Frequency dependence of signal power and spatial reach of the local field potential

The first recording of electrical potential from brain activity was reported already in 1875, but still the interpretation of the signal is debated. To take full advantage of the new generation of microelectrodes with hundreds or even thousands of electrode contacts, an accurate quantitative link between what is measured and the underlying neural circuit activity is needed. Here we address the question of how the observed frequency dependence of recorded local field potentials (LFPs) should be interpreted. By use of a well-established biophysical modeling scheme, combined with detailed reconstructed neuronal morphologies, we find that correlations in the synaptic inputs onto a population of pyramidal cells may significantly boost the low-frequency components of the generated LFP. We further find that these low-frequency components may be less `local' than the high-frequency LFP components in the sense that (1) the size of signal-generation region of the LFP recorded at an electrode is larger and (2) that the LFP generated by a synaptically activated population spreads further outside the population edge due to volume conduction

Rigid spheres in Riemannian spaces

Choice of an appropriate (3+1)-foliation of spacetime or a (2+1)-foliation of the Cauchy space, leads often to a substantial simplification of various mathematical problems in General Relativity Theory. We propose a new method to construct such foliations. For this purpose we define a special family of topological two-spheres, which we call "rigid spheres". We prove that there is a four-parameter family of rigid spheres in a generic Riemannian three-manifold (in case of the flat Euclidean three-space these four parameters are: 3 coordinates of the center and the radius of the sphere). The rigid spheres can be used as building blocks for various ("spherical", "bispherical" etc.) foliations of the Cauchy space. This way a supertranslation ambiguity may be avoided. Generalization to the full 4D case is discussed. Our results generalize both the Huang foliations (cf. \cite{LHH}) and the foliations used by us (cf. \cite{JKL}) in the analysis of the two-body problem.Comment: 23 page

Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings

The recent development of large multielectrode recording arrays has made it affordable for an increasing number of laboratories to record from multiple brain regions simultaneously. The development of analytical tools for array data, however, lags behind these technological advances in hardware. In this paper, we present a method based on forward modeling for estimating current source density from electrophysiological signals recorded on a two-dimensional grid using multi-electrode rectangular arrays. This new method, which we call two-dimensional inverse Current Source Density (iCSD 2D), is based upon and extends our previous one- and three-dimensional techniques. We test several variants of our method, both on surrogate data generated from a collection of Gaussian sources, and on model data from a population of layer 5 neocortical pyramidal neurons. We also apply the method to experimental data from the rat subiculum. The main advantages of the proposed method are the explicit specification of its assumptions, the possibility to include system-specific information as it becomes available, the ability to estimate CSD at the grid boundaries, and lower reconstruction errors when compared to the traditional approach. These features make iCSD 2D a substantial improvement over the approaches used so far and a powerful new tool for the analysis of multielectrode array data. We also provide a free GUI-based MATLAB toolbox to analyze and visualize our test data as well as user datasets

PyMICE: 0.2.5

PyMICE is a Python® library for mice behavioural data analysis. The library can be used for loading and analysing of data obtained from IntelliCage™ system in an intuitive way in Python programming language. The library provides user with an object oriented application programming interface (API) and a data abstraction layer. It also comes with auxiliary tools supporting development of analysis workflows, like data validators and a tool for workflow configuration. We ask that PyMICE resource identifier (RRID:nlx_158570) is provided in any published research making use of PyMICE. For more details please see the project website: https://neuroinflab.wordpress.com/research/pymice/ . Auhors of the library Jakub Dzik Szymon Łęski Author of the tutorial data Alicja PuścianAcknowledgement: JD and SŁ supported by Symfonia NCN grant: UMO-2013/08/W/NZ4/00691. AP supported by a grant from Switzerland through the Swiss Contribution to the enlarged European Union (PSPB-210/2010 to Ewelina Knapska and Hans-Peter Lipp)

PyMICE: 1.2.0 release

PyMICE is a Python® library for mice behavioural data analysis. The library can be used for loading and analysing of data obtained from IntelliCage™ system in an intuitive way in Python programming language. The library provides user with an object oriented application programming interface (API) and a data abstraction layer. It also comes with auxiliary tools supporting development of analysis workflows, like data validators and a tool for workflow configuration. We ask that that reference to our paper as well as to the library itself is provided in any published research making use of PyMICE. The recommended in-text citation format is: PyMICE (Dzik, Puścian, et al. 2017) v. 1.2.0 (Dzik, Łęski, & Puścian 2017) and the recommended bibliography entry format: Dzik J. M., Łęski S., Puścian A. (July 21, 2017) "PyMICE" computer software (v. 1.2.0; RRID:nlx_158570) doi: 10.5281/zenodo.832982 Dzik J. M., Puścian A., Mijakowska Z., Radwanska K., Łęski S. (June 22, 2017) "PyMICE: A Python library for analysis of IntelliCage data" Behavior Research Methods doi: 10.3758/s13428-017-0907-5 If the journal does not allow for inclusion of the resource identifier (RRID:nlx_158570) in the bibliography, we ask to provide it in-text: PyMICE (RRID:nlx_158570) [1] v. 1.2.0 [2] Dzik JM, Puścian A, Mijakowska Z, Radwanska K, Łęski S. PyMICE: A Python library for analysis of IntelliCage data. Behav Res Methods. 2017. DOI: 10.3758/s13428-017-0907-5 Dzik JM, Łęski S, Puścian A. PyMICE [computer software]. Version 1.2.0. Warsaw: Nencki Institute - PAS; 2017. DOI: 10.5281/zenodo.832982 We have provided a solution to facilitate referencing to the library. Please run: >>> help(pm.Citation) for more information (given that the library is imported as pm). For more details please see the project website: https://neuroinflab.wordpress.com/research/pymice/ . Auhors of the library Jakub Dzik Szymon Łęski Author of the tutorial data Alicja Puścian Changes Citation class provided to facilitate referencing to the library Dependencies provide