Exploration of a collection of documents in neuroscience and extraction of topics by clustering.

This paper presents a preliminary analysis of the neuroscience knowledge domain, and an application of cluster analysis to identify topics in neuroscience. A collection of posters presented at the Society for Neuroscience (SfN) Annual Meeting in 2006 is first explored by viewing existing topics and poster sessions using multidimensional scaling. Based on the Vector Space Model, several Term Spaces were built on the basis of a set of terms extracted from the posters' abstracts and titles, and a set of free keywords assigned to the posters by their authors. The ensuing Term Spaces were compared from the point of view of retrieving the genuine category titles. Topics were extracted from the abstracts of posters by clustering the documents using a bisecting k-means algorithm and selecting the most salient terms for each cluster by ranking. The terms extracted as topic descriptors were evaluated by comparing them to existing titles assigned to thematic categories defined by human experts in neuroscience. A comparison of two approaches for terms ranking (Document Frequency and Log-Entropy) resulted in better performance of the Log-Entropy scores, allowing to retrieve 31.0% of original title terms in clustered documents (and 37.1% in original thematic categories).

Computational theory and applications of a filling-in process at the blind spot.

A mathematical model for filling-in at the blind spot is proposed. The general scheme of the standard regularization theory was used to derive the model deductively. First, we present the problems encountered with a diffusion equation, which is frequently used for various types of perceptual completion. To solve these problems, we investigated the computational meaning of a neural property discovered by Matsumoto and Komatsu [Matsumoto, M., & Komatsu, H. (2005). Neural responses in the macaque V1 to bar stimuli with various lengths presented on the blind spot. Journal of Neurophysiology, 93, 2374-2387]. Based on our observations, we introduce two types of curvature information of image properties into the a priori knowledge of missing images in the blind spot. Moreover, two different information pathways for filling-in, which were suggested by results of physiological experiments (slow conductive paths of horizontal connections in V1, and fast feedforward/feedback paths via V2), were considered theoretically as the neural embodiment of an adiabatic approximation between V1 and V2 interaction. Numerical simulations show that the output of the proposed model for filling-in is consistent with neurophysiological experimental results. The model can be used as a powerful tool for digital image inpainting.

Customizable neuroinformatics database system: XooNIps and its application to the pupil platform.

The developing field of neuroinformatics includes technologies for the collection and sharing of neuro-related digital resources. These resources will be of increasing value for understanding the brain. Developing a database system to integrate these disparate resources is necessary to make full use of these resources. This study proposes a base database system termed XooNIps that utilizes the content management system called XOOPS. XooNIps is designed for developing databases in different research fields through customization of the option menu. In a XooNIps-based database, digital resources are stored according to their respective categories, e.g., research articles, experimental data, mathematical models, stimulations, each associated with their related metadata. Several types of user authorization are supported for secure operations. In addition to the directory and keyword searches within a certain database, XooNIps searches simultaneously across other XooNIps-based databases on the Internet. Reviewing systems for user registration and for data submission are incorporated to impose quality control. Furthermore, XOOPS modules containing news, forums schedules, blogs and other information can be combined to enhance XooNIps functionality. These features provide better scalability, extensibility, and customizability to the general neuroinformatics community. The application of this system to data, models, and other information related to human pupils is described here.

Prostaglandins E1 and E2, but not F2alpha or latanoprost, inhibit monkey ciliary muscle contraction.

PURPOSE: To investigate the effects of prostaglandin (PG) E1, E2, F2alpha, and latanoprost acid on the electrically evoked contractile response of isolated rhesus monkey ciliary muscle. METHODS: Longitudinal ciliary muscle preparations from rhesus monkeys were mounted in an organ bath, and tension changes were recorded by an isometric transducer. Electrical field stimulation (100 Hz, 0.3 ms, 10 V) was applied through a pair of platinum plate electrodes. RESULTS: The ciliary muscle produced atropine-sensitive excitatory contraction in response to field stimulation. PGE1 and PGE2 (1 microM) attenuated the contraction to levels that were 68% and 65.1%, respectively, of the normal amplitude. However, PGF2alpha and latanoprost acid (1 microM) did not significantly change the response amplitude. CONCLUSIONS: Our results indicate that PGF2alpha and latanoprost acid do not interact with the prostanoid receptor involved at the pre- and/or postsynaptic site. Therefore, it is unlikely that the hypotensive action by these agents is due to relaxation of the ciliary muscle.

