The role of receptor/channel activity in neuronal cell migration

Confocal laser microscopy, in conjunction with carbocyanine dyes and calcium-sensitive fluorescent indicators, was used in slices and explant cultures of developing cerebellum to study cellular mechanisms underlying a motility of neuronal cell migration. The results indicate that a combination of voltage- and ligand-activated ion channels cooperatively regulates Ca²⁺ influx into the migrating cells. We suggest that molecules, present in the local cellular milieu, affect cell motility by activating specific ion channels and second messengers that influence polymerization of stiff and contractile cytoskeletal proteins. This early interaction between postmitotic neurons and surrounding cells controls the rate of their movements, sculpts their shapes, establishes their positions, and, therefore, indirectly determines their identities to prior formation of synaptic connections. © 1995 John Wiley Sons, Inc.     

The stochastic properties of input spike trains control neuronal arithmetic

In the nervous system, the representation of signals is based predominantly on the rate and timing of neuronal discharges. In most everyday tasks, the brain has to carry out a variety of mathematical operations on the discharge patterns. Recent findings show that even single neurons are capable of performing basic arithmetic on the sequences of spikes. However, the interaction of the two spike trains, and thus the resulting arithmetic operation may be influenced by the stochastic properties of the interacting spike trains. If we represent the individual discharges as events of a random point process, then an arithmetical operation is given by the interaction of two point processes. Employing a probabilistic model based on detection of coincidence of random events and complementary computer simulations, we show that the point process statistics control the arithmetical operation being performed and, particularly, that it is possible to switch from subtraction to division solely by changing the distribution of the inter-event intervals of the processes. Consequences of the model for evaluation of binaural information in the auditory brainstem are demonstrated. The results accentuate the importance of the stochastic properties of neuronal discharge patterns for information processing in the brain; further studies related to neuronal arithmetic should therefore consider the statistics of the interacting spike trains.     

A comparative statistical study of hippocampal neuronal spontaneous spike activity in situ and in vitro

The statistical characteristics of the spontaneous spike activity of rat hippocampal neurons in fields CA_1?2 were compared in situ and in tissue culture. Statistical analyses have shown strong similarities in estimators of basic numerical characteristics of interspike interval (ISI) distributions. These similarities may serve as evidence of maintenance of normal functional properties and an “organotypic arrangement” of neurons in tissue culture, and they are also indicative of an intrahippocampal origin of the spontaneous impulse activity in the hippocampus. On the other hand, some differences are noted in the tests of firing patterns. Interpretation of these results leads to some assumptions about mechanisms of the phenomenon under study.     

Modeling the process of rate selection in neuronal activity

We present the elements of a mathematical computational model that reflects the experimental finding that the time-scale of a neuron is not fixed; but rather varies with the history of its stimulus. Unlike most physiological models, there are no pre-determined rates associated with transitions between states of the system nor are there pre-determined constants associated with adaptation rates; instead, the model is a kind of "modulating automata" where the rates emerge from the history of the system itself. We focus in this paper on the temporal dynamics of a neuron and show how a simple internal structure will give rise to complex temporal behavior. The internal structure modeled here is an abstraction of a reasonably well-understood physiological structure. We also suggest that this behavior can be used to transform a "rate" code into a "temporal one".     

Alias-free sampling of neuronal spike trains

Summary Spectral analysis provides powerful techniques for describing the lower order moments of a stochastic process and interactions between two or more stochastic processes. A major problem in the application of spectral analysis to neuronal spike trains is how to obtain equispaced samples of the spike trains which will give unbiased and alias-free spectral estimates. Various sampling methods, which treat the spike train as a continuous signal, a point process and as a series of Dirac delta-functions, are reviewed and their limitations discussed. A new sampling technique, which gives unbiased and alias-free estimates, is described. This technique treats the spike train as a series of delta functions and generates samples by digital filtering. Implementation of this technique on a small computer is simple and virtually on-line.     

Recurrence plots of neuronal spike trains

The recently developed qualitative method of diagnosis of dynamical systems — recurrence plots has been applied to the analysis of dynamics of neuronal spike trains recorded from cerebellum and red nucleus of anesthetized cats. Recurrence plots revealed robust and common changes in the similarity structure of interspike interval sequences as well as significant deviations from randomness in serial ordering of intervals. Recurring episodes of alike, quasi-deterministic firing patterns suggest the spontaneous modulation of the dynamical complexity of the trajectories of observed neurons. These modulations are associated with changing dynamical properties of a neuronal spike-train-generating system. Their existence is compatible with the information processing paradigm of attractor neural networks.     

