Abstract
The non-topographical representation of odor quality space differentiates early olfactory representations from those in other sensory systems. Decorrelation among olfactory representations with respect to physical odorant similarities has been proposed to rely upon local feed-forward inhibitory circuits in the glomerular layer that decorrelate odor representations with respect to the intrinsically high-dimensional space of ligand–receptor potency relationships. A second stage of decorrelation is likely to be mediated by the circuitry of the olfactory bulb external plexiform layer. Computations in this layer, or in the analogous interneuronal network of the insect antennal lobe, are dependent on fast network oscillations that regulate the timing of mitral cell and projection neuron (MC/PN) action potentials; this suggests a largely spike timing-dependent metric for representing odor information, here proposed to be a precedence code. We first illustrate how the rate coding metric of the glomerular layer can be transformed into a spike precedence code in MC/PNs. We then show how this mechanism of representation, combined with spike timing-dependent plasticity at MC/PN output synapses, can progressively decorrelate high-dimensional, non-topographical odor representations in third-layer olfactory neurons. Reducing MC/PN oscillations abolishes the spike precedence code and blocks this progressive decorrelation, demonstrating the learning network's selectivity for these sparsely synchronized MC/PN spikes even in the presence of temporally disorganized background activity. Finally, we apply this model to odor representations derived from calcium imaging in the honeybee antennal lobe, and show how odor learning progressively decorrelates odor representations, and how the abolition of PN oscillations impairs odor discrimination.
Highlights
As neural representations of sensory stimuli progress from peripheral sensors into the central nervous system, they are transformed in terms of feature selectivity and in terms of the underlying spike encoding metric
Whereas neurons embedded in primary sensory organs appear to represent information largely by “rate coding” – a simple metric in which the instantaneous spike rate of a cell represents its level of activation, and the timecourse of activity follows that of the stimulus – higher-order sensory neurons can transform this information into more sophisticated metrics, with evoked action potentials typically sparser in terms of total activity and more tightly regulated in time
Model mitral cell and projection neuron (MC/PN) received direct synaptic excitation from the olfactory sensory neurons (OSNs) population associated with a given glomerulus as well as periodic feedforward inhibition from a non-spiking interneuron representing a population of interconnected inhibitory interneurons. (The network mechanisms responsible for generating fast oscillations in the olfactory bulb (OB) and antennal lobe (AL) are contested, and it is not the goal of the present model to explore their relative merits)
Summary
As neural representations of sensory stimuli progress from peripheral sensors into the central nervous system, they are transformed in terms of feature selectivity and in terms of the underlying spike encoding metric. In the analogous mammalian olfactory bulb (OB), the enhancement of oscillations has been associated with increased perceptual acuity (Nusser et al, 2001; Beshel et al, 2007; Kay et al, 2009); olfactory acuity is impaired by reducing inhibitory synaptic strengths in the recurrent circuit from which gamma oscillations are generated, and enhanced by the potentiation of this inhibition (Abraham et al, 2010) That is, in this system, and perhaps generally, spike timing regulation appears not to replace but to supplement and modify the specificity of the underlying identity code, in which chemosensory information is represented by the identities of the ensemble of spiking projection neurons (reviewed by Laurent, 1999) – or, more precisely, by the pattern of relative levels of activation across the ensemble (Cleland et al, 2007)
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