Disorder and the Neural Representation of Complex Odors

Abstract

Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors and valences. Here, we propose how the architecture of olfactory circuits leverages disorder, diffuse sensing and redundancy in representation to meet these immense complementary challenges. First, the diffuse and disordered binding of receptors to many molecules compresses a vast but sparsely-structured odor space into a small receptor space, yielding an odor code that preserves similarity in a precise sense. Introducing any order/structure in the sensing degrades similarity preservation. Next, lateral interactions further reduce the correlation present in the low-dimensional receptor code. Finally, expansive disordered projections from the periphery to the central brain reconfigure the densely packed information into a high-dimensional representation, which contains multiple redundant subsets from which downstream neurons can learn flexible associations and valences. Moreover, introducing any order in the expansive projections degrades the ability to recall the learned associations in the presence of noise. We test our theory empirically using data from Drosophila. Our theory suggests that the neural processing of sparse but high-dimensional olfactory information differs from the other senses in its fundamental use of disorder.

Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb

“…On the theoretical side, it is widely recognized that the tremendous compression of dimensionality inherent in the transformation from odorant molecules to receptor activity means that the olfactory decoding problem is analogous to the one faced in compressed sensing (CS) [21][22][23][24][25][26][27][72][73][74]. Classical CS theory shows that sparse high-dimensional signals can be recovered from a small number of random projections [28][29][30][31][32][33].…”

“…Despite significant effort, attempts to find such structure in olfactory stimuli and link that geometry to maps in olfactory areas have succeeded only in identifying coarse principles for highlevel organization, far from the precision of orientation columns or tonotopy in visual and auditory cortices [17][18][19]. In the absence of geometric intuitions, the principles of compressed sensing (CS) have emerged as an alternative paradigm for understanding olfactory coding [20][21][22][23][24][25][26][27]. This framework provides a partial answer to the question of how an organism could identify which of millions of possible odorants are present given the activity of only a few hundred receptor types [28][29][30][31][32][33].…”

Efficient processing of natural scenes in visual cortex

“…Thinking in this way, researchers have explained many aspects of early vision, e.g., nonlinearities in the fly visual system (Laughlin, 1981 ), center-surround receptive fields of neurons in the vertebrate retina (Atick and Redlich, 1990 ; van Hateren, 1992b ; Vincent and Baddeley, 2003 ; Kuang et al, 2012 ; Pitkow and Meister, 2012 ; Simmons et al, 2013 ; Gupta et al, 2022 ), spike timing statistics (Fairhall et al, 2001 ), the preponderance of OFF cells over ON cells (Ratliff et al, 2010 ; Gjorgjieva et al, 2014 ), the mosaic organization of ganglion cells (Borghuis et al, 2008 ; Liu et al, 2009 ), the scarcity of blue cones and the large variability in numbers of red and green cones in humans (Garrigan et al, 2010 ), selection of predictive information by ganglion cells (Palmer et al, 2015 ; Salisbury and Palmer, 2016 ), and the expression of ion channels in insect photoreceptors (Weckström and Laughlin, 1995 ). Similar analyses suggest that the auditory (Schwartz and Simoncelli, 2001 ; Lewicki, 2002 ; Smith and Lewicki, 2006 ; Carlson et al, 2012 ) and olfactory (Teşileanu et al, 2019 ; Singh et al, 2021 ; Krishnamurthy et al, 2022 ) peripheries are also adapted to the statistical structure of the environment so that they use limited resources efficiently to represent sensory information (Sterling and Laughlin, 2015 ). While many of these analyses have focused on linear filtering properties, some have focused on the nonlinear separation of the visual stream into separate information channels like bright and dark spots or color channels (Garrigan et al, 2010 ; Ratliff et al, 2010 ; Gjorgjieva et al, 2014 ).…”

“…The sensory systems of animals face the challenge of using limited resources to process very large and high dimensional sensory spaces. For example, the olfactory systems of most animals use a few hundred to a thousand receptor types (Vosshall et al, 2000 ; Zozulya et al, 2001 ; Zhang and Firestein, 2002 ) to encode a vast number of mixtures of odorants drawn from the tens of thousands of possible volatile molecules (Dunkel et al, 2009 ; Touhara and Vosshall, 2009 ; Mayhew et al, 2022 ) with corresponding challenges for encoding and decoding odors (see Singh et al, 2021 ; Krishnamurthy et al, 2022 ) and references therein). The visual system faces the similarly acute problem of encoding the relevant information in continuously changing scenes composed of photons with frequencies that range continuously across the visual spectrum, light intensities spanning over 10 orders of magnitude (Tkačik et al, 2011 ), and a vast diversity of possible textures, shapes, objects, and kinds of motion.…”

“…corresponding challenges for encoding and decoding odors (see Singh et al, 2021;Krishnamurthy et al, 2022) and references therein). The visual system faces the similarly acute problem of encoding the relevant information in continuously changing scenes composed of photons with frequencies that range continuously across the visual spectrum, light intensities spanning over 10 orders of magnitude (Tkačik et al, 2011), and a vast diversity of possible textures, shapes, objects, and kinds of motion.…”