An olfactory recognition model based on spatio-temporal encoding of odor quality in the olfactory bulb

Abstract

In order to study the problem how the olfactory neural system processes the odorant molecular information for constructing the olfactory image of each object, we present a dynamic model of the olfactory bulb constructed on the basis of well-established experimental and theoretical results. The information relevant to a single odor, i.e. its constituent odorant molecules and their mixing ratios, are encoded into a spatio-temporal pattern of neural activity in the olfactory bulb, where the activity pattern corresponds to a limit cycle attractor in the mitral cell network. The spatio-temporal pattern consists of a temporal sequence of spatial firing patterns: each constituent molecule is encoded into a single spatial pattern, and the order of magnitude of the mixing ratio is encoded into the temporal sequence. The formation of a limit cycle attractor under the application of a novel odor is carried out based on the intensity-to-time-delay encoding scheme. The dynamic state of the olfactory bulb, which has learned many odors, becomes a randomly itinerant state in which the current firing state of the bulb itinerates randomly among limit cycle attractors corresponding to the learned odors. The recognition of an odor is generated by the dynamic transition in the network from the randomly itinerant state to a limit cycle attractor state relevant to the odor, where the transition is induced by the short-term synaptic changes made according to the Hebbian rule under the application of the odor stimulus.

Associative Memory with Dynamic Synapses

“…Although the functional roles and relevance of these results for associative memory are not yet clear, they might be linked to the spatiotemporal encoding scheme (Hoshino, Kashimori, & Kambara, 1998) of the olfactory cortex, which is believed to represent a good model for analysis of associative memory processes (Haberly & Bower, 1989). In Hoshino et al (1998), the model based on experimental ndings showed that the information about odors could be encoded into a spatiotemporal pattern of neural activity in the olfactory bulb, which consists of a temporal sequence of spatial activity patterns. The recognition of olfactory information, as also suggested by other authors, might be related to oscillatory activity patterns.…”

Role of centrifugal projections to the olfactory bulb in olfactory processing

“…While some data demonstrate effects of noradrenergic, serotonergic, and cholinergic modulation on olfactory learning in rodents (see, e.g., McLean et al 1993;Ravel et al 1994;Wilson et al 1994;Ciombor et al 1999;De Rosa and Hasselmo 2000;Wirth et al 2000;De Rosa et al 2001;Linster et al 2001a;Bouret and Sara 2002;Fletcher and Wilson 2002;Wilson et al 2004) and some of the local physiological effects of these neuromodulators have been analyzed in vitro and in vivo (McLean et al 1993;Ciombor et al 1999;Czesnik et al 2001;Hayar et al 2001;Bouret and Sara 2002;Yuan et al 2003, for review, see Linster and Hasselmo 2001), little is known about the roles of cortical and hippocampal feedback projections to the OB for olfactory processing. Studies in rabbits and in rats have suggested that the bulbo-cortical loop is crucial for maintaining the oscillatory dynamics of the OB (Gray and Skinner 1988;Neville and Haberly 2003;Martin et al 2006) and these hypotheses have been further investigated in modeling studies (Freeman 1987;Linster and Hasselmo 1997;Hoshino 1998).…”

A neural network model of the inferior colliculus with modifiable lateral inhibitory synapses for human echolocation

“…Neuron i ®res with a probability determined by the following equation (Hoshino et al 1998;Oyamada et al 2000):…”