Event Abstract

Towards Realistic Receptive Field Properties in a Biologically Inspired Spiking Network Model of the Mammalian Primary Visual Cortex

  • 1 University of Luebeck, Institute for Robotics and Cognitive Systems, Germany
  • 2 Graduate School for Computing in Medicine in Life Sciences, Germany
  • 3 University-Clinics Schleswig-Holstein, Department of Psychiatry, Germany

The mammalian primary visual cortex is one of the most studied brain regions and we have a good understanding of the basic principles of information processing in the visual system. Since the operation principles in cortical regions are similar, and dysfunctions of some of these principle networks is thought to underlie several neurological and psychiatric diseases, it appears particularly fruitful to explore candidate mechanisms in the comparatively well-known visual cortex.

Building computational models is a crucial step in exploring how the visual system computes and in understanding how neuropsychiatric diseases affect these computations. Our approach tries to incorporate anatomical and physiological data in order to build a model capable of showing a diversity of different, well-understood features.

Therefore, we have built a detailed biologically plausible model that unifies as many aspects of early visual processing as possible, with a focus on receptive field properties of single neurons. We used multi-compartment neurons of Hodgkin- Huxley type with a total of 11 active conductances that allowed us to tune single neurons to different firing properties according to experimental data [1,2] and more complex.models [3] Afterwards, we assembled the network with biologically plausible connections and synaptic mechanisms. In contrast to the model of Oliveira et al. [4] our model is based on more detailed single neuron models and was designed to include a broader variety of receptive field properties.

We present the detailed network specifications and show simulations testing a variety of receptive field properties (e.g. orientation and direction selectivity, spatial and temporal tuning) of the neurons in our model. An extensive comparison with receptive field properties of real neurons [5,6] is also given.

Acknowledgements

This work was partially supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany's Excellence Initiative [DFG GSC 235/1].

References

1. Cunningham MO et al.: A role for fast rhythmic bursting neurons in cortical gamma oscillations in vitro. Proc. Natl. Acad. Sci, USA, 2004, 101:7152-7157.
2. Traub RD et al.: Fast rhythmic bursting can be induced in layer 2/3 cortical neurons by enhancing na+ conductance or by blocking bk channels. J. Neurophysiol., 2003, 89:909-921.
3. Traub RD et al.: Single-Column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. J Neurophysiol., 2005, 93:2194-2232.
4 Oliveira et al.: A biologically plausible neural network model of the primate primary visual cortex. Neurocomputing, 2002, 44-46:957-963.
5. Gilbert CD: Laminar differences in receptive field properties of cells in cat primary visual cortex. J. Physiol., 1977, 268:391-421.
6. Girman SV et al.: Receptive field properties of single neurons in rat primary visual cortex. J Neurophysiol., 1999, 82:301-311.

Keywords: multi-compartment model, Neurons, networks and dynamical systems, receptive field properties, Visual System

Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.

Presentation Type: Poster

Topic: neurons, networks and dynamical systems (please use "neurons, networks and dynamical systems" as keywords)

Citation: Metzner C, Schweikard A and Zurowski B (2011). Towards Realistic Receptive Field Properties in a Biologically Inspired Spiking Network Model of the Mammalian Primary Visual Cortex
. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00066

Received: 23 Aug 2011; Published Online: 04 Oct 2011.

* Correspondence: Mr. Christoph Metzner, University of Luebeck, Institute for Robotics and Cognitive Systems, Luebeck, Germany, c.metzner@herts.ac.uk

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