Mutation-based genetic neural network.

Evolving gradient-learning artificial neural networks (ANNs) using an evolutionary algorithm (EA) is a popular approach to address the local optima and design problems of ANN. The typical approach is to combine the strength of backpropagation (BP) in weight learning and EA's capability of searching the architecture space. However, the BP's "gradient descent" approach requires a highly computer-intensive operation that relatively restricts the search coverage of EA by compelling it to use a small population size. To address this problem, we utilized mutation-based genetic neural network (MGNN) to replace BP by using the mutation strategy of local adaptation of evolutionary programming (EP) to effect weight learning. The MGNN's mutation enables the network to dynamically evolve its structure and adapt its weights at the same time. Moreover, MGNN's EP-based encoding scheme allows for a flexible and less restricted formulation of the fitness function and makes fitness computation fast and efficient. This makes it feasible to use larger population sizes and allows MGNN to have a relatively wide search coverage of the architecture space. MGNN implements a stopping criterion where overfitness occurrences are monitored through "sliding-windows" to avoid premature learning and overlearning. Statistical analysis of its performance to some well-known classification problems demonstrate its good generalization capability. It also reveals that locally adapting or scheduling the strategy parameters embedded in each individual network may provide a proper balance between the local and global searching capabilities of MGNN.

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 frameworks 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.

Mechanical properties of the rabbit iris smooth muscles.

The study focuses on obtaining the visco-elastic properties of the iris sphincter and dilator muscles. Two kinds of experiments were performed: the isometric contraction experiment and the isotonic quick release experiment. The length-tension relationship was obtained from the former experiment. This relationship clarified the contribution of each muscle in determining the statics of the pupil. The viscous and serial elastic properties were obtained from the latter experiment. The viscosity could be expressed by the expanded Hill's equation as a function of velocity and contractile tension. We argue that serial elasticity is independent of contractile tension. These properties provide insights into the pupillary mechanism.

A simulation analysis on mechanisms of damped oscillation in retinal rod photoreceptor cells.

The different actions of two I(h) channel blockers, zatebradine (UL-FS 49) and ZD7288, on rod photoresponses were analysed by computer simulation using a newly revised ionic current model of the rod photoreceptor, based on Hodgkin-Huxley equations. The model, adjusted to fit the experimental results of amphibian rods, shows that both of the blockers enhance the light-induced membrane hyperpolarization. Our model can also predict a mechanism of a damped oscillation arising during the recovery phase appeared only in the presence of zatebradine which, unlike ZD7288, reduces both I(h) and I(Kv). We suggest that the oscillation can appear due to the alternative activation of voltage-dependent Ca(2+) current (I(Ca)) and calcium-dependent current (I(K(Ca)) and I(Cl(Ca))) when I(Kv) is blocked, with I(K(Ca)) having a stronger effect than I(Cl(Ca)).

Visiome: neuroinformatics research in vision project.

The coordinated efforts within Japan's Neuroinformatics Research in Vision (NRV) program to build a neuroinformatics portal for vision science in Japan, the 'Visiome' (Vision+Ome) Platform is presented. We introduce the general concepts underlying of the NRV Project and an example of a specific neuroinformatics study on the vertebrate retina, as developed in Visiome.

Neuroinformatics Research for Vision Science: NRV project.

The NRV project (Neuroinformatics Research for Vision Science) is the first project in Japan started in 1999 under the Science and Technology Agency of Japan, aimed at building the foundation of neuroinformatics research. Because of the wealth of data on the visual system, the NRV project will use vision research to promote experimental, theoretical and technical research as a pilot study on neuroinformatics. Details can be found at: http://www.neuroinformatics.gr.jp/.

