant Location of Inhibitory Synaptic Marker Proteins in the Hippocampus of Dystrophin-Deficient Mice: Implications for Cognitive Impairment in Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a neuromuscular disease that arises from mutations in the dystrophin-encoding gene. Apart from muscle pathology, cognitive impairment, primarily of developmental origin, is also a significant component of the disorder. Convergent lines of evidence point to an important role for dystrophin in regulating the molecular machinery of central synapses. The clustering of neurotransmitter receptors at inhibitory synapses, thus impacting on synaptic transmission, is of particular significance. However, less is known about the role of dystrophin in influencing the precise expression patterns of proteins located within the pre- and postsynaptic elements of inhibitory synapses. To this end, we exploited molecular markers of inhibitory synapses, interneurons and dystrophin-deficient mouse models to explore the role of dystrophin in determining the stereotypical patterning of inhibitory connectivity within the cellular networks of the hippocampus CA1 region. In tissue from wild-type (WT) mice, immunoreactivity of neuroligin2 (NL2), an adhesion molecule expressed exclusively in postsynaptic elements of inhibitory synapses, and the vesicular GABA transporter (VGAT), a marker of GABAergic presynaptic elements, were predictably enriched in strata pyramidale and lacunosum moleculare. In acute contrast, NL2 and VGAT immunoreactivity was relatively evenly distributed across all CA1 layers in dystrophin-deficient mice. Similar changes were evident with the cannabinoid receptor 1, vesicular glutamate transporter 3, parvalbumin, somatostatin and the GABA_A receptor alpha1 subunit. The data show that in the absence of dystrophin, there is a rearrangement of the molecular machinery, which underlies the precise spatio-temporal pattern of GABAergic synaptic transmission within the CA1 sub-field of the hippocampus.     

Population rate coding in recurrent neuronal networks with unreliable synapses

Neuron transmits spikes to postsynaptic neurons through synapses. Experimental observations indicated that the communication between neurons is unreliable. However most modelling and computational studies considered deterministic synaptic interaction model. In this paper, we investigate the population rate coding in an all-to-all coupled recurrent neuronal network consisting of both excitatory and inhibitory neurons connected with unreliable synapses. We use a stochastic on-off process to model the unreliable synaptic transmission. We find that synapses with suitable successful transmission probability can enhance the encoding performance in the case of weak noise; while in the case of strong noise, the synaptic interactions reduce the encoding performance. We also show that several important synaptic parameters, such as the excitatory synaptic strength, the relative strength of inhibitory and excitatory synapses, as well as the synaptic time constant, have significant effects on the performance of the population rate coding. Further simulations indicate that the encoding dynamics of our considered network cannot be simply determined by the average amount of received neurotransmitter for each neuron in a time instant. Moreover, we compare our results with those obtained in the corresponding random neuronal networks. Our numerical results demonstrate that the network randomness has the similar qualitative effect as the synaptic unreliability but not completely equivalent in quantity.     

Synaptic Efficacy as a Function of Ionotropic Receptor Distribution: A Computational Study

Glutamatergic synapses are the most prevalent functional elements of information processing in the brain. Changes in pre-synaptic activity and in the function of various post-synaptic elements contribute to generate a large variety of synaptic responses. Previous studies have explored postsynaptic factors responsible for regulating synaptic strength variations, but have given far less importance to synaptic geometry, and more specifically to the subcellular distribution of ionotropic receptors. We analyzed the functional effects resulting from changing the subsynaptic localization of ionotropic receptors by using a hippocampal synaptic computational framework. The present study was performed using the EONS (Elementary Objects of the Nervous System) synaptic modeling platform, which was specifically developed to explore the roles of subsynaptic elements as well as their interactions, and that of synaptic geometry. More specifically, we determined the effects of changing the localization of ionotropic receptors relative to the presynaptic glutamate release site, on synaptic efficacy and its variations following single pulse and paired-pulse stimulation protocols. The results indicate that changes in synaptic geometry do have consequences on synaptic efficacy and its dynamics.     

Synaptic morphology of the carotid body of the domestic fowl

Efferent and reciprocal synapses have been demonstrated in the carotid body of the domestic fowl (Gallus gallus domesticus). Synapses were also found with purely afferent morphology, but were probably components of reciprocal synapses. The general morphology of the endings suggested the presence of two types of axon, afferent axons making reciprocal and perhaps afferent synapses with Type I cells, and efferent axons making efferent synapses with Type I cells. A few axo-dendritic synapses were also found. The dense-cored vesicles associated with the afferent components of reciprocal synapses and with the possible true afferent synapses varied in diameter and core but could belong to one population of pre-synaptic vesicles. These observations are consistent wtih a new theory for the carotid body receptor mechanism. This proposes a spontaneously discharging afferent axon inhibited by an inhibitory transmitter substance released by the Type I cell via the "afferent" component of its reciprocal synapse, the "efferent" component inhibiting this release. Besides this chemoreceptor modulation of its afferent axon, the Type I cell may also have a general secretory function.     

Optimal information storage in noisy synapses under resource constraints

Experimental investigations have revealed that synapses possess interesting and, in some cases, unexpected properties. We propose a theoretical framework that accounts for three of these properties: typical central synapses are noisy, the distribution of synaptic weights among central synapses is wide, and synaptic connectivity between neurons is sparse. We also comment on the possibility that synaptic weights may vary in discrete steps. Our approach is based on maximizing information storage capacity of neural tissue under resource constraints. Based on previous experimental and theoretical work, we use volume as a limited resource and utilize the empirical relationship between volume and synaptic weight. Solutions of our constrained optimization problems are not only consistent with existing experimental measurements but also make nontrivial predictions.