Dimensionality reduction beyond neural subspaces
Over the past decade, neural representations have been studied from the lens of low-dimensional subspaces defined by the co-activation of neurons. However, this view has overlooked other forms of covarying structure in neural activity, including i) condition-specific high-dimensional neural sequences, and ii) representations that change over time due to learning or drift. In this talk, I will present a new framework that extends the classic view towards additional types of covariability that are not constrained to a fixed, low-dimensional subspace. In addition, I will present sliceTCA, a new tensor decomposition that captures and demixes these different types of covariability to reveal task-relevant structure in neural activity. Finally, I will close with some thoughts regarding the circuit mechanisms that could generate mixed covariability. Together this work points to a need to consider new possibilities for how neural populations encode sensory, cognitive, and behavioral variables beyond neural subspaces.
Functional Divergence at the Mouse Bipolar Cell Terminal
Research in our lab focuses on the circuit mechanisms underlying sensory computation. We use the mouse retina as a model system because it allows us to stimulate the circuit precisely with its natural input, patterns of light, and record its natural output, the spike trains of retinal ganglion cells. We harness the power of genetic manipulations and detailed information about cell types to uncover new circuits and discover their role in visual processing. Our methods include electrophysiology, computational modeling, and circuit tracing using a variety of imaging techniques.
Integrators in short- and long-term memory
The accumulation and storage of information in memory is a fundamental computation underlying animal behavior. In many brain regions and task paradigms, ranging from motor control to navigation to decision-making, such accumulation is accomplished through neural integrator circuits that enable external inputs to move a system’s population-wide patterns of neural activity along a continuous attractor. In the first portion of the talk, I will discuss our efforts to dissect the circuit mechanisms underlying a neural integrator from a rich array of anatomical, physiological, and perturbation experiments. In the second portion of the talk, I will show how the accumulation and storage of information in long-term memory may also be described by attractor dynamics, but now within the space of synaptic weights rather than neural activity. Altogether, this work suggests a conceptual unification of seemingly distinct short- and long-term memory processes.
Learning to aggress – Behavioral and circuit mechanisms of aggression reward
Aggression is an ethologically complex behavior with equally complex underlying mechanisms. Here, I present data on one form of aggression, appetitive or rewarding aggression, and the behavioral, cellular and system-level mechanisms guiding this behavior. First, I will present one way in which appetitive aggression is modeled in mice, and extend aggression motivation to the concept of compulsive aggression seeking and relapse. I will then briefly highlight recent advances in computer vision and machine learning for automated scoring of aggressive behavior, the role of specific cell-types in controlling aggression reward, and close with preliminary data on the whole brain aggression reward functional connectome using light sheet fluorescent microscopy (LSFM).
Cortical estimation of current and future bodily states
Interoception, the sense of internal bodily signals, is essential for physiological homeostasis, cognition, and emotions. Human neuroimaging studies suggest insular cortex plays a central role in interoception, yet the cellular and circuit mechanisms of its involvement remain unclear. We developed a microprism-based cellular imaging approach to monitor insular cortex activity in behaving mice across different physiological need states. We combine this imaging approach with manipulations of peripheral physiology, circuit-mapping, cell type-specific and circuit-specific manipulation approaches to investigate the underlying circuit mechanisms. I will present our recent data investigating insular cortex activity during two physiological need states – hunger and thirst. These wereinduced naturally by caloric/fluid deficiency, or artificially by activation of specific hypothalamic “hunger neurons” and “thirst neurons”. We found that insular cortex ongoing activity faithfully represents current physiological state, independently of behavior or arousal levels. In contrast, transient responses to learned food- or water-predicting cues reflect a population-level “simulation” of future predicted satiety. Together with additional circuit-mapping and manipulation experiments, our findings suggest that insular cortex integrates visceral-sensory inputs regarding current physiological state with hypothalamus-gated amygdala inputs signaling availability of food/water. This way, insular cortex computes a prediction of future physiological state that can be used to guide behavioral choice.
Theme and variations: circuit mechanisms of behavioural evolution
Animals exhibit extraordinