Bundle formation of sperm: Influence of environmental factors.

Cooperative behaviour of sperm is one of the mechanisms that plays a role in sperm competition. It has been observed in several species that spermatozoa interact with each other to form agglomerates or bundles. In this study, we investigate the effect of physical and biochemical factors that will most likely promote bundle formation in bull sperm. These factors include fluid viscosity, swim-up process, post-thaw incubation time and media additives which promote capacitation. While viscosity does not seem to influence the degree of sperm bundling, swim-up, post-thaw migration time and suppressed capacitation increase the occurrence of sperm bundles. This leads to the conclusion that sperm bundling is a result of hydrodynamic and adhesive interactions between the cells which occurs frequently during prolonged incubation times.

Modular Organization of Signal Transmission in Primate Somatosensory Cortex.

Axonal patches are known as the major sites of synaptic connections in the cerebral cortex of higher order mammals. However, the functional role of these patches is highly debated. Patches are formed by populations of nearby neurons in a topographic manner and are recognized as the termination fields of long-distance lateral connections within and between cortical areas. In addition, axons form numerous boutons that lie outside the patches, whose function is also unknown. To better understand the functional roles of these two distinct populations of boutons, we compared individual and collective morphological features of axons within and outside the patches of intra-areal, feedforward, and feedback pathways by way of tract tracing in the somatosensory cortex of New World monkeys. We found that, with the exception of tortuosity, which is an invariant property, bouton spacing and axonal convergence properties differ significantly between axons within patch and no-patch domains. Principal component analyses corroborated the clustering of axons according to patch formation without any additional effect by the type of pathway or laminar distribution. Stepwise logistic regression identified convergence and bouton density as the best predictors of patch formation. These findings support that patches are specific sites of axonal convergence that promote the synchronous activity of neuronal populations. On the other hand, no-patch domains could form a neuroanatomical substrate to diversify the responses of cortical neurons.

Synaptic organization of cortico-cortical communication in primates.

In cortical circuitry, synaptic communication across areas is based on two types of axon terminals, small and large, with modulatory and driving roles, respectively. In contrast, it is not known whether similar synaptic specializations exist for intra-areal projections. Using anterograde tracing and three-dimensional reconstruction by electron microscopy (3D-EM), we asked whether large boutons form synapses in the circuit of somatosensory cortical areas 3b and 1. In contrast to observations in macaque visual cortex, light microscopy showed both small and large boutons not only in inter-areal pathways, but also in long-distance intrinsic connections. 3D-EM showed that correlation of surface and volume provides a powerful tool for classifying cortical endings. Principal component analysis supported this observation and highlighted the significance of the size of mitochondria as a distinguishing feature of bouton type. The larger mitochondrion and higher degree of perforated postsynaptic density associated with large rather than to small boutons support the driver-like function of large boutons. In contrast to bouton size and complexity, the size of the postsynaptic density appeared invariant across the bouton types. Comparative studies in human supported that size is a major distinguishing factor of bouton type in the cerebral cortex. In conclusion, the driver-like function of the large endings could facilitate fast dissemination of tactile information within the intrinsic and inter-areal circuitry of areas 3b and 1.

Causal relationship between local field potential and intrinsic optical signal in epileptiform activity in vitro.

The directed causal relationship were examined between the local field potential (LFP) and the intrinsic optical signal (IOS) during induced epileptiform activity in in vitro cortical slices by the convergent cross-mapping causality analysis method. Two components of the IOS signal have been distinguished: a faster, activity dependent component (IOSh) which changes its sign between transmitted and reflected measurement, thus it is related to the reflectance or the scattering of the tissue and a slower component (IOSl), which is negative in both cases, thus it is resulted by the increase of the absorption of the tissue. We have found a strong, unidirectional, delayed causal effect from LFP to IOSh with 0.5-1s delay, without signs of feedback from the IOSh to the LFP, while the correlation was small and the peaks of the cross correlation function did not reflect the actual causal dependency. Based on these observations, a model has been set up to describe the dependency of the IOSh on the LFP power and IOSh was reconstructed, based on the LFP signal. This study demonstrates that causality analysis can lead to better understanding the physiological interactions, even in case of two data series with drastically different time scales.

Multi-scale modeling of altered synaptic plasticity related to Amyloid ß effects.

