Normalized level set model for segmentation of low-contrast objects in 2- and 3- dimensional images.

Analyses of biomedical images often rely on accurate segmentation of structures of interest. Traditional segmentation methods based on thresholding, watershed, fast marching, and level set perform well in high-contrast images containing structures of similar intensities. However, such methods can under-segment or miss entirely low-intensity objects on noisy backgrounds.

Correction of topological errors in automated traces of neurites.

Our understanding of synaptic connectivity in the brain relies on the ability to accurately trace sparsely labeled neurons from 3D optical microscopy stacks of images. A variety of automated algorithms and software tools have been developed for this task. These algorithms can capture the general layout of neurites with high fidelity, but the resulting traces often contain topological errors such as broken and incorrectly merged branches.

Noise in Neurons and Synapses Enables Reliable Associative Memory Storage in Local Cortical Circuits.

Neural networks in the brain can function reliably despite various sources of errors and noise present at every step of signal transmission. These sources include errors in the presynaptic inputs to the neurons, noise in synaptic transmission, and fluctuations in the neurons' postsynaptic potentials (PSPs). Collectively they lead to errors in the neurons' outputs which are, in turn, injected into the network.

Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits.

The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from associative learning. To test this hypothesis, we trained recurrent networks of excitatory and inhibitory neurons on memories composed of varying numbers of associations and compared the resulting network properties with those observed experimentally.

Artificial neural network filters for enhancing 3D optical microscopy images of neurites.

The ability to extract accurate morphology of labeled neurons from microscopy images is crucial for mapping brain connectivity and for understanding changes in connectivity that underlie learning and neurological disorders. There are, however, two problems, specific to optical microscopy imaging of neurons, which make accurate neuron tracing exceedingly challenging: () neurites can appear broken due to inhomogeneous labeling and () neurites can appear fused in 3D due to limited resolution. Here, we propose and evaluate several artificial neural network (ANN) architectures and conventional image enhancement filters with the aim of alleviating both problems.

Accurate registration of time-lapse images.

imaging experiments often require automated detection and tracking of changes in the specimen. These tasks can be hindered by variations in the position and orientation of the specimen relative to the microscope, as well as by linear and nonlinear tissue deformations. We propose a feature-based registration method, coupled with optimal transformations, designed to address these problems in 3D time-lapse microscopy images.

Computer assisted detection of axonal bouton structural plasticity in time-lapse images.

The ability to measure minute structural changes in neural circuits is essential for long-term in vivo imaging studies. Here, we propose a methodology for detection and measurement of structural changes in axonal boutons imaged with time-lapse two-photon laser scanning microscopy (2PLSM). Correlative 2PLSM and 3D electron microscopy (EM) analysis, performed in mouse barrel cortex, showed that the proposed method has low fractions of false positive/negative bouton detections (2/0 out of 18), and that 2PLSM-based bouton weights are correlated with their volumes measured in EM ( = 0.

Neocortex: a lean mean memory storage machine.

Connectivity patterns of neocortex exhibit several odd properties: for example, most neighboring excitatory neurons do not connect, which seems curiously wasteful. Brunel’s elegant theoretical treatment reveals how optimal information storage can naturally impose these peculiar properties.

Effects of homeostatic constraints on associative memory storage and synaptic connectivity of cortical circuits.

The impact of learning and long-term memory storage on synaptic connectivity is not completely understood. In this study, we examine the effects of associative learning on synaptic connectivity in adult cortical circuits by hypothesizing that these circuits function in a steady-state, in which the memory capacity of a circuit is maximal and learning must be accompanied by forgetting. Steady-state circuits should be characterized by unique connectivity features.

Active learning of neuron morphology for accurate automated tracing of neurites.

Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy.

Efficient associative memory storage in cortical circuits of inhibitory and excitatory neurons.

Many features of synaptic connectivity are ubiquitous among cortical systems. Cortical networks are dominated by excitatory neurons and synapses, are sparsely connected, and function with stereotypically distributed connection weights. We show that these basic structural and functional features of synaptic connectivity arise readily from the requirement of efficient associative memory storage.

Automated Reconstruction of Neural Trees Using Front Re-initialization.

This paper proposes a greedy algorithm for automated reconstruction of neural arbors from light microscopy stacks of images. The algorithm is based on the minimum cost path method. While the minimum cost path, obtained using the Fast Marching Method, results in a trace with the least cumulative cost between the start and the end points, it is not sufficient for the reconstruction of neural trees.

Statistical traces of long-term memories stored in strengths and patterns of synaptic connections.

