Aug
12
Mon
2013
Invited Talk: Can we compute what we think? @ Amriteshwari Hall
Aug 12 @ 10:20 am – 10:51 am

gauteGaute Einevoll, Ph.D.
Professor of Physics, Department of Mathematical Sciences & Technology, Norwegian University of Life Sciences (UMB)


Multiscale modeling of cortical network activity: Key challenges

Gaute T. Einevoll Computational Neuroscience Group, Norwegian University of Life Sciences, 1432 Ås, Norway; Norwegian National Node of the International Neuroinformatics Coordinating Facility (INCF)

Several challenges must be met in the development of multiscale models of cortical network activity. In the presentation I will, based on ongoing work in our group (http://compneuro.umb.no/ ) on multiscale modeling of cortical columns, outline some of them. In particular,

  1. Combined modeling schemes for neuronal, glial and vascular dynamics [1,2],
  2. Development of sets of interconnected models describing system at different levels of biophysical detail [3-5],
  3. Multimodal modeling, i.e., how to model what you can measure [6-12],
  4. How to model when you don’t know all the parameters, and
  5. Development of neuroinformatics tools and routines to do simulations efficiently and accurately [13,14].

References:

  1. L. Øyehaug, I. Østby, C. Lloyd, S.W. Omholt, and G.T. Einevoll: Dependence of spontaneous neuronal firing and depolarisation block on astroglial membrane transport mechanisms, J Comput Neurosci 32, 147-165 (2012)
  2. I. Østby, L. Øyehaug, G.T. Einevoll, E. Nagelhus, E. Plahte, T. Zeuthen, C. Lloyd, O.P. Ottersen, and S.W. Omholt: Astrocytic mechanisms explaining neural-activity-induced shrinkage of extraneuronal space, PLoS Comp Biol 5, e1000272 (2009)
  3. T. Heiberg, B. Kriener, T. Tetzlaff, A. Casti, G.T. Einevoll, and H.E. Plesser: Firing-rate models can describe the dynamics of the retina-LGN connection, J Comput Neurosci (2013)
  4. T. Tetzlaff, M. Helias, G.T. Einevoll, and M. Diesmann: Decorrelation of neural-network activity by inhibitory feedback, PLoS Comp Biol 8, e10002596 (2012).
  5. E. Nordlie, T. Tetzlaff, and G.T. Einevoll: Rate dynamics of leaky integrate-and-fire neurons with strong synapses, Frontiers in Comput Neurosci 4, 149 (2010)
  6. G.T. Einevoll, F. Franke, E. Hagen, C. Pouzat, K.D. Harris: Towards reliable spike-train recording from thousands of neurons with multielectrodes, Current Opinion in Neurobiology 22, 11-17 (2012)
  7. H. Linden, T Tetzlaff, TC Potjans, KH Pettersen, S Grun, M Diesmann, GT Einevoll: Modeling the spatial reach of LFP, Neuron 72, 859-872 (2011).
  8. H. Linden, K.H. Pettersen, and G.T. Einevoll: Intrinsic dendritic filtering gives low-pass power spectra of local field potentials, J Computational Neurosci 29, 423-444 (2010)
  9. K.H. Pettersen and G.T. Einevoll: Amplitude variability and extracellular low-pass filtering of neuronal spikes, Biophysical Journal 94, 784-802 (2008).
  10. K.H. Pettersen, E. Hagen, and G.T. Einevoll: Estimation of population firing rates and current source densities from laminar electrode recordings, J Comput Neurosci 24, 291-313 (2008).
  11. K. Pettersen, A. Devor, I. Ulbert, A.M. Dale and G.T. Einevoll. Current-source density estimation based on inversion of electrostatic forward solution: Effects of finite extent of neuronal activity and conductivity discontinuities, Journal of Neuroscience Methods 154, 116-133 (2006).
  12. G.T. Einevoll, K. Pettersen, A. Devor, I. Ulbert, E. Halgren and A.M. Dale: Laminar Population Analysis: Estimating firing rates and evoked synaptic activity from multielectrode recordings in rat barrel cortex, Journal of Neurophysiology 97, 2174-2190 (2007).
  13. LFPy: A tool for simulation of extracellular potentials (http://compneuro.umb.no)
  14. E. Nordlie, M.-O. Gewaltig, H. E. Plesser: Towards reproducible descriptions of neuronal network models, PLoS Comp Biol 5, e1000456 (2009).

Gaute

Aug
13
Tue
2013
Invited Talk: Spatially Distributed and Hierarchical Nanomaterials in Biotechnology @ Amriteshwari Hall
Aug 13 @ 9:30 am – 10:03 am

ShantiShantikumar Nair, Ph.D.
Professor & Director, Amrita Center for Nanosciences & Molecular Medicine, Amrita University, India


 

Spatially Distributed and Hierarchical Nanomaterials in Biotechnology 

Although nano materials are well investigated in biotechnology in their zero-, one- and two-dimensional forms, three-dimensional nanomaterials are relatively less investigated for their biological applications.  Three dimensional nano materials are much more complex with several structural and hierarchical variables controlling their mechanical, chemical and biological functionality.  In this talk examples are given of some complex three dimensional systems including,  scaffolds, aggregates, fabrics and membranes. Essentially three types of hierarchies are considered: one-dimensional hierarchy, two-dimensional hierarchy and three-dimensional hierarchy each giving rise to unique behaviors.

Shanti

Invited Talk: Nanomaterials for ‘enzyme-free’ biosensing @ Amriteshwari Hall
Aug 13 @ 2:17 pm – 2:35 pm

SatheeshSatheesh Babu T. G., Ph.D.
Associate Professor, Department of Sciences, School of Engineering, Amrita University, Coimbatore, India


Nanomaterials for ‘enzyme-free’ biosensing

Enzyme based sensors have many draw backs such as poor storage stability, easily affected by the change in pH and temperature and involves complicated enzyme immobilization procedures.  To address this limitation, an alternative approach without the use of enzyme, “non-enzymatic” has been tried recently. Choosing the right catalyst for direct electrochemical oxidation / reduction of a target molecule is the key step in the fabrication of non-enzymatic sensors.

Non-enzymatic sensors for glucose, creatinine, vitamins and cholesterol are fabricated using different nanomaterials, such as nanotubes, nanowires and nanoparticles of copper oxide, titanium dioxide, tantalum oxide, platinum, gold and graphenes. These sensors selectively catalyse the targeted analyte with very high sensitivity. These nanomaterials based sensors combat the drawbacks of enzymatic sensors.

Satheesh