Circuit reconstruction tools today

Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, United States.
Current Opinion in Neurobiology (Impact Factor: 6.77). 11/2007; 17(5):601-8. DOI: 10.1016/j.conb.2007.11.004
Source: PubMed

ABSTRACT To understand how a brain processes information, we must understand the structure of its neural circuits-especially circuit interconnection topologies and the cell and synapse molecular architectures that determine circuit-signaling dynamics. Our information on these key aspects of neural circuit structure has remained incomplete and fragmentary, however, because of limitations of the best available imaging methods. Now, new transgenic tool mice and new image acquisition tools appear poised to permit very significant advances in our abilities to reconstruct circuit connection topologies and molecular architectures.

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    • "Connectomics methods based on light microscopy (LM) are well suited for the mesoscopic level (Osten and Margrie, 2013), but can also reveal microscale connectivity through the visualization of intrinsic or genetically encoded synaptic markers. Array Tomography (AT; Micheva and Smith, 2007; Micheva et al., 2010) uses serial application of antibodies directed against endogenous proteins known to localize to vesicles or the PSD. Validated by correlative EM studies, synapse identification can be >80% reliable , which is extremely useful in the context of high-throughput capacity (Rah et al., 2013). "
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    ABSTRACT: The recent development of powerful tools for high-throughput mapping of synaptic networks promises major advances in understanding brain function. One open question is how circuits integrate and store information. Competing models based on random vs. structured connectivity make distinct predictions regarding the dendritic addressing of synaptic inputs. In this article we review recent experimental tests of one of these models, the input clustering hypothesis. Across circuits, brain regions and species, there is growing evidence of a link between synaptic co-activation and dendritic location, although this finding is not universal. The functional implications of input clustering and future challenges are discussed.
    Frontiers in Neural Circuits 09/2014; 8:112. DOI:10.3389/fncir.2014.00112 · 2.95 Impact Factor
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    • "However, data acquisition in this setting remains difficult, as is the analysis of the resulting very large imaging data sets (Burton, 1999; Briggman and Denk, 2006; Smith, 2007; Helmstaedter et al., 2008; Lu et al., 2009; Helmstaedter et al., 2011). The automation of data acquisition, image analysis, and neural process tracking in serial electron microscopy remains an area of active research (Denk and Horstmann, 2004; Smith, 2007; Helmstaedter et al., 2008; Mishchenko, 2009; Lu et al., 2009; Briggman et al., 2011; Bock et al., 2011; Helmstaedter et al., 2011). "
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    ABSTRACT: In recent years, the problem of reconstructing the connectivity in large neural circuits ("connectomics") has re-emerged as one of the main objectives of neuroscience. Classically, reconstructions of neural connectivity have been approached anatomically, using electron or light microscopy and histological tracing methods. This paper describes a statistical approach for connectivity reconstruction that relies on relatively easy-to-obtain measurements using fluorescent probes such as synaptic markers, cytoplasmic dyes, transsynaptic tracers, or activity-dependent dyes. We describe the possible design of these experiments and develop a Bayesian framework for extracting synaptic neural connectivity from such data. We show that the statistical reconstruction problem can be formulated naturally as a tractable L (1)-regularized quadratic optimization. As a concrete example, we consider a realistic hypothetical connectivity reconstruction experiment in C. elegans, a popular neuroscience model where a complete wiring diagram has been previously obtained based on long-term electron microscopy work. We show that the new statistical approach could lead to an orders of magnitude reduction in experimental effort in reconstructing the connectivity in this circuit. We further demonstrate that the spatial heterogeneity and biological variability in the connectivity matrix-not just the "average" connectivity-can also be estimated using the same method.
    Journal of Computational Neuroscience 03/2012; 33(2):371-88. DOI:10.1007/s10827-012-0390-z · 2.09 Impact Factor
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    • "One of the central goals of neuroscience is to understand how the structure of neural circuits underlies the processing of information in the brain, and in recent years a considerable effort has been focused on measuring neural connectivity empirically (Shepherd et al., 2003; Bureau et al., 2004; Briggman and Denk, 2006; Hagmann et al., 2007; Sato et al., 2007; Smith, 2007; Hagmann et al., 2008; Luo et al., 2008; Bohland, J. et al, 2009; Helmstaedter et al., 2009). " Functional " approaches to this neural connectivity problem rely on statistical analysis of neural activity observed with experimental techniques such as multielectrode extracellular recording (Hatsopoulos et al., 1998; Harris et al., 2003; Stein et al., 2004; Paninski, 2004; Truccolo et al., 2005; Santhanam et al., 2006; Luczak et al., 2007; Pillow et al., 2008) or calcium imaging (Tsien, 1989; Cossart et al., 2003; Yuste et al., 2006; Ohki et al., 2005). "
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    ABSTRACT: Monte Carlo approaches have recently been proposed to quantify connectivity in neuronal networks. The key problem is to sample from the conditional distribution of a single neuronal spike train, given the activity of the other neurons in the network. Dependencies between neurons are usually relatively weak; however, temporal dependencies within the spike train of a single neuron are typically strong. In this paper we develop several specialized Metropolis--Hastings samplers which take advantage of this dependency structure. These samplers are based on two ideas: (1) an adaptation of fast forward--backward algorithms from the theory of hidden Markov models to take advantage of the local dependencies inherent in spike trains, and (2) a first-order expansion of the conditional likelihood which allows for efficient exact sampling in the limit of weak coupling between neurons. We also demonstrate that these samplers can effectively incorporate side information, in particular, noisy fluorescence observations in the context of calcium-sensitive imaging experiments. We quantify the efficiency of these samplers in a variety of simulated experiments in which the network parameters are closely matched to data measured in real cortical networks, and also demonstrate the sampler applied to real calcium imaging data.
    The Annals of Applied Statistics 11/2011; 5(2011). DOI:10.1214/11-AOAS467 · 1.69 Impact Factor
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