91 research outputs found
The Scalable Brain Atlas: instant web-based access to public brain atlases and related content
The Scalable Brain Atlas (SBA) is a collection of web services that provide
unified access to a large collection of brain atlas templates for different
species. Its main component is an atlas viewer that displays brain atlas data
as a stack of slices in which stereotaxic coordinates and brain regions can be
selected. These are subsequently used to launch web queries to resources that
require coordinates or region names as input. It supports plugins which run
inside the viewer and respond when a new slice, coordinate or region is
selected. It contains 20 atlas templates in six species, and plugins to compute
coordinate transformations, display anatomical connectivity and fiducial
points, and retrieve properties, descriptions, definitions and 3d
reconstructions of brain regions. The ambition of SBA is to provide a unified
representation of all publicly available brain atlases directly in the web
browser, while remaining a responsive and light weight resource that
specializes in atlas comparisons, searches, coordinate transformations and
interactive displays.Comment: Rolf K\"otter sadly passed away on June 9th, 2010. He co-initiated
this project and played a crucial role in the design and quality assurance of
the Scalable Brain Atla
Construction of a multi-scale spiking model of macaque visual cortex
Understanding the relationship between structure and dynamics of the mammalian cortex is a key challenge of neuroscience. So far, it has been tackled in two ways: by modeling neurons or small circuits in great detail, and through large-scale models representing each area with a small number of differential equations. To bridge the gap between these two approaches, we construct a spiking network model extending earlier work on the cortical microcircuit by Potjans & Diesmann (2014) to all 32 areas of the macaque visual cortex in the parcellation of Felleman & Van Essen (1991). The model takes into account spe- cific neuronal densities and laminar thicknesses of the individual areas. The connectivity of the model combines recently updated binary tracing data from the CoCoMac database (Stephan et al., 2001) with quantitative tracing data providing connection densities (Markov et al., 2014a) and laminar connection patterns (Stephan et al., 2001; Markov et al., 2014b). We estimate missing data using structural regular- ities such as the exponential decay of connection densities with distance between areas (Ercsey-Ravasz et al., 2013) and a fit of laminar patterns versus logarithmic ratios of neuron densities. The model integrates a large body of knowledge on the structure of macaque visual cortex into a consistent framework that allows for progressive refinement
The missing link: Predicting connectomes from noisy and partially observed tract tracing data
Our understanding of the wiring map of the brain, known as the connectome, has increased greatly in the last decade, mostly due to technological advancements in neuroimaging techniques and improvements in computational tools to interpret the vast amount of available data. Despite this, with the exception of the C. elegans roundworm, no definitive connectome has been established for any species. In order to obtain this, tracer studies are particularly appealing, as these have proven highly reliable. The downside of tract tracing is that it is costly to perform, and can only be applied ex vivo. In this paper, we suggest that instead of probing all possible connections, hitherto unknown connections may be predicted from the data that is already available. Our approach uses a 'latent space model' that embeds the connectivity in an abstract physical space. Regions that are close in the latent space have a high chance of being connected, while regions far apart are most likely disconnected in the connectome. After learning the latent embedding from the connections that we did observe, the latent space allows us to predict connections that have not been probed previously. We apply the methodology to two connectivity data sets of the macaque, where we demonstrate that the latent space model is successful in predicting unobserved connectivity, outperforming two baselines and an alternative model in nearly all cases. Furthermore, we show how the latent spatial embedding may be used to integrate multimodal observations (i.e. anterograde and retrograde tracers) for the mouse neocortex. Finally, our probabilistic approach enables us to make explicit which connections are easy to predict and which prove difficult, allowing for informed follow-up studies
PENGARUH IMPLEMENTASI METODE STRUKTURAL ANALITIK DAN SINTETIK (SAS) UNTUK MENINGKATKAN KETERAMPILAN MEMBACA DAN MENULIS SISWA PENDIDIKAN ANAK USIA DINI DI PAUD AL-HIKMAH NGEMBEH JOGOROTO
Dalam pengajaran membaca dan menulis kita mengenal bermacam-macam metode diantaranya metode Struktural Analitik dan Sistetik (SAS), metode tersebut siswa dihadapkan dengan beberapa gambar dan membaca beberapa kata/kalimat yang ada di bawah gambar tersebut secara berulang-ulang hingga lancar. Rumusan masalah dalam penelitian ini adalah bagaimana implementasi, keterampilan membaca dan menulis serta pengaruh implementasi metode SAS. Tujuan untuk mengetahui implementasi, keterampilan membaca dan menulis serta pengaruh implementasi metode SAS. Dalam penelitian ini menggunakan metode penelitian kuantitatif. Metode pengumpulan data yang digunakan angket, observasi, wawancara dan dokumentasi. Desain pengukuran menggunakan skala Likert. Analisis data menggunakan rumus prosentase dan regresi linier sederhana. Berdasarkan analisis data penelitian dapat disimpulkan bahwa 1). Implementasi metode SAS menunjukkan angka sebesar 67,8 dan masuk dalam kategori cukup baik. 2). Keterampilan membaca dan menulis siswa menunjukkan bahwa 76,95 dan masuk kategori baik 3). Hasil pengujian hipotesis menunjukkan bahwa ada pengaruh implementasi metode struktural analitik sintetik (SAS) untuk meningkatkan keterampilan membaca dan menulis siswa pendidikan anak usia dini di PAUD Al-Hikmah Ngembeh Jogoroto.
Kata kunci : Metode Struktural Analitik dan Sintetik, keterampilan membaca dan menulis
Bringing Anatomical Information into Neuronal Network Models
For constructing neuronal network models computational neuroscientists have
access to wide-ranging anatomical data that nevertheless tend to cover only a
fraction of the parameters to be determined. Finding and interpreting the most
relevant data, estimating missing values, and combining the data and estimates
from various sources into a coherent whole is a daunting task. With this
chapter we aim to provide guidance to modelers by describing the main types of
anatomical data that may be useful for informing neuronal network models. We
further discuss aspects of the underlying experimental techniques relevant to
the interpretation of the data, list particularly comprehensive data sets, and
describe methods for filling in the gaps in the experimental data. Such methods
of `predictive connectomics' estimate connectivity where the data are lacking
based on statistical relationships with known quantities. It is instructive,
and in certain cases necessary, to use organizational principles that link the
plethora of data within a unifying framework where regularities of brain
structure can be exploited to inform computational models. In addition, we
touch upon the most prominent features of brain organization that are likely to
influence predicted neuronal network dynamics, with a focus on the mammalian
cerebral cortex. Given the still existing need for modelers to navigate a
complex data landscape full of holes and stumbling blocks, it is vital that the
field of neuroanatomy is moving toward increasingly systematic data collection,
representation, and publication
Modelling human choices: MADeM and decisionâmaking
Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15â20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)
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