Social learning in birds and its role in shaping a foraging niche

We briefly review the literature on social learning in birds, concluding that strong evidence exists mainly for predator recognition, song, mate choice and foraging. The mechanism of local enhancement may be more important than imitation for birds learning to forage, but the former mechanism may be sufficient for faithful transmission depending on the ecological circumstances. To date, most insights have been gained from birds in captivity. We present a study of social learning of foraging in two passerine birds in the wild, where we cross-fostered eggs between nests of blue tits, Cyanistes caeruleus and great tits, Parus major. Early learning causes a shift in the foraging sites used by the tits in the direction of the foster species. The shift in foraging niches was consistent across seasons, as showed by an analysis of prey items, and the effect lasted for life. The fact that young birds learn from their foster parents, and use this experience later when subsequently feeding their own offspring, suggests that foraging behaviour can be culturally transmitted over generations in the wild. It may therefore have both ecological and evolutionary consequences, some of which are discussed.     

Geometrical size of the neuronal network plays a key role in synchronization of its activity

Using computer simulation of electrical processes in a GABA-ergic interneuronal network, we found that synchronization of electrical discharges of the neurons is critically dependent on the geometrical size of the virtual network. If the size, under our simulation conditions, was either less than 2.1 mm or greater than 4.2 mm, the network was not able to generate synchronous discharges. Our findings may explain the geometrical size-dependent gamma rhythm generated by neurons of the hippocampal networks. Neirofiziologiya/Neurophysiology, Vol. 40, No. 3, pp. 264–267, May–June, 2008.     

Elimination of response latency variability in neuronal spike trains

 Neuronal activity in the mammalian cortex exhibits a considerable amount of trial-by-trial variability. This may be reflected by the magnitude of the activity as well as by the response latency with respect to an external event, such as the onset of a sensory stimulus, or a behavioral event. Here we present a novel nonparametric method for estimating trial-by-trial differences in response latency from neuronal spike trains. The method makes use of the dynamic rate profile for each single trial and maximizes their total pairwise correlation by appropriately shifting all trials in time. The result is a new alignment of trials that largely eliminates the variability in response latency and provides a new internal trigger that is independent of experiment time. To calibrate the method, we simulated spike trains based on stochastic point processes using a parametric model for phasic response profiles. We illustrate the method by an application to simultaneous recordings from a pair of neurons in the motor cortex of a behaving monkey. It is demonstrated how the method can be used to study the temporal relation of the neuronal response to the experiment, to investigate whether neurons share the same dynamics, and to improve spike correlation analysis. Differences between this and other previously published methods are discussed. Received: 8 April 2002 / Accepted: 26 November 2002 / Published online: 7 April 2003 Correspondence to: Stefan Rotter (e-mail: rotter@biologie.uni-freiburg.de), Tel.: +49-761-2032862, Fax: +49-761-2032860 Acknowledgements. We are grateful to Alexa Riehle for providing us with the monkey data and for valuable discussions. We also thank Felix Kümmell, Hiroyuki Nakahara, and Shun-ichi Amari for helpful discussions. Partial funding was received by the Deutsche Forschungsgemeinschaft (DFG, SFB 505) and the German-Israeli Foundation (GIF). Additional support was provided by the RIKEN Brain Science Institute.     

Combined disturbances and the role of their spatial and temporal properties in shaping community structure

Disturbances are characteristic for many ecosystems. However, we still lack generalizations concerning their role in shaping communities, particularly when disturbances co-occur. To study such effects, we used a novel modeling approach that is unrestricted by a priori tradeoffs among specific plant traits, except for those generated by allocation principles. Thus, trait combinations were emergent properties associated with biotic and abiotic constraints. Specifically, we asked which traits dominate under specific disturbance regimes, whether single and combined disturbance regimes promote similar trait tradeoffs and how complex disturbance regimes affect species richness and functional diversity. Overall, disturbances’ temporal properties governed the outcome of combined disturbances and were a stronger assortative force than spatial disturbance properties: low temporal predictability decreased seed-dispersability and dormancy, but increased competitive ability and disturbance tolerance. Evidence for tradeoffs between different colonization modes and between dormancy and disturbance tolerance were found, while surprisingly, the widely accepted colonization–competition tradeoff was not generated. Diversity was highest at intermediate disturbance intensity, but decreased monotonically with increasing unpredictability. In accordance with our results, future models should avoid restrictive assumptions about tradeoffs to generate robust and more general predictions about the role of disturbances for community dynamics.     

