Pattern Recognition in the Olfactory System of the Locust: Priming, Gain Control and Coding Issues
<p>Object recognition requires both specificity, to ensure that stimuli with distinct behavioral relevance are distinguished, and invariance, to ensure that different instances of the same stimulus are recognized as the same under varied conditions (intensity, pitch, position,...). Psychophy...
| Summary: | <p>Object recognition requires both specificity, to ensure that stimuli with distinct behavioral relevance
are distinguished, and invariance, to ensure that different instances of the same stimulus
are recognized as the same under varied conditions (intensity, pitch, position,...). Psychophysical
studies show that an odor can be perceived as identical over significant ranges of concentrations.
Whether concentration invariance results, at least in part, from low-level neural phenomena rather
than cognitive grouping is so far unknown.</p>
<p>I explore, firstly, the contribution of projection neurons (PNs) in the antennal lobe of the
locust, the analog of the vertebrate olfactory bulb, to the recognition of odor identity across concentrations;
and secondly, what role spike timing, neuronal identity, and synchronization among neuronal
assemblies play in the encoding and decoding of odor information by downstream neurons.</p>
<p>I show the following: <br />
A novel computerized odor delivery system capable of delivering binary mixtures in arbitrary ratios and with arbitrary timecourses selected in real-time.<br />
The locust can recognize odors, and shows innate olfactory preferences.</p>
<p>PNs solve the task of encoding both odorant concentration and odorant identity, independently
of concentration, in three ways. First, by multiplexing information in different response dimensions
using a code that involves neuronal identity, spike timing (on a timescale slower than
previously believed) and synchronization across a neuronal assembly. Second, via a novel phenomenon
of experience-dependent plasticity that contributes to PNs’ invariance to concentration and
sensitizes PNs after exposure to an odor at high concentration, contrary to the adaptation exhibited
by receptors. Third, a phenomenon of gain control, whereby excitatory and inhibitory responses balance
out massive changes in receptor activity as a function of odorant concentration, maintains the
output of PNs within a small dynamic range.</p>
<p>A further mechanism of gain control contributing to keep the activity of early olfactory circuits
relatively constant across the wide dynamic range of odorant concentrations in the air is the
physical chemistry of odorant reception confers the olfactory system invariance to odorant volatility,
a physical property that has hitherto been believed to play a fundamental role in an odorant’s effectiveness.</p>
<p>Response patterns sometimes exhibit stable representations over large composition ranges
and then abrupt transitions as a function of concentration and mixture composition, suggesting the
difference between “same” and “different” odors may be delineated by sharp boundaries in odor
space.</p>
<p>Finally, how is the distributed code for odors in PN assemblies decoded? I show that
although synchronization among PN assemblies does not augment stimulus information in PN temporal
responses, it is necessary for the read-out of odor information by downstream neurons.
In sum, early olfactory circuits appear to employ plasticity, gain control and temporal coding
across synchronizing neuronal assemblies to solve the odor recognition problem across multiple
concentrations.</p>
<p>Appendices show that the variability of PN responses is correlated across neurons, show
how to produce non-cyclic Winnerless Competition (WLC) and a learning rule that causes random
networks to self-organize into WLC, present an exact hypothesis test for binomial distributions,
improvements to sliding-window cross-correlation and to the K-nearest neighbor classification algorithm,
a combinatorial analysis of the connectivity between the locust antennal lobes and mushroom
bodies, a didactic exposition of Victor and Purpura’s spike cost-based metric and an experiment
showing heterogeneity along the length of the locust antenna.</p>
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