Illuminating Vertebrate Olfactory Processing

The olfactory system encodes information about molecules by spatiotemporal patterns of activity across distributed populations of neurons and extracts information from these patterns to control specific behaviors. Recent studies used in vivo recordings, optogenetics, and other methods to analyze the mechanisms by which odor information is encoded and processed in the olfactory system, the functional connectivity within and between olfactory brain areas, and the impact of spatiotemporal patterning of neuronal activity on higher-order neurons and behavioral outputs. The results give rise to a faceted picture of olfactory processing and provide insights into fundamental mechanisms underlying neuronal computations. This review focuses on some of this work presented in a Mini-Symposium at the Annual Meeting of the Society for Neuroscience in 2012

Imposing structure on odor representations during learning in the prefrontal cortex

Animals have evolved sensory systems that afford innate and adaptive responses to stimuli in the environment. Innate behaviors are likely to be mediated by hardwired circuits that respond to invariant predictive cues over long periods of evolutionary time. However, most stimuli do not have innate value. Over the lifetime of an animal, learning provides a mechanism for animals to update the predictive value of cues through experience. Sensory systems must therefore generate neuronal representations that are able to acquire value through learning. A fundamental challenge in neuroscience is to understand how and where value is imposed in brain during learning. The olfactory system is an attractive sensory modality to study learning because the anatomical organization is concise in that there are relatively few synapses separating the sense organ from brain areas implicated in learning. Thus, the circuits for learned olfactory behaviors appear to be relatively shallow and therefore more experimentally accessible than other sensory systems. The goal of this thesis is to characterize the representation and function of neural circuits involved in olfactory associative learning. Odor perception is initiated by the binding of odors onto olfactory receptors expressed in the sensory epithelium. Each olfactory receptor neuron (ORN) expresses one of 1500 different receptor genes, the expression of which pushes the ORN to project with spatial specificity onto a defined loci within the olfactory bulb, the olfactory glomeruli. Therefore, each and every odor evokes a stereotyped map of glomerular activity in the bulb. The projection neurons of the olfactory bulb, mitral and tufted (M/T) cells, send axons to higher brain areas, including a significant input to the primary olfactory cortex, the piriform cortex. Axons from M/T cells project diffusely to the piriform without apparent spatial preference; as a consequence, the spatial order of the bulb is discarded in the piriform. In agreement with anatomical data, electrophysiological and optical imaging studies also demonstrate that individual odorants activate sparse subsets of neurons across the piriform without any spatial order. Moreover, individual piriform neurons exhibit discontinuous receptive fields that defy chemical or perceptual categorization. These observations suggests that piriform neurons receive random subsets of glomerular input. Therefore, odor representations in piriform are unlikely to be hardwired to drive specific behaviors. Rather, this model suggests that value must be imposed upon the piriform through learning. Indeed, the piriform has been shown to be both sufficient and necessary for aversive olfactory learning without affecting innate odor responses. However, how value is imposed on odor representations in the piriform and downstream associational areas remain largely unknown. We first developed a strategy to track neural activity in a population of neurons across multiple days in deep brain areas using 2-photon endoscopic imaging. This allowed us to assay changes in neural responses to odors during learning in piriform and in downstream associative areas. Using this technique, we first observe that piriform odor responses are unaffected by learning, so learning must therefore impose discernable changes in neural activity downstream of piriform. Piriform projects to multiple downstream areas that are implicated in appetitive associative learning, such as the orbitofrontal cortex (OFC). Imaging of neural activity in the OFC reveal that OFC neurons acquire strong responses to conditioned odors (CS+) during learning. Moreover, multiple and distinct CS+ odors activatethe same population of OFC neurons, and these responses are gated by context and internal state. Together, our imaging data shows that an external and sensory representation in the piriform is transformed into an internal and cognitive representation of value in the OFC. Moreover, we found that optogenetic silencing of the OFC impaired the ability of mice to acquire learned associations. Therefore, the robust representation of expected value of the odor cues is necessary for the formation of appetitive associations. We made an important observation: once the task has been learned with a set of odors, the OFC representation decays after learning has plateaued and remains silent even when mice encounter novel odors they haven’t previously experienced. Moreover, silencing the OFC when it was not actively engaged during the subsequent learning of new odors had no effect on learning. These sets of imaging and silencing experiments reveal that the OFC is only important during initial learning; once task structure has been acquired, it is no longer needed. Task performance after initial task acquisition must therefore be accommodated by other brain regions that can store the learned association for long durations. We therefore searched for other brain regions that held learned associations long-term. In the medial prefrontal cortex (mPFC), we observe that the learned representation persists throughout the entire course of training. Unlike the OFC, not only does this representation encode the positive expected value of CS+ odors, it also encodes the negative expected value of CS- odors in a non-overlapping ensemble of neurons. We further show through optogenetic silencing that this representation is necessary for task performance after the task structure has already been acquired. Therefore, while the OFC representation is required for initial task acquisition, the mPFC representation is required for subsequent appetitive learning and performance. Why would a learned representation vanish in the OFC and betransfered elsewhere? We hypothesize that the brain may allocate a portion of its real estate to be a cognitive playground where experimentation and hypothesis testing takes place. Once this area solves a task, it may unload what it has learned to storage units located elsewhere to free up space to learn new tasks. We further imaged another associative area, the basolateral amygdala (BLA), and found a representation of positive value that appears to be generated from a Hebbian learning mechanism. However, the silencing of this representation during learning had no effect. This suggests that while multiple and distributed brain areas encode cues that predict the reward, not all may be necessary for the learning process or for task performance. In summary, we have described a series of experiments that map the representation and function of different associational areas that underlie learning. The data and the techniques employed have the potential to significantly advance the understanding of learned behavior