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.

Proton feedback mediates the cascade of color-opponent signals onto H3 horizontal cells in goldfish retina.

It has been postulated that horizontal cells (HCs) send feedback signals onto cones via a proton feedback mechanism, which generates the center-surround receptive field of bipolar cells, and color-opponent signals in many non-mammalian vertebrates. Here we used a strong pH buffer, HEPES, to reduce extracellular proton concentration changes and so determine whether protons mediate color-opponent signals in goldfish H3 (triphasic) HCs. Superfusion with 10mM HEPES-fortified saline elicited depolarization of H3 HCs' dark membrane potential and enhanced hyperpolarizing responses to blue stimuli, but suppressed both depolarization by yellow and orange and hyperpolarization by red stimuli. The response components suppressed by HEPES resembled the inverse of spectral responses of H2 (biphasic) HCs. These results are consistent with the Stell-Lightfoot cascade model, in which the HEPES-suppressed component of H3 HCs was calculated using light responses recorded experimentally in H1 (monophasic) and H2 HCs. Selective suppression of long- or long-+middle-wavelength cone signals by long-wavelength background enhanced the responses to short-wavelength stimuli. These results suggest that HEPES inhibited color opponent signals in H3 HCs, in which the source of opponent-color signals is primarily a feedback from H2 HCs and partly from H1 HCs onto short-wavelength cones, probably mediated by protons.

A model-based theory on the signal transformation for microsaccade generation.

The eyes are continuously fluctuating even during fixation. The fluctuations are called miniature eye movements and consist of microsaccades, drifts, and tremors. It has been revealed that these miniature eye movements aid our vision; they improve the visibility of high spatial frequency components, and prevent retinal adaptation during fixation. Although the functional roles of the miniature eye movements have gradually been uncovered, their generation mechanism remains a mystery. Here, we focused on microsaccades, and constructed a neuronal network model to explore their generation mechanism. Several lines of evidence ensure that microsaccades and saccades share the same neuronal circuitry because they fall on the same main sequence, a relationship between their amplitudes and peak velocities. In the saccade pathway, saccade commands generated in the superior colliculus are relayed to motoneurons via burst neurons (BNs) and the integrator network. The BNs are inhibited by omnipause neurons (OPNs) except when saccades are generated. We configured a model for microsaccades based on the well-defined saccade neuronal pathway including tonic neurons, BNs, OPNs, the integrator network, and the eye plant. The model successfully reproduced various characteristics of microsaccade: square-wave jerk, single-sided microsaccades, and the main sequence. Moreover, during microsaccades, BNs showed low-rate spikes due to a partial release from the OPN inhibition. These results suggest that microsaccades are generated when BNs are partially, but not completely, released from tonic inhibition by OPNs during fixation, in contrast to the generation of ordinary saccades in which OPNs pause firing and release BNs from their strong inhibition.

PLATO: data-oriented approach to collaborative large-scale brain system modeling.

The brain is a complex information processing system, which can be divided into sub-systems, such as the sensory organs, functional areas in the cortex, and motor control systems. In this sense, most of the mathematical models developed in the field of neuroscience have mainly targeted a specific sub-system. In order to understand the details of the brain as a whole, such sub-system models need to be integrated toward the development of a neurophysiologically plausible large-scale system model. In the present work, we propose a model integration library where models can be connected by means of a common data format. Here, the common data format should be portable so that models written in any programming language, computer architecture, and operating system can be connected. Moreover, the library should be simple so that models can be adapted to use the common data format without requiring any detailed knowledge on its use. Using this library, we have successfully connected existing models reproducing certain features of the visual system, toward the development of a large-scale visual system model. This library will enable users to reuse and integrate existing and newly developed models toward the development and simulation of a large-scale brain system model. The resulting model can also be executed on high performance computers using Message Passing Interface (MPI).

Simulation Platform: a cloud-based online simulation environment.

