A neural network model of general olfactory coding in the insect antennal lobe

Abstract

A central problem in olfaction is understanding how the quality of olfactory stimuli is encoded in the insect antennal lobe (or in the analogously structured vertebrate olfactory bulb) for perceptual processing in the mushroom bodies of the insect protocerebrum (or in the vertebrate olfactory cortex). In the study reported here, a relatively simple neural network model, inspired by our current knowledge of the insect antennal lobes, is used to investigate how each of several features and elements of the network, such as synapse strengths, feedback circuits and the steepness of neural activation functions, influences the formation of an olfactory code in neurons that project from the antennal lobes to the mushroom bodies (or from mitral cells to olfactory cortex). An optimal code in these projection neurons (PNs) should minimize potential errors by the mushroom bodies in misidentifying the quality of an odor across a range of concentrations while maximizing the ability of the mushroom bodies to resolve odors of different quality. Simulation studies demonstrate that the network is able to produce codes independent or virtually independent of concentration over a given range. The extent of this range is moderately dependent on a parameter that characterizes how long it takes for the voltage in an activated neuron to decay back to its resting potential, strongly dependent on the strength of excitatory feedback by the PNs onto antennal lobe intrinsic neurons (INs), and overwhelmingly dependent on the slope of the activation function that transforms the voltage of depolarized neurons into the rate at which spikes are produced. Although the code in the PNs is degraded by large variations in the concentration of odor stimuli, good performance levels are maintained when the complexity of stimuli, as measured by the number of component odorants, is doubled. When excitatory feedback from the PNs to the INs is strong, the activity in the PNs undergoes transitions from initial states to stimulus-specific equilibrium states that are maintained once the stimulus is removed. When this PN-IN feedback is weak the PNs are more likely to relax back to a stimulus-independent equilibrium state, in which case the code is not maintained beyond the application of the stimulus. Thus, for the architecture simulated here, strong feedback from the PNs onto the INs, together with step-like neuronal activation functions, could well be important in producing easily discriminable odor quality codes that are invariant over several orders of magnitude in stimulus concentration.