Temporal-to-spatial dynamic mapping, flexible recognition, and temporal correlations in an olfactory cortex model.
This paper proposes temporal-to-spatial dynamic mapping inspired by neural dynamics of the olfactory cortex. In our model the temporal structure of olfactory-bulb patterns is mapped to the spatial dynamics of the ensemble of cortical neurons. This mapping is based on the following biological mechanism: while anterior part of piriform cortex can be excited by the afferent input alone, the posterior areas require both afferent and association signals, which are temporally correlated in a specific way. One of the functional types of the neurons in our model corresponds to the cortical spatial dynamics and encodes odor components, and another represents temporal activity of association-fiber signals, which, we suggest, may be relevant to the encoding of odor concentrations. The temporal-to-spatial mapping and distributed representation of the model enable simultaneous rough cluster classification and fine recognition of patterns within a cluster as parts of the same dynamic process. The model is able to extract and segment the components of complex odor patterns which are spatiotemporal sequences of neural activity.
Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY 40292, USA.
Mykola
M
Lysetskiy
Andrzej
A
Lozowski
Jacek M
JM
Zurada
D000818:Animals; D001931:Brain Mapping; D019706:Excitatory Postsynaptic Potentials; D006801:Humans; D008959:Models, Neurological; D009812:Odorants; D009830:Olfactory Bulb / Q000502:physiology*; D009833:Olfactory Pathways / Q000502:physiology*; D012903:Smell / Q000502:physiology*; D013569:Synapses / Q000502:physiology; D013997:Time Factors