Understanding the nature of neural representation is a central challenge of neuroscience. One common approach to this challenge is to compute receptive fields by correlating neural activity with external variables drawn from sensory signals. But these receptive fields are only meaningful to the experimenter, not the organism, because only the experimenter has access to both the neural activity and knowledge of the external variables. To understand neural representation more directly, recent methodological advances have sought to capture the intrinsic geometry of sensory driven neural responses without external reference. To date, this approach has largely been restricted to low-dimensional stimuli as in spatial navigation. In this talk, I will discuss recent work from my lab examining the intrinsic geometry of sensory representations in a model vocal communication system, songbirds. From the assumption that sensory systems capture invariant relationships among stimulus features, we conceptualized the space of natural birdsongs to lie on the surface of an n-dimensional hypersphere. We computed composite receptive field models for large populations of simultaneously recorded single neurons in the auditory forebrain and show that solutions to these models define convex regions of response probability in the spherical stimulus space. We then define a combinatorial code over the set of receptive fields, realized in the moment-to-moment spiking and non-spiking patterns across the population, and show that this code can be used to reconstruct high-fidelity spectrographic representations of natural songs from evoked neural responses. Notably, we find that topological relationships among combinatorial codewords directly mirror acoustic relationships among songs in the spherical stimulus space. That is, the time-varying pattern of co-activity across the neural population expresses an intrinsic representational geometry that mirrors the natural, extrinsic stimulus space.  Combinatorial patterns across this intrinsic space directly represent complex vocal communication signals, do not require computation of receptive fields, and are in a form, spike time coincidences, amenable to biophysical mechanisms of neural information propagation.

I will argue that the circuit architecture of the early olfactory system provides an adaptive, efficient mechanism for compressing the vast space of odor mixtures into the responses of a small number of sensors. In this view, the olfactory sensory repertoire employs a disordered code to compress a high dimensional olfactory space into a low dimensional receptor response space while preserving distance relations between odors. The resulting representation is dynamically adapted to efficiently encode the changing environment of volatile molecules. I will show that this adaptive combinatorial code can be efficiently decoded by systematically eliminating candidate odorants that bind to silent receptors. The resulting algorithm for 'estimation by elimination' can be implemented by a neural network that is remarkably similar to the early olfactory pathway in the brain. Finally, I will discuss how diffuse feedback from the central brain to the bulb, followed by unstructured projections back to the cortex, can produce the convergence and divergence of the cortical representation of odors presented in shared or different contexts. Our theory predicts a relation between the diversity of olfactory receptors and the sparsity of their responses that matches animals from flies to humans. It also predicts specific deficits in olfactory behavior that should result from optogenetic manipulation of the olfactory bulb and cortex, and in some disease states.

A globally invasive form of the mosquito Aedes aegypti has evolved to specialize in biting humans, making it an efficient vector of dengue, yellow fever, Zika, and chikungunya. Host-seeking females identify humans primarily by smell, strongly preferring human odour over the odor of non-human animals. Exactly how they discriminate, however, is unclear. Human and animal odors are complex blends that share most of the same chemical components, presenting an interesting challenge in sensory coding. I will talk about recent work from the lab showing that (1) human and animal blends can be distinguished by the relative concentration of a diverse array of compounds and that (2) these complex chemical differences translate into a neural code for human odor that involves as few as two to three olfactory glomeruli in the mosquito brain. Our work demonstrates how organisms may evolve to discriminate complex odor stimuli of special biological relevance with a surprisingly simple combinatorial code and reveals novel targets for the design of next-generation mosquito control strategies.