Review articleA computational model of the response of honey bee antennal lobe circuitry to odor mixtures: overshadowing, blocking and unblocking can arise from lateral inhibition
Introduction
Odor mixtures can present peculiar problems to an animal, particularly in regard to feature segmentation, because the exact composition of a mixture of odorants and/or the background in which it is presented can change across short spatial and temporal scales. For example, a foraging honey bee, having just received reinforcement at a particular flower, must make a decision whether or not the odor of the next flower signals similar reinforcement even though the mixture may vary considerably from flower to flower [30]. Feature segmentation, that is, perception of the elements of the mixture, is made difficult by the tuning characteristics of sensory cells in peripheral olfactory systems [41]. Sensory cells for non-pheromonal odorants are typically broadly tuned to respond to many different odorants, as are cells involved in detecting floral odorants in bees [13]. Given the highly nonlinear interactions that arise as a result of the transduction pathways 1, 6, 43, it is easy to presume that a mixture of odors will give rise to new perceptual qualities and hide any such qualities of individual elements. In this case, feature segmentation in a mixture, or generalization from one mixture to another, should be difficult.
The objective of the model presented below is to explore the contribution of central circuitry, particularly that of the circuits within the first relay station of olfactory processing, to this problem of generalization among mixtures of odors. Within this layer, which in insects is called the antennal lobe, sensory cells synapse onto output neurons, which then carry information out of the first layer for processing in other neuropils [16]. Several types of local interneurons potentially regulate the kinds of information that the output neurons carry. Neurons in the vertebrate olfactory bulb, which performs an analogous function to the antennal lobe, integrate information not only from intra-bulbar neurons, but also from numerous centrifugal structures, including piriform cortex, telencephalic basal forebrain and the brain stem locus coerulus. Behavioral investigations on the role of centrifugal input have shown that noradrenergic bulbar modulation is critical for long-term storage of information in mice in the accessory olfactory bulb [5], in the main olfactory bulb in sheep 18, 31and in new born rats [42]. Bulbar cholinergic modulation could be associated with short-term olfactory memory [32].
These data demonstrate that the first-order olfactory processing areas participate in some components of odor memory. In the present work we use physiological data from studies of the honey bee antennal lobe to show how modulation of inhibition can account for several behavioral results. These results, together with the present modeling effort, indicate that honey bees have the capacity to perform, at least in a limited sense, an elemental analysis of odor mixtures. As such, the model identifies and refines hypotheses that must now be subjected to empirical analysis.
Section snippets
Olfactory overshadowing, blocking and unblocking
Several behavioral paradigms have recently been applied to the study of mixture generalization and discrimination. Two of these—termed `overshadowing' and `blocking' 33, 34—will be the focus of the modeling effort outlined below. When animals are conditioned to respond to an odor mixture (OHO2) and then tested with one component, the response to that component (`R1mix' refers to the post-training response to odor 1 in a group that received mixture training) is typically lower than the response
Model architecture
The modeling approach and the equations used for neurons and synapses have been presented in detail before 22, 25. We present here some details of the circuitry and its manifestation in the model; the equations and parameter values are given in Appendix A. We will then describe the new features added for associative learning.
In the honey bee, the peripheral (sensory) representation of an odor stimulus is manifested in relatively broadly tuned sensory cells 2, 45. In the model, we introduce 15
Learning of pure odorants and mixtures
Before learning VUM does not respond to olfactory stimuli (Fig. 4A). After learning of a pure odorant (Fig. 4B, O1+S), VUM responds with an increased number of spikes to that stimulus (Fig. 4B, O1) or to binary mixtures containing that component (Fig. 4B, O1+O2). But VUM does not respond to other odorants (Fig. 4B, O2).
To verify the results over the whole input space, we ran simulations with a given network configuration using all possible configurations of O1 and O2 (210 combinations). In the
Discussion
We have presented a neural model of olfactory learning in the honey bee antennal lobe that reproduces physiological and behavioral results. The antennal lobe model is inspired by known anatomical data 10, 44and reproduces electrophysiological data pertaining to olfactory processing in the honey bee antennal lobe 21, 25. We show how the known responses of an identified interneuron, the VUMmx1 neuron 15, 16, which mediates information about the unconditioned stimulus, can be accounted for by
Acknowledgements
We wish to thank Seetha Bhagavan, Wayne Getz, Rainer Malaka, Claudine Masson and Robby Thorn for fruitful discussions. Supported by a grant from the National Institute of Mental Health (to B.H. Smith). C. Linster was supported by a fellowship from the International Human Frontier Science Program.
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