Faculty Bibliography

The mammalian brain orchestrates goal-directed behavior through complex interactions between multiple memory systems, with the striatum and hippocampus playing pivotal interrelated roles. A central open question is the extent to which distinct memory signals from these systems drive learning to achieve desired goals and, once those are learned, maintain performance. Here, we used an MRI-compatible platform to obtain whole-brain functional imaging of head-fixed mice as they learn to perform a lick go/no-go odor discrimination task from the naïve state to task proficiency. Behaving mouse functional MRI (fMRI) acquired over a period of several days allowed us to characterize distributed activity as the animals learned the task, demonstrating differential involvement of the striatal and hippocampal memory systems accounting for correct and incorrect task responses. A consideration of the contribution of striatal sub-regions revealed that the responses of the dorsal striatum were correlated with improvement in reaction time, while responses in the ventral striatum were correlated with learning the task and maintaining task performance. In contrast, the dorsal hippocampus showed depressed responses to correct licks to the target odor (hits) and increased responses to incorrect licks to the non-target odor (false alarms). False alarms that were correlated with positive hippocampal responses had longer reaction times and emerged after the mouse learned the task, implicating the hippocampus in driving false memory responses. These results show that behaviorally beneficial actions were correlated with the striatum with a competing involvement of the hippocampus driving erroneous actions, setting the stage to study circuit-based mechanisms of false memory in the mouse.

Understanding sensory processing relies on the establishment of a consistent relationship between the stimulus space, its neural representation, and perceptual quality. In olfaction, the difficulty in establishing these links lies partly in the complexity of the underlying odor input space and perceptual responses. Based on the recently proposed primacy model for concentration invariant odor identity representation and a few assumptions, we have developed a theoretical framework for mapping the odor input space to the response properties of olfactory receptors. We analyze a geometrical structure containing odor representations in a multidimensional space of receptor affinities and describe its low-dimensional implementation, the primacy hull. We propose the implications of the primacy hull for the structure of feedforward connectivity in early olfactory networks. We test the predictions of our theory by comparing the existing receptor-ligand affinity and connectivity data obtained in the fruit fly olfactory system. We find that the Kenyon cells of the insect mushroom body integrate inputs from the high-affinity (primacy) sets of olfactory receptors in agreement with the primacy theory.

Connecting neuronal activity to perception requires tools that can probe neural codes at cellular and circuit levels, paired with sensitive behavioral measures. In this chapter, we present an overview of current methods for connecting neural codes to perception using precision optogenetics and psychophysical measurements of synthetically induced percepts. We also highlight new methodologies for validating precise control of optical and behavioral manipulations. Finally, we provide a perspective on upcoming developments that are poised to advance the field.
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Perceptual similarities between a specific stimulus and other stimuli of the same modality provide valuable information about the structure and geometry of sensory spaces. While typically assessed in human behavioral experiments, perceptual similarities-or distances-are rarely measured in other species. However, understanding the neural computations responsible for sensory representations requires the monitoring and often manipulation of neural activity, which is more readily achieved in non-human experimental models. Here, we develop a behavioral paradigm that enables the quantification of perceptual similarity between sensory stimuli using mouse olfaction as a model system.

When it comes to detecting volatile chemicals, biological olfactory systems far outperform all artificial chemical detection devices in their versatility, speed, and specificity. Consequently, the use of trained animals for chemical detection in security, defense, healthcare, agriculture, and other applications has grown astronomically. However, the use of animals in this capacity requires extensive training and behavior-based communication. Here we propose an alternative strategy, a bio-electronic nose, that capitalizes on the superior capability of the mammalian olfactory system, but bypasses behavioral output by reading olfactory information directly from the brain. We engineered a brain-computer interface that captures neuronal signals from an early stage of olfactory processing in awake mice combined with machine learning techniques to form a sensitive and selective chemical detector. We chronically implanted a grid electrode array on the surface of the mouse olfactory bulb and systematically recorded responses to a large battery of odorants and odorant mixtures across a wide range of concentrations. The bio-electronic nose has a comparable sensitivity to the trained animal and can detect odors on a variable background. We also introduce a novel genetic engineering approach that modifies the relative abundance of particular olfactory receptors in order to improve the sensitivity of our bio-electronic nose for specific chemical targets. Our recordings were stable over months, providing evidence for robust and stable decoding over time. The system also works in freely moving animals, allowing chemical detection to occur in real-world environments. Our bio-electronic nose outperforms current methods in terms of its stability, specificity, and versatility, setting a new standard for chemical detection.

A new study finds that mammalian olfaction may be far faster than previously thought. Mice can discriminate between olfactory stimuli that differ in fine temporal structure, at frequencies of up to 40 Hz. But how might mammals achieve high-bandwidth olfaction, and why?

Sensory systems transform the external world into time-varying spike trains. What features of spiking activity are used to guide behavior? In the mouse olfactory bulb, inhalation of different odors leads to changes in the set of neurons activated, as well as when neurons are activated relative to each other (synchrony) and the onset of inhalation (latency). To explore the relevance of each mode of information transmission, we probed the sensitivity of mice to perturbations across each stimulus dimension (i.e., rate, synchrony, and latency) using holographic two-photon optogenetic stimulation of olfactory bulb neurons with cellular and single-action-potential resolution. We found that mice can detect single action potentials evoked synchronously across <20 olfactory bulb neurons. Further, we discovered that detection depends strongly on the synchrony of activation across neurons, but not the latency relative to inhalation.

How does neural activity generate perception? Finding the combinations of spatial or temporal activity features (such as neuron identity or latency) that are consequential for perception remains challenging. We trained mice to recognize synthetic odors constructed from parametrically defined patterns of optogenetic activation, then measured perceptual changes during extensive and controlled perturbations across spatiotemporal dimensions. We modeled recognition as the matching of patterns to learned templates. The templates that best predicted recognition were sequences of spatially identified units, ordered by latencies relative to each other (with minimal effects of sniff). Within templates, individual units contributed additively, with larger contributions from earlier-activated units. Our synthetic approach reveals the fundamental logic of the olfactory code and provides a general framework for testing links between sensory activity and perception.