An oscillation-driven neural network for the simulation of an olfactory system
Understanding the nonlinear dynamics of an olfactory bulb (OB) is essential for the modelling of the brain and nervous system. We have analysed the nature of odour-receptor interactions and the conditions controlling neural oscillations. This analysis is the basis for the proposed biologically plausible three-tiered model of an oscillation-driven neural network (ODNN) with three non-linearities. The layered architecture of the bulb is viewed as a composition of different processing stages performing specific computational tasks. The presented three-tiered model of the olfactory system (TTOS) contains the sensory, olfactory bulb and anterior nucleus tiers. The number of excitatory (mitral/tufted) cells differs from the number of inhibitory (granule) cells, which improves the cognitive ability of the model. The odour molecules are first received at the sensory layer, where receptor neurons spatio-temporally encode them in terms of spiking frequencies. Neurons expressing a specific receptor project to two or more topographically fixed glomeruli in the OB and create a sensory map. Excitatory postsynaptic potentials are formed in the primary dendrite of mitral cells and are encoded in an exclusive way to present them to the coupled non-linear oscillatory model of the next mitral-granule layer. In a noisy background, our model functions as an associative memory, although it operates in oscillatory mode.
olfactory bulb oscillatory modelling precortex model