Modeling the role of myosin 1c in neuronal growth cone turning

We addressed the mechanical basis for how embryonic chick dorsal root ganglion growth cones turn on a uniform substrate of laminin-1. Turning is significantly correlated with lamellipodial area but not with filopodial length. We assessed the lamellipodial contribution to turning by asymmetric micro-CALI of myosin isoforms that causes localized lamellipodial expansion (myosin 1c) or filopodial retraction (myosin V). Episodes of asymmetric micro-CALI of myosin 1c (or myosin 1c and V together) caused significant turning of the growth cone. In contrast, repeated micro-CALI of myosin V or irradiation without added antibody did not turn growth cones. These findings argue that lamellipodia and not filopodia are necessary for growth cone turning. To model the role of myosin 1c on growth cone turning, we fitted the measured trajectories from asymmetric micro-CALI of myosin 1c-treated and untreated growth cones to the persistent random walk model. The first parameter in this equation, root-mean-square speed, is indistinguishable between the two data sets whereas the second parameter, the persistence of motion, is significantly increased (2.5-fold) as a result of asymmetric inactivation of myosin 1c by micro-CALI. This analysis demonstrates that growth cone turning results from an increase in the persistence of directional motion rather than a change in speed. Taken together, our results suggest that myosin 1c is a molecular correlate for directional persistence underlying growth cone motility.     

Reflection of the plastic properties of the simplest neuronal associations in statistical characteristics of the spike activity of their elements. Polysynaptically linked neurons

Changes of crosscorrelation histograms of trains of action potentials and mean interspike intervals of polysynaptically connected neurones were studied by means of mathematical modelling of synaptic neuronal interaction at changes of efficiency of interneuronal monosynaptic connections, at changes of neuronal excitability, and at changes of total action on them of independent disorderly afferent synaptic inflows. Increase of amplitude of the main maximum (minimum) of the normalized crosscorrelation histogram of trains of action potentials accompanied by reduction of mean interspike intervals of both neurones, was shown to be a unsignificant indication of an increase of efficiency of polysynaptic excitatory (inhibitory) connections between the neurones (due to modification of synapses or to a change of the functional state of interneurones).     

Regulation and role of cyclin-dependent kinase activity in neuronal survival and death

Cyclin-dependent kinase (Cdk)5 is a proline-directed Ser/Thr protein kinase that functions mainly in neurons and is activated by binding to a regulatory subunit, p35 or p39. Kinase activity is mainly determined by the amount of p35 available, which is controlled by a balance between synthesis and degradation. Kinase activity is also regulated by Cdk5 phosphorylation, but the activity of phosphorylated Cdk5 is in contrast to that of cycling Cdks. Cdk5 is a versatile protein kinase that regulates multiple neuronal activities including neuronal migration and synaptic signaling. Further, Cdk5 plays a role in both survival and death of neurons. Long-term inactivation of Cdk5 triggers cell death, and the survival activity of Cdk5 is apparent when neurons suffer from stress. In contrast, hyper-activation of Cdk5 by p25 promotes cell death, probably by reactivating cell-cycle machinery in the nucleus. The pro-death activity is suppressed by membrane association of Cdk5 via myristoylation of p35. Appropriate activity, localization, and regulation of Cdk5 may be critical for long-term survival of neurons, which is more than 80 years in the case of humans.     

Structure of spike activity of cold thermoreceptors at its various levels

In acute experiments on anesthetized rabbits we investigated the spike activity of cold fibers of the infraorbital nerve during a steady decrease in skin temperature from 39 to 7°C at a rate of 0.8 ± 0.05°/min. Analysis of interspike intervals (ISI) in the firing of receptors demonstrated that in the investigated range of skin temperatures the ISI histograms changed significantly several times, reflecting a shift in the pattern of firing. In addition, the reactions of each cold thermoreceptor had individual aspects, which lays the foundation for discussion of the perception of various characteristics of the temperature stimulus of the set of thermoreceptors.I. P. Pavlov Physiology Institute, Russian Academy of Sciences, Saint Petersburg. Translated from Neirofiziologiya, Vol. 24, No. 5, pp. 559–566, September–October, 1992.     

Modeling K(ATP) channel gating and its regulation

ATP-sensitive potassium (K_ATP) channels couple cell metabolism to plasmalemmal potassium fluxes in a variety of cell types. The activity of these channels is primarily determined by intracellular adenosine nucleotides, which have both inhibitory and stimulatory effects. The role of K_ATP channels has been studied most extensively in pancreatic beta-cells, where they link glucose metabolism to insulin secretion. Many mutations in K_ATP channel subunits (Kir6.2, SUR1) have been identified that cause either neonatal diabetes or congenital hyperinsulinism. Thus, a mechanistic understanding of K_ATP channel behavior is necessary for modeling beta-cell electrical activity and insulin release in both health and disease. Here, we review recent advances in the K_ATP channel structure and function. We focus on the molecular mechanisms of K_ATP channel gating by adenosine nucleotides, phospholipids and sulphonylureas and consider the advantages and limitations of various mathematical models of macroscopic and single-channel K_ATP currents. Finally, we outline future directions for the development of more realistic models of K_ATP channel gating.