Jaap van Pelt

NETMORPH: A Framework for the Stochastic Generation of Large Scale Neuronal Networks With Realistic Neuron Morphologies

We present a simulation framework, called NETMORPH, for the developmental generation of 3D large-scale neuronal networks with realistic neuron morphologies. In NETMORPH, neuronal morphogenesis is simulated from the perspective of the individual growth cone. For each growth cone in a growing axonal or dendritic tree, its actions of elongation, branching and turning are described in a stochastic, phenomenological manner. In this way, neurons with realistic axonal and dendritic morphologies, including neurite curvature, can be generated. Synapses are formed as neurons grow out and axonal and dendritic branches come in close proximity of each other. NETMORPH is a flexible tool that can be applied to a wide variety of research questions regarding morphology and connectivity. Research applications include studying the complex relationship between neuronal morphology and global patterns of synaptic connectivity. Possible future developments of NETMORPH are discussed.

Dynamics and plasticity in developing neuronal networks in vitro

When dissociated cortical tissue is brought into culture, neurons readily grow out by forming axonal and dendritic arborizations and synaptic connections. These developing neuronal networks in vitro display spontaneous firing activity from about the end of the first week in vitro. When cultured on multielectrode arrays firing activity can be recorded from many neurons simultaneously over long periods of time. These experimental approaches provide valuable data for studying firing dynamics in neuronal networks in relation to an ongoing development of neurons and synaptic connectivity in the network. This chapter summarizes recent findings on the characteristics and developmental changes in the spontaneous firing dynamics. These changes include long-lasting transient periods of increased firing at individual sites on a time scale of days to weeks, and an age-specific repetitive pattern of synchronous network firing (network bursts) on a time scale of seconds. Especially the spatio-temporal organization of firing within network bursts showed great stability over many hours. In addition, a progressive day-to-day evolution was observed, with an initial broadening of the burst firing rate profile during the 3rd week in vitro (WIV) and a pattern of abrupt onset and precise spike timing from the 5th WIV onwards. These developmental changes are discussed in the light of structural changes in the network and activity-dependent plasticity mechanisms. Preliminary findings are presented on the pattern of spike sequences within network burst, as well as the effect of external stimulation on the spatio-temporal organization within network bursts.

Measures for quantifying dendritic arborizations

Topological and metrical measures are reviewed, which describe whole dendritic trees and variables within trees. These measures are applied to differentiate and classify groups of neurons. They are also of importance for simulation or reconstruction of neuronal trees in view of functional computational characteristics.

Tree asymmetry--a sensitive and practical measure for binary topological trees.

The topological structure of a binary tree is characterized by a measure called tree asymmetry, defined as the mean value of the asymmetry of its partitions. The statistical properties of this tree-asymmetry measure have been studied using a growth model for binary trees. The tree-asymmetry measure appears to be sensitive for topological differences and the tree-asymmetry expectation for the growth model that we used appears to be almost independent of the size of the trees. These properties and the simple definition make the measure suitable for practical use, for instance for characterizing, comparing and interpreting sets of branching patterns. Examples are given of the analysis of three sets of neuronal branching patterns. It is shown that the variance in tree-asymmetry values for these observed branching patterns corresponds perfectly with the variance predicted by the used growth model.

The effect of dendritic topology on firing patterns in model neurons

Neuronal firing patterns are influenced by both membrane properties and dendritic morphology. Distinguishing two sources of morphological variability—metrics and topology—we investigate the extent to which model neurons that have the same metrical and membrane properties can still produce different firing patterns as a result of differences in dendritic topology. Within a set of dendritic trees that have the same number of terminal segments and the same total dendritic length, we show that firing frequency strongly correlates with topology as expressed by the mean dendritic path length. The effect of dendritic topology on firing frequency is bigger for trees with equal segment diameters than for trees whose segment diameters obey Ralls 3/2 power law. If active dendritic channels are present, dendritic topology influences not only firing frequency but also type of firing (regular, bursting).

