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This article is part of the supplement: Nineteenth Annual Computational Neuroscience Meeting: CNS*2010

Open Access Poster Presentation

Using feed-forward networks to infer the activity of feed-back neuronal networks

Xinxian Huang1*, Farzan Nadim12 and Amitabha Bose13

Author Affiliations

1 Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA

2 Department of Biological Sciences, Rutgers University, Newark, NJ 07102, USA

3 School of Physical Sciences, Jawaharlal Nehru University, New Delhi 110067 India

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BMC Neuroscience 2010, 11(Suppl 1):P48  doi:10.1186/1471-2202-11-S1-P48

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/11/S1/P48


Published:20 July 2010

© 2010 Huang et al; licensee BioMed Central Ltd.

Poster Presentation

Phase-locked activity of neurons is important for producing the proper activation sequence and coordination of neurons within Central Pattern Generating (CPG) networks. As it is often difficult to analyze the phase-locked activity of a large feedback network directly, it is critical to develop analytic methods that are lower-dimensional which involve different pieces of the full network. Here, we provide a technique to study smaller feed-forward networks and then combine the obtained information to understand the activity of feedback networks. As a case study, we study the phase of activity of two reciprocally inhibitory bursting neurons in the crustacean stomatogastric ganglion (STG) in which the firing time of one neuron has an effect on the period of the other, and vice versa. Each of these two processes defines a feed-forward map and we combine them to determine the period and activity phase of the feedback network. We examine the conditions on the existence and stability of phase-locked solutions when the synapses display short-term depression, a common form of synaptic plasticity. We find that the stability is lost in some circumstances in the feedback network with the existence of the synaptic depression.

Consider two neurons coupled via inhibitory synapses. Neuron A is modeled as oscillatory while neuron B as tonically active. The synapse from A to B is depressing but B to A is not. The cycle period of neuron A depends on the firing time of B relative to A, which affects the recovery and strength of the synapse from A to B. This synapse, in turn, determines the firing time of B relative to A. We derive a one-dimensional Poincare map Π , defined at the onset of A activity, that measures the strength sn of the A to B synapse (Fig. 1). The model is built as in [1]. Here d measures the extent of depression of the A to B synapse, tn the firing time of B relative to A and Pn the period of A in each cycle. A fixed point of this map determines the unique period and phase at which A and B lock. Its existence corresponds to the intersection point of the two feed-forward maps (Fig. 2), each of which is obtained from a different feed-forward sub-network. One map, H, determines how tn affects sn+1, while the other map, G, determines how sn affects tn. The map H is defined separately in three different regions. In Region I, neuron B fires when A is still active but its activity is not terminated by B; Region II is similar to I but the activity of A is terminated by B; in Region III, B fires when A is inactive. In biological networks Region I and II are small and the graphs of H and G typically intersect in Region III, as observed in the two mutually inhibitory neurons PD and LP in the STG. We find that the phase-locked solution is stable when it occurs in Regions I and III but the stability is lost in some circumstances in Region II.

thumbnailFigure 1. Reciprocally inhibitory network.

thumbnailFigure 2. Intersection of feed-forward curves H and G correspond to a fixed point of the one-dimensional feedback map.

Acknowledgements

Supported by NSF DMS-0615168 and NIH MH-60605.

References

  1. Bose A, Manor Y, Nadim F: The activity phase of postsynaptic neurons in a simplified rhythmic network.

    J Comput Neurosci 2004, 17:245-261. PubMed Abstract | Publisher Full Text OpenURL