For multi-scale and multi-modal neural modeling, it is needed to handle multiple neural models described at different levels seamlessly. Database technology will become more important for these studies, specifically for downloading and handling the neural models seamlessly and effortlessly. To date, conventional neuroinformatics databases have solely been designed to archive model files, but the databases should provide a chance for users to validate the models before downloading them. In this paper, we report our on-going project to develop a cloud-based web service for online simulation called "Simulation Platform". Simulation Platform is a cloud of virtual machines running GNU/Linux. On a virtual machine, various software including developer tools such as compilers and libraries, popular neural simulators such as GENESIS, NEURON and NEST, and scientific software such as Gnuplot, R and Octave, are pre-installed. When a user posts a request, a virtual machine is assigned to the user, and the simulation starts on that machine. The user remotely accesses to the machine through a web browser and carries out the simulation, without the need to install any software but a web browser on the user's own computer. Therefore, Simulation Platform is expected to eliminate impediments to handle multiple neural models that require multiple software.

Reprint of: Simulation Platform: a cloud-based online simulation environment.

For multi-scale and multi-modal neural modeling, it is needed to handle multiple neural models described at different levels seamlessly. Database technology will become more important for these studies, specifically for downloading and handling the neural models seamlessly and effortlessly. To date, conventional neuroinformatics databases have solely been designed to archive model files, but the databases should provide a chance for users to validate the models before downloading them. In this paper, we report our on-going project to develop a cloud-based web service for online simulation called "Simulation Platform". Simulation Platform is a cloud of virtual machines running GNU/Linux. On a virtual machine, various software including developer tools such as compilers and libraries, popular neural simulators such as GENESIS, NEURON and NEST, and scientific software such as Gnuplot, R and Octave, are pre-installed. When a user posts a request, a virtual machine is assigned to the user, and the simulation starts on that machine. The user remotely accesses to the machine through a web browser and carries out the simulation, without the need to install any software but a web browser on the user's own computer. Therefore, Simulation Platform is expected to eliminate impediments to handle multiple neural models that require multiple software.

Enlarged gold-tipped silicon microprobe arrays and signal compensation for multi-site electroretinogram recordings in the isolated carp retina.

In order to record multi-site electroretinogram (ERG) responses in isolated carp retinae, we utilized three-dimensional (3D), extracellular, 3.5-µm-diameter silicon (Si) probe arrays fabricated by the selective vapor-liquid-solid (VLS) growth method. Neural recordings with the Si microprobe exhibit low signal-to-noise (S/N) ratios of recorded responses due to the high-electrical-impedance characteristics of the small recording area at the probe tip. To increase the S/N ratio, we designed and fabricated enlarged gold (Au) tipped Si microprobes (10-µm-diameter Au tip and 3.5-µm-diameter probe body). In addition, we demonstrated that the signal attenuation and phase delay of ERG responses recorded via the Si probe can be compensated by the inverse filtering method. We conclude that the reduction of probe impedance and the compensation of recorded signals are useful approaches to obtain distortion-free recording of neural signals with high-impedance microelectrodes.

Electrical interfacing between neurons and electronics via vertically integrated sub-4 microm-diameter silicon probe arrays fabricated by vapor-liquid-solid growth.

We report here a technique for use in electrical interfaces between neurons and microelectronics, using vertically integrated silicon probe arrays with diameters of 2-3.5 microm and lengths of 60-120 microm. Silicon probe arrays can be fabricated by selective vapor-liquid-solid (VLS) growth. A doped n-type silicon probe with the resistance of 1 k Omega has an electrical impedance of less than 10 M Omega in physiological saline. After inserting the probe arrays into the retina of a carp (Cyrpinus carpio), we conducted electrical recording of neural signals, using the probes to measure light-evoked electrical neural signals. We determined that recorded signals represented local field potentials of the retina (electroretinogram (ERG)). The VLS-probe can provide minimally invasive neural recording/stimulation capabilities at high spatial resolution for fundamental studies of nervous systems. In addition, the probe arrays can be integrated with microelectronics; therefore, these probes make it possible to construct interfaces between neurons and microelectronics in advanced neuroscience applications.