A shape analysis framework for neuromorphometry

This paper addresses in an integrated and systematic fashion the relatively overlooked but increasingly important issue of measuring and characterizing the geometrical properties of nerve cells and structures, an area often called neuromorphology. After discussing the main motivation for such an endeavour, a comprehensive mathematical framework for characterizing neural shapes, capable of expressing variations over time, is presented and used to underline the main issues in neuromorphology. Three particularly powerful and versatile families of neuromorphological approaches, including differential measures, symmetry axes/skeletons, and complexity, are presented and their respective potentials for applications in neuroscience are identified. Examples of applications of such measures are provided based on experimental investigations related to automated dendrogram extraction, mental retardation characterization, and axon growth analysis. ‘…the functional superiority of the human brain is intimately linked up with the prodigious abundance and unaccustomed wealth of forms of the so-called neurons with short axons.’ (Recollections of My Life, Santiago Ramon y Cajal)

NEUROINFORMATICS: THE INTEGRATION OF SHARED DATABASES AND TOOLS TOWARDS INTEGRATIVE NEUROSCIENCE

There is significant interest amongst neuroscientists in sharing neuroscience data and analytical tools. The exchange of neuroscience data and tools between groups affords the opportunity to differently re-analyze previously collected data, encourage new neuroscience interpretations and foster otherwise uninitiated collaborations, and provide a framework for the further development of theoretically based models of brain function. Data sharing will ultimately reduce experimental and analytical error. Many small Internet accessible database initiatives have been developed and specialized analytical software and modeling tools are distributed within different fields of neuroscience. However, in addition large-scale international collaborations are required which involve new mechanisms of coordination and funding. Provided sufficient government support is given to such international initiatives, sharing of neuroscience data and tools can play a pivotal role in human brain research and lead to innovations in neuroscience, informatics and treatment of brain disorders. These innovations will enable application of theoretical modeling techniques to enhance our understanding of the integrative aspects of neuroscience. This article, authored by a multinational working group on neuroinformatics established by the Organization for Economic Co-operation and Development (OECD), articulates some of the challenges and lessons learned to date in efforts to achieve international collaborative neuroscience.

NATURAL VARIABILITY IN THE NUMBER OF DENDRITIC SEGMENTS: MODEL-BASED INFERENCES ABOUT BRANCHING DURING NEURITE OUTGROWTH

A study was made of the possible basis for naturally occurring variations in the number of segments in individual dendritic trees. Distributions of the number of terminal segments have been studied in dendrites from rat, cat, and frog motoneurons, basal dendrites from rat visual cortex pyramidal and non‐pyramidal neurons, in rat cerebellar Purkinje cell dendritic trees, and in human hippocampal dentate granule cells. By means of a mathematical model for dendritic branching, it was shown that the variation in the number of dendritic segments can be accounted for by assuming that new branches during neurite outgrowth are formed randomly at terminal segments. The observed terminal segment number distributions could be closely approximated by additionally assuming that branching probabilities decline with increasing number of terminal segments in growing dendrites. The pyramidal neuron group differed significantly from the other neuron groups in such a way as to suggest that this decline is stronger than in the dendrites of other types of neurons. By using literature data on the mean number of terminal segments in rat cerebellar Purkinje cells, measured at different times during early development, an estimate could be obtained of the time‐course of the branching probabilities. The branching probability of a terminal segment was found to be in the order of 0.002 per hour in the first 4 weeks postnatal with a 5‐fold transient increase in the second week. J. Comp. Neurol. 387:325–340, 1997.

Neuroscience data and tool sharing: a legal and policy framework for neuroinformatics.

The requirements for neuroinformatics to make a significant impact on neuroscience are not simply technical—the hardware, software, and protocols for collaborative research—they also include the legal and policy frameworks within which projects operate. This is not least because the creation of large collaborative scientific databases amplifies the complicated interactions between proprietary, for-profit R&D and public “open science.” In this paper, we draw on experiences from the field of genomics to examine some of the likely consequences of these interactions in neuroscience.Facilitating the widespread sharing of data and tools for neuroscientific research will accelerate the development of neuroinformatics. We propose approaches to overcome the cultural and legal barriers that have slowed these developments to date. We also draw on legal strategies employed by the Free Software community, in suggesting frame-works neuroinformatics might adopt to reinforce the role of public-science databases, and propose a mechanism for identifying and allowing “open science” uses for data whilst still permitting flexible licensing for secondary commercial research.

Descriptive and comparative analysis of geometrical properties of neuronal tree structures.

The morphology of neurons is an important factor for the identification and the study of the changes that occur in the nervous system during development or as a result of disease or an experimental treatment. A number of methods to describe the topological aspects of neuronal morphology is discussed. Furthermore it is illustrated how different groups of neurons can be compared. Although both topological and metrical aspects are considered in the comparative sections emphasis is put on counting instead of measuring. Our intention is to present quick and easy methods that are applicable to camera lucida drawings.