Learning and long-term memory rely on plasticity of neural circuits. In adult cerebral cortex, plasticity can result from potentiation and depression of synaptic strengths and structural reorganization of circuits through growth and retraction of dendritic spines. By analyzing 166 distributions of spine head volumes and spine lengths from mouse, rat, monkey, and human brains, we determine the "generalized cost" of dendritic spines.

Automated tracing of neurites from light microscopy stacks of images.

Automating the process of neural circuit reconstruction on a large-scale is one of the foremost challenges in the field of neuroscience. In this study we examine the methodology for circuit reconstruction from three-dimensional light microscopy (LM) stacks of images. We show how the minimal error-rate of an ideal reconstruction procedure depends on the density of labeled neurites, giving rise to the fundamental limitation of an LM based approach for neural circuit research.

Cooperative synapse formation in the neocortex.

Neuron morphology plays an important role in defining synaptic connectivity. Clearly, only pairs of neurons with closely positioned axonal and dendritic branches can be synaptically coupled. For excitatory neurons in the cerebral cortex, such axo-dendritic oppositions, termed potential synapses, must be bridged by dendritic spines to form synaptic connections.

Maximization of the connectivity repertoire as a statistical principle governing the shapes of dendritic arbors.

The shapes of dendritic arbors are fascinating and important, yet the principles underlying these complex and diverse structures remain unclear. Here, we analyzed basal dendritic arbors of 2,171 pyramidal neurons sampled from mammalian brains and discovered 3 statistical properties: the dendritic arbor size scales with the total dendritic length, the spatial correlation of dendritic branches within an arbor has a universal functional form, and small parts of an arbor are self-similar. We proposed that these properties result from maximizing the repertoire of possible connectivity patterns between dendrites and surrounding axons while keeping the cost of dendrites low.

The fractions of short- and long-range connections in the visual cortex.

When analyzing synaptic connectivity in a brain tissue slice, it is difficult to discern between synapses made by local neurons and those arising from long-range axonal projections. We analyzed a data set of excitatory neurons and inhibitory basket cells reconstructed from cat primary visual cortex in an attempt to provide a quantitative answer to the question: What fraction of cortical synapses is local, and what fraction is mediated by long-range projections? We found an unexpectedly high proportion of nonlocal synapses. For example, 92% of excitatory synapses near the axis of a 200-microm-diameter iso-orientation column come from neurons located outside the column, and this fraction remains high--76%--even for an 800-micromocular dominance column.

Detection of the optimal neuron traces in confocal microscopy images.

Quantitative methods of analysis of neural circuits rely on large datasets of neurons reconstructed accurately in three dimensions (3D). Due to the complexity of neuronal arbors, large datasets of reconstructed neurons must be generated with automated algorithms. Here, we attempted to automate the process of neuron tracing from sparsely labeled 3D stacks of confocal microscopy images.

Structural plasticity of circuits in cortical neuropil.

Learning and memory formation in the brain depend on the plasticity of neural circuits. In the adult and developing cerebral cortex, this plasticity can result from the formation and elimination of dendritic spines. New synaptic contacts appear in the neuropil where the gaps between axonal and dendritic branches can be bridged by dendritic spines.

Local potential connectivity in cat primary visual cortex.

Time invariant description of synaptic connectivity in cortical circuits may be precluded by the ongoing growth and retraction of dendritic spines accompanied by the formation and elimination of synapses. On the other hand, the spatial arrangement of axonal and dendritic branches appears stable. This suggests that an invariant description of connectivity can be cast in terms of potential synapses, which are locations in the neuropil where an axon branch of one neuron is proximal to a dendritic branch of another neuron.

Neurogeometry and potential synaptic connectivity.

The advent of high-quality 3D reconstructions of neuronal arbors has revived the hope of inferring synaptic connectivity from the geometric shapes of axons and dendrites, or 'neurogeometry'. A quantitative description of connectivity must be built on a sound theoretical framework. Here, we review recent developments in neurogeometry that can provide such a framework.

Geometric and functional organization of cortical circuits.

Can neuronal morphology predict functional synaptic circuits? In the rat barrel cortex, 'barrels' and 'septa' delineate an orderly matrix of cortical columns. Using quantitative laser scanning photostimulation we measured the strength of excitatory projections from layer 4 (L4) and L5A to L2/3 pyramidal cells in barrel- and septum-related columns. From morphological reconstructions of excitatory neurons we computed the geometric circuit predicted by axodendritic overlap.

Class-specific features of neuronal wiring.

Brain function relies on specificity of synaptic connectivity patterns among different classes of neurons. Yet, the substrates of specificity in complex neuropil remain largely unknown. We search for imprints of specificity in the layout of axonal and dendritic arbors from the rat neocortex.