FY2010 Theoretical and Experimental Neurobiology Unit

Theoretical and Experimental Neurobiology Unit

Principal Investigator: Klaus M. Stiefel
Research Theme: Single cell computation, Dendritic morphology, Theoretical Neurobiology

Abstract

The goal of this research unit is to shed more light on the function of individual brain cells. For that purpose, we are using a combination of experimental and theoretical approaches.

The experimental approaches are whole-cell patch clamp recordings in slices of the mouse frontal cortex. The basic question we want to answer is “what type of computer is a neuron”? We are studying several aspects of the signal processing of neurons, such as their   precision, phase-reset curves as well as their behavior under intrinsic and forced oscillations. As neurons in the cortex are subject to a variety of neuromodulatory (dopaminergic, cholinergic) influences, we are also studying all the aforementioned phenomena in cells subjected to these modulators. The theoretical approaches are biophysical simulations of neurons and the use of genetic algorithms.

This year, we continued our research into the dynamical systems behavior of neurons.  Specifically, we are currently investigating stochastic resonance, the phenomenon in which a moderate level of background noise leads to improved signal processing　in neurons. Furthermore, we are still investigating phase-reset curves of neurons, and have developed and published a novel method for determining this important measure of neural oscillatory behavior.

 1. Staff

  • Dr. Marylka Uusisaari, Researcher
  • Ms. Vandana R. Padala, Technical Staff
  • Ms. Ryoko Uchida, Research administrator /  Secretary

2. Collaborations

Nothing to report

3. Activities and Findings

3.1 Phase Reset Curves  

We continued to investigate neurons as dynamical systems. In particular, we determined phase-response curves from neurons, which are an important measure of the oscillatory behavior of a neuron. These functions plot the phase shift experienced by neural spiking as a funtion of the phase of the perturbation and give valuable hints to the behavior of neurons in synaptcally connected networks. We developed, and published, a novel method for determining phase-response curves from fluctuating current injections. We combine slice recordings and biophysical simulations for this research project.

 3.2. Stochastic resonance

Another aspect of non-linear neural behavior we investigate is stochastic resonance, the effect that a small amount of noise leads to an improved signal processing performance. we investigate this property in cortical pyramidal neurons, and measure how neuromodulation affects stochastic resonance. We also combine in-vitro experimental techniques and theoretical investigations for this project.

 3.3 Dendritic morphologies

Dendrites are a neuron's cellular protrusions which collect synaptic inputs from other neurons. There is a multitude of dendrites in different animals and brain regions, but it is not known yet what computations many of these dendrites carry out. We investigate this with an inverse approach, where we use optimization algorithms to find dendrites optimized for certain computational functions. In the latest research project along these lines we investigate how dendrites cope with stochasticity in the inputs to neurons.

 3.4 Fish Biodiversity & Ecology

In a project going into a new avenue for our unit, one of us (K.M.S.) conducted a survey of the gobies, small but incredibly diverse marine fishes, in an island (Malapascua) and a nearby seamount (Monad Shoal) the central Philippines. We found 59 species in 19 genera, including 1 undescribed species of the genus Trimma, and 2 geographic and 3 depth range expansions. We observed a bias towards hovering species (of the genus Trimma) and away from shrimp-associated gobies on the Monad seamount compared to Malapascua island. These findings confirm the known shift of gobies towards planctotrophy with increasing depth. The comparison of the island versus seamount goby fauna also shows a lesser abundance and species richness of shrimp-associated gobies at the seamount. This likely reflects the fact that hydrodynamic features of the environment play a dominant role in selecting which gobiid species, or their synbiontic  shrimp, will be found in a certain location.

