Visual inspection of neurons suggests that dendritic orientation may be determined both by internal constraints (e.g. membrane tension) and by external vector fields (e.g. neurotrophic gradients). For example, basal dendrites of pyramidal cells appear nicely fan-out. This regular orientation is hard to justify completely with a general tendency to grow straight, given the zigzags observed experimentally. Instead, dendrites could (A) favor a fixed ("external") direction, or (B) repel from their own soma. To investigate these possibilities quantitatively, reconstructed hippocampal cells were subjected to Bayesian analysis. The statistical model combined linearly factors A and B, as well as the tendency to grow straight. For all morphological classes, B was found to be significantly positive and consistently greater than A. In addition, when dendrites were artificially re-oriented according to this model, the resulting structures closely resembled real morphologies. These results suggest that somatodendritic repulsion may play a role in determining dendritic orientation. Since hippocampal cells are very densely packed and their dendritic trees highly overlap, the repulsion must be cellspecific. We discuss possible mechanisms underlying such specificity.

A variety of approaches are available for using computational models to help understand neural processes over many levels of description, from sub-cellular processes to behavior. Alongside purely deductive bottom-up or top-down modeling, a systems design strategy has the advantage of providing a clear goal for the behavior of a complex model. The order in which biological details are added is dictated by functional requirements in terms of the tasks the model should perform.

The function of the human brain arises from computations that occur within and among billions of nerve cells known as neurons. A neuron is composed primarily of a cell body (soma) from which emanates a collection of branching structures (dendrites). How neuronal signals are processed is dependent on the dendrites' specific morphology and distribution of voltage-gated ion channels. To understand this processing, it is necessary to acquire an accurate structural analysis of the cell. Toward this end, we present an automated reconstruction system which extracts the morphology of neurons imaged from confocal and multiphoton microscopes. As we place emphasis on this being a rapid (and therefore automated) process, we have developed several techniques that provide high-quality reconstructions with minimal human interaction. In addition to generating a tree of connected cylinders representing the reconstructed neuron, a computational model is also created for purposes of performing functional simulations. We present visual and statistical results from reconstructions performed both on real image volumes and on noised synthetic data from the Duke-Southampton archive.

# The Author(s) 2008. This article is published with open access at Springerlink.com Abstract Neuronal morphology affects network connectivity, plasticity, and information processing. Uncovering the design principles and functional consequences of dendritic and axonal shape necessitates quantitative analysis and computational modeling of detailed experimental data. Digital reconstructions provide the required neuromorphological descriptions in a parsimonious, comprehensive, and reliable numerical format. NeuroMorpho.Org is the largest webaccessible repository service for digitally reconstructed neurons and one of the integrated resources in the Neuroscience Information Framework (NIF). Here we describe the NeuroMorpho.Org approach as an exemplary experience in designing, creating, populating, and curating a neuroscience digital resource. The simple three-tier architecture of Neuro-Morpho.Org (web client, web server, and relational database) encompasses all necessary elements to support a large-scale, integrate-able repository. The data content, while heterogeneous in scientific scope and experimental origin, is unified in format and presentation by an in house standardization protocol. The server application (MRALD) is secure, customizable, and developer-friendly. Centralized processing and expert annotation yields a comprehensive set of metadata that enriches and complements the raw data. The thoroughly tested interface design allows for optimal and effective data search and retrieval. Availability of data in both original and standardized formats ensures compatibility with existing

It was shown recently that the basal dendrites of cortical pyramidal neurons generate NMDA-spikes. In the present study, we made whole-cell recordings from hippocampal CA1 pyramidal neurons and examined whether NMDA receptor activation was involved in synaptic responses. At low input stimulus intensity, EPSPs with a fast decay time were induced. As the intensity of stimulation was increased in the presence of GABA receptor antagonists, a depolarizing after-potential (DAP) was generated in addition to a fast decaying potential. A DAP was never observed when the input was applied to the apical dendrites. The DAP was suppressed by hyperpolarization or by NMDA receptor antagonists, but not by Na +,K +,orCa 2+ channel blockers. One possible mechanism is that the morphology of the basal dendrites favors DAP generation. A compartmental model simulation showed that synaptic inputs to thinner shorter dendrites generated a potential that resembled a DAP. Our study shows that a synaptic input to the basal dendrites of a hippocampal pyramidal neuron can generate a NMDA receptor-mediated potential in the presence of GABA receptor blockade.

The importance of neuronal morphology has been recognized from the early days of neuroscience. Elucidating the functional roles of axonal and dendritic arbors in synaptic integration, signal transmission, network connectivity, and circuit dynamics requires quantitative analyses of digital three-dimensional reconstructions. We extensively searched the scientific literature for all original reports describing reconstructions of neuronal morphology since the advent of this technique three decades ago. From almost 50,000 titles, 30,000 abstracts, and more than 10,000 full-text articles, we identified 902 publications describing ∼44,000 digital reconstructions. Reviewing the growth of this field exposed general research trends on specific animal species, brain regions, neuron types, and experimental approaches. The entire bibliography, annotated with relevant metadata and (wherever available) direct links to the underlying digital data, is accessible at NeuroMorpho.Org. Keywords: neuron morphology, digital reconstruction, NeuroMorpho.Org, three-dimensional reconstruction, data mining, literature mining, data sharing, potential connectivity