- Poster presentation
- Open Access
A modeling study of cortical waves in primary auditory cortex
© Beeman; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
- Auditory Cortex
- Spike Train
- Basket Cell
- Primary Auditory Cortex
- Inhibitory Connection
Cortical waves have been observed in many cortical areas, including the primary auditory cortex (AI). The thalamorecipient layer (IV) of AI is an ideal test-bed for the study of cortical waves because the inputs from the thalamus are arranged tonotopically along an axis. Wave propagation along this axis is essentially one-dimensional. Thus, a biologically realistic model of a piece of this area can act like a 'ripple-tank' used for the study of one-dimensional wave motion in physics.
AI has been less well studied than other neocortical areas, and any complete model will contain an enormous space of poorly-quantified parameters to be explored. In order to simplify the model enough to analyze, it was limited to a population of pyramidal cells and a smaller population of inhibitory basket cells. Inputs from other cortical layers were crudely represented by Poisson-distributed random inputs to provide background levels of firing for the two populations that are in agreement with measured values. The components within this simple model are represented realistically, with parameters constrained by experimental measurements. The multi-compartmental spiking neurons have firing patterns representative of pyramidal and basket cells in the auditory cortex. Synaptic inputs occur at appropriate locations on the dendrites. The distance dependence of the connection probabilities for the four types of connections was fit to experimental measurements. It has equivalent ranges for excitatory and inhibitory connections, unlike many models that account for lateral inhibition by assuming a greater range for inhibitory connections. Although the thalamic input model provides for more realistic inputs, the example shown in Figure 1 used single row excitations of spike trains at 220 and 440 Hz in the absence of background input.
David Beeman is partially supported by NIH grant 3 R01 NS049288-06.
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