Neural Circuit Dynamics and Ensemble Coding in the Locust and Fruit Fly Olfactory System

Raw sensory information is usually processed and reformatted by an organism’s brain to carry out tasks like identification, discrimination, tracking and storage. The work presented in this dissertation focuses on the processing strategies of neural circuits in the early olfactory system in two insects, the locust and the fruit fly. Projection neurons (PNs) in the antennal lobe (AL) respond to an odor presented to the locust’s antennae by firing in slow information-carrying temporal patterns, consistent across trials. Their downstream targets, the Kenyon cells (KCs) of the mushroom body (MB), receive input from large ensembles of transiently synchronous PNs at a time. The information arrives in slices of time corresponding to cycles of oscillatory activity originating in the AL. In the first part of the thesis, ensemble-level analysis techniques are used to understand how the AL-MB system deals with the problem of identifying odors across different concentrations. Individual PN odor responses can vary dramatically with concentration, but invariant patterns in PN ensemble responses are shown to allow odor identity to be extracted across a wide range of intensities by the KCs. Second, the sensitivity of the early olfactory system to stimulus history is examined. The PN ensemble and the KCs are found capable of tracking an odor in most conditions where it is pulsed or overlapping with another, but they occasionally fail (are masked) or reach intermediate states distinct from those seen for the odors presented alone or in a static mixture. The last part of the thesis focuses on the development of new recording techniques in the fruit fly, an organism with well-studied genetics and behavior. Genetically expressed fluorescent sensors of calcium offer the best available option to study ensemble activity in the fly. Here, simultaneous electrophysiology and two-photon imaging are used to estimate the correlation between G-CaMP, a popular genetically expressible calcium sensor, and electrical activity in PNs. The sensor is found to have poor temporal resolution and to miss significant spiking activity. More generally, this combination of electrophysiology and imaging enables explorations of functional connectivity and calibrated imaging of ensemble activity in the fruit fly.</p

Rapid Bayesian learning in the mammalian olfactory system

Many experimental studies suggest that animals can rapidly learn to identify odors and predict the rewards associated with them. However, the underlying plasticity mechanism remains elusive. In particular, it is not clear how olfactory circuits achieve rapid, data efficient learning with local synaptic plasticity. Here, we formulate olfactory learning as a Bayesian optimization process, then map the learning rules into a computational model of the mammalian olfactory circuit. The model is capable of odor identification from a small number of observations, while reproducing cellular plasticity commonly observed during development. We extend the framework to reward-based learning, and show that the circuit is able to rapidly learn odor-reward association with a plausible neural architecture. These results deepen our theoretical understanding of unsupervised learning in the mammalian brain