4. Publications

4.1 Journals

Stiefel, K. M., Fellous, J. M., Thomas, P. J. & Sejnowski, T. J. Intrinsic subthreshold oscillations extend the influence of inhibitory synaptic inputs on cortical pyramidal neurons (vol 31, pg 1019, 2010). European Journal of Neuroscience 31, 1509-1509, doi:10.1111/j.1460-9568.2010.07245.x (2010) 

Torben-Nielsen, B., Uusisaari, M., and Stiefel, K. M. A comparison of methods to determine neuronal phase-response curves. Front Neuroinformatics 4, 6, doi:10.3389/fninf.2010.00006 (2010) 

Torben-Nielsen, B., and Stiefel, K. M. Wide-Field Motion Integration in Fly VS Cells: Insights from an Inverse Approach. Plos Computational Biology 6, doi:10.1371/journal.pcbi.1000932 (2010) 

Torben-Nielsen B., and Stiefel K.M., An inverse approach for elucidating dendritic function. Front. Comput. Neurosci. 4:128. doi:10.3389/fncom.2010.00128(2010)

Uusisaari, M. and Knopfel T., GlyT2+ neurons in the lateral cerebellar nucleus. Cerebellum 9(1): 42-55 (2010)

Knopfel T., and Uusisaari, M.,  Functional Classification of Neurons in the Mouse Lateral Cerebellar Nuclei. Cerebellum, doi:10.1007/s12311-010-0240-3 (2010)  

 4.2 Books and other one-time publications

Nothing to report

4.3 Oral and Poster Presentations

Stiefel, K. M., An inverse approach for elucidating dendritic function., National Institute for Physiological Sciences(NIPS) "Fresh Perspectives of Computation in neuronal Systems" International Workshop, Okazaki, Japan, June 2, 2010 

Stiefel, K. M., An inverse approach for investigating dendritic function-structure relationships., Single neuron morphologies & computation workshop, University of  Amsterdam, the Netherlands, July 8, 2010

Stiefel, K.M. Introduction to modeling morphologically detailed neurons., Okinawa Computational Neuroscience course, June 16, 2010

Uusisaari M, Torben-Nielsen B., Gutkin B. , and Stifel K.M., Stochastic resonance improves weak signal detection in the pyramidal neurons of mouse visual cortex., SFN Annual Meeting 2010, San Diego, CA, Nov 15, 2010

Uusisaari M., Torben-Nielsen B. & Stiefel K.M., Neuromodulation of stochastic resonance in the cortical neurons., Poster presentation at the FENS 2010, Amsterdam, The Netherlands, July 7, 2010

Uusisaari M., Gutkin B. & Stiefel K.M., Phase response curve analysis of network properties in the deep cerebellar nuclei., Poster presentation at CNS2010, San Antonio, Texas, USA. July 20, 2010

Uusisaari M, Torben-Nielsen B., Gutkin B. , and Stifel K.M., Stochastic resonance improves weak signal detection in the pyramidal neurons of mouse visual cortex., Poster presented at the SFN Annual Meeting 2010, San Diego, CA, November 15, 2010

 5. Intellectual Property Rights and Other Specific Achievements

Nothing to report 

 6. Meetings and Events

6.1 Single Neuron Morphologies & Computation Workshop

  • Date: July 8,2010-July 9, 2010
  • Venue: VU University of Amsterdam
  • Co-organizers:
    • Benjamin Torben-Nielsen (Okinawa Institute of Science and Technology)
    • Jaap van Pelt (VU University Amsterdam)
    • Arjen van Ooyen (VU University Amsterdam)
  • Co-spnsors: VU University Amsterdam
    • Giorgio Ascoli (George Mason University)
    •  Luciano da Fontoura Costa (Instituto Federal de Santa Catarina)
    •  Hermann Cuntz (University College London)
    •  Rodney Douglas (Institute of neuroinformatics)
    •  Boris Gutkin (Ecole Normale Superieure)Christiaan de Kock (VU University Amsterdam)
    •  Marcel Oberlaender (Max Planck Florida Institute)
    • Arjen van Ooyen (VU University Amsterdam)
    •  Jaap van Pelt (VU University Amsterdam)
    •  Imad Riachi (Blue Brain Project)
    •  Arnd Roth (University College London)
    •  Klaus Stiefel (Okinawa Institute of Science and Technology)
    •  Volker Steuber (University of Hertfordshire)
    •  Benjamin Torben-Nielsen (Okinawa Institute of Science and Technology)
    •  Gabriel Wittum (Universitat Frankfurt)

  7. Others

 Nothing to report