Relational representation in the olfactory system

Abstract

The perceptual quality of odors usually is robust to variability in concentration. However, maps of neural activation across the olfactory bulb glomerular layer are not stable in this respect; rather, glomerular odor representations both broaden and intensify as odorant concentrations are increased. The relative levels of activation among glomeruli, in contrast, remain relatively stable across concentrations, suggesting that the representation of odor quality may rely on these relational activity patterns. However, the neural normalization mechanisms enabling extraction of such relational representations are unclear. Using glomerular imaging activity profiles from the rat olfactory bulb together with computational modeling, we here show that (i) global normalization preserves concentration-independent odor-quality information; (ii) perceptual similarities, as assessed behaviorally, are better predicted by normalized than by raw bulbar activity profiles; and (iii) a recurrent excitatory circuit recently described in the olfactory bulb is capable of performing such normalization. We show that global feed-forward normalization in a sensory system is behaviorally relevant, and that a center-surround neural architecture does not necessarily imply center-surround function.coding ͉ computational neuroscience ͉ glomerulus ͉ mitral cell ͉ short-axon cell

Chemical Factors Determine Olfactory System Beta Oscillations in Waking Rats

“…8). A recent study has suggested that glomerular circuitry serves to normalize the strength of inputs to the olfactory bulb across wide ranges of concentration, consistent with our observations of the theta rhythm power (Cleland et al 2007). If the trigeminal system mediates part of the beta oscillatory response, we would again expect a similar effect in the anesthetized rats.…”

Olfactory Information Processing in theDrosophilaAntennal Lobe: Anything Goes?

“…The information coded in the remaining ϳ40 antennal lobe glomeruli in the fly might be enough to sustain discriminability over a broader range of concentrations. In mammals, across-glomerulus odor responses also increase with concentration, while relative activity patterns are stable over several orders of magnitude, with a similar increase in information content with increasing concentration as shown here (Cleland et al, 2007). This is in good agreement with behavioral data from bees and rats, showing better odor discrimination with increasing odor concentration (Bhagavan and Smith, 1997;Pelz et al, 1997;Cleland et al, 2007).…”

“…Information about odor concentration has an important ecological value for the fly and must therefore be re- tained along the olfactory pathway. Indeed, flies can learn to discriminate different odor concentrations (Borst, 1983;Xia and Tully, 2007), as has been shown for bees (Bhagavan and Smith, 1997;Pelz et al, 1997;Ditzen et al, 2003) and mammals (Cleland et al, 2007). Concentration-response curves were different for the different neuron subpopulations in the same glomerulus (Fig.…”

Role of intraglomerular circuits in shaping temporally structured responses to naturalistic inhalation-driven sensory input to the olfactory bulb

“…Our strategy to evaluate model performance was to assess how accurately the OB network generated output parameters, in particular, spiking dynamics, matching those recorded experimentally given biologically realistic inputs. This strategy differs from other models which have sought to identify circuit mechanisms capable of mediating particular neural computations, such as concentration-invariant odor discrimination (Brody and Hopfield 2003;Cleland et al 2007Margrie and Schaefer 2003), contrast enhancement and pattern decorrelation (Cleland and Linster 2012;Cleland and Sethupathy 2006;Imam et al 2012;Luo et al 2010;Wiechert et al 2010), and representation sparsening (Assisi et al 2007;Finelli et al 2008), among others. Our approach does not presume that any particular computation might be performed by a specific circuit component (nor does it investigate OB computations), but rather seeks to identify how specific network components influence experimentally measured output features, in this case, inhalation-driven MC spiking patterns.…”