As suggested by Palop and Mucke (2010) pathologically elevated ß-amyloid (Aß) impairs long term potentiation (LTP) and enhances long term depression (LTD) possible underlying mechanisms in Alzheimer's Disease (AD). In the present paper we adopt and further elaborate a phenomenological computational model of bidirectional plasticity based on the calcium control hypothesis of Shouval et al. (2002). First, to account for Aß effects the activation function Ω was modified assuming competition between LTP and LTD, and parameter sets were identified that well describe both normal and pathological synaptic plasticity processes. Second, a biophysically plausible kinetic model of bidirectional synaptic plasticity by D'Alcantara et al. (2003) was used to support findings of the phenomenological model and to further explain underlying kinetic processes. Model fitting pointed out molecular contributors, particularly calcineurin and type 1 protein phosphatase that might contribute to observed physiological disturbances in AD.

Impaired associative learning in schizophrenia: behavioral and computational studies.

Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto-hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits. We present a preliminary computational model designed to capture learning dynamics in healthy control and schizophrenia subjects. Experimental data was collected on a spatial-object paired-associate learning task. The task evinces classic patterns of negatively accelerated learning in both healthy control subjects and patients, with patients demonstrating lower rates of learning than controls. Our rudimentary computational model of the task was based on biologically plausible assumptions, including the separation of dorsal/spatial and ventral/object visual streams, implementation of rules of learning, the explicit parameterization of learning rates (a plausible surrogate for synaptic plasticity), and learning capacity (a plausible surrogate for memory capacity). Reductions in learning dynamics in schizophrenia were well-modeled by reductions in learning rate and learning capacity. The synergy between experimental research and a detailed computational model of performance provides a framework within which to infer plausible biological bases of impaired learning dynamics in schizophrenia.

Convergence and divergence are mostly reciprocated properties of the connections in the network of cortical areas.

Cognition is based on the integrated functioning of hierarchically organized cortical processing streams in a manner yet to be clarified. Because integration fundamentally depends on convergence and the complementary notion of divergence of the neuronal connections, we analysed integration by measuring the degree of convergence/divergence through the connections in the network of cortical areas. By introducing a new index, we explored the complementary convergent and divergent nature of connectional reciprocity and delineated the backward and forward cortical sub-networks for the first time. Integrative properties of the areas defined by the degree of convergence/divergence through their afferents and efferents exhibited distinctive characteristics at different levels of the cortical hierarchy. Areas previously identified as hubs exhibit information bottleneck properties. Cortical networks largely deviate from random graphs where convergence and divergence are balanced at low reciprocity level. In the cortex, which is dominated by reciprocal connections, balance appears only by further increasing the number of reciprocal connections. The results point to the decisive role of the optimal number and placement of reciprocal connections in large-scale cortical integration. Our findings also facilitate understanding of the functional interactions between the cortical areas and the information flow or its equivalents in highly recurrent natural and artificial networks.

Model-based source localization of extracellular action potentials.

A new model-based analysis method was set up for revealing information encrypted in extracellular spatial potential patterns of neocortical action potentials. Spikes were measured by extracellular linear multiple microelectrode in vivo cat's primary auditory cortex and were analyzed based on current source density (CSD) distribution models. Validity of the monopole and other point source approximations were tested on the measured potential patterns by numerical fitting. We have found, that point source models could not provide accurate description of the measured patterns. We introduced a new model of the CSD distribution on a spiking cell, called counter-current model (CCM). This new model was shown to provide better description of the spatial current distribution of the cell during the initial negative deflection of the extracellular action potential, from the onset of the spike to the negative peak. The new model was tested on simulated extracellular potentials. We proved numerically, that all the parameters of the model could be determined accurately based on measurements. Thus, fitting of the CCM allowed extraction of these parameters from the measurements. Due to model fitting, CSD could be calculated with much higher accuracy as done with the traditional method because distance dependence of the spatial potential patterns was explicitly taken into consideration in our method. Average CSD distribution of the neocortical action potentials was calculated and spatial decay constant of the dendritic trees was determined by applying our new method.

Properties of a random attachment growing network.

In this study we introduce and analyze the statistical structural properties of a model of growing networks which may be relevant to social networks. At each step a new node is added which selects k possible partners from the existing network and joins them with probability delta by undirected edges. The "activity" of the node ends here; it will get new partners only if it is selected by a newcomer. The model produces an infinite-order phase transition when a giant component appears at a specific value of delta, which depends on k. The average component size is discontinuous at the transition. In contrast, the network behaves significantly different for k=1. There is no giant component formed for any delta and thus in this sense there is no phase transition. However, the average component size diverges for delta> or =1/2.