Driving Opposing Behaviors with Ensembles of Piriform Neurons

SummaryAnatomic and physiologic studies have suggested a model in which neurons of the piriform cortex receive convergent input from random collections of glomeruli. In this model, odor representations can only be afforded behavioral significance upon experience. We have devised an experimental strategy that permits us to ask whether the activation of an arbitrarily chosen subpopulation of neurons in piriform cortex can elicit different behavioral responses dependent upon learning. Activation of a small subpopulation of piriform neurons expressing channelrhodopsin at multiple loci in the piriform cortex, when paired with reward or shock, elicits either appetitive or aversive behavior. Moreover, we demonstrate that different subpopulations of piriform neurons expressing ChR2 can be discriminated and independently entrained to elicit distinct behaviors. These observations demonstrate that the piriform cortex is sufficient to elicit learned behavioral outputs in the absence of sensory input. These data imply that the piriform does not use spatial order to map odorant identity or behavioral output.PaperCli

Massive normalization of olfactory bulb output in mice with a 'monoclonal nose'

Perturbations in neural circuits can provide mechanistic understanding of the neural correlates of behavior. In M71 transgenic mice with a 'monoclonal nose', glomerular input patterns in the olfactory bulb are massively perturbed and olfactory behaviors are altered. To gain insights into how olfactory circuits can process such degraded inputs we characterized odor-evoked responses of olfactory bulb mitral cells and interneurons. Surprisingly, calcium imaging experiments reveal that mitral cell responses in M71 transgenic mice are largely normal, highlighting a remarkable capacity of olfactory circuits to normalize sensory input. In vivo whole cell recordings suggest that feedforward inhibition from olfactory bulb periglomerular cells can mediate this signal normalization. Together, our results identify inhibitory circuits in the olfactory bulb as a mechanistic basis for many of the behavioral phenotypes of mice with a 'monoclonal nose' and highlight how substantially degraded odor input can be transformed to yield meaningful olfactory bulb output

Odor representations in the mammalian olfactory bulb

A first key step in studying a sensory modality is to define how the brain represents the features of the sensory stimulus. This has proven to be a challenge in olfaction, where even the stimulus features have been a matter of considerable debate. In this review, we focus on olfactory representations in the first stage of the olfactory pathway, the olfactory bulb (OB). We examine the diverging viewpoints on spatially organized versus distributed representations. We then consider how odor sampling through respiration is a key part of the odorant code. Finally, we ask how the bulb handles the challenging task of representing mixtures. We suggest that current evidence points toward a representation that is spatially organized at the inputs but later distributed, with the spatial organization not being used for much computation. Nevertheless, this is a simple representation that effectively represents multiple individual odorants, as well as odor mixtures

Implementation of Olfactory Bulb Glomerular-Layer Computations in a Digital Neurosynaptic Core

We present a biomimetic system that captures essential functional properties of the glomerular layer of the mammalian olfactory bulb, specifically including its capacity to decorrelate similar odor representations without foreknowledge of the statistical distributions of analyte features. Our system is based on a digital neuromorphic chip consisting of 256 leaky-integrate-and-fire neurons, 1024 × 256 crossbar synapses, and address-event representation communication circuits. The neural circuits configured in the chip reflect established connections among mitral cells, periglomerular cells, external tufted cells, and superficial short-axon cells within the olfactory bulb, and accept input from convergent sets of sensors configured as olfactory sensory neurons. This configuration generates functional transformations comparable to those observed in the glomerular layer of the mammalian olfactory bulb. Our circuits, consuming only 45 pJ of active power per spike with a power supply of 0.85 V, can be used as the first stage of processing in low-power artificial chemical sensing devices inspired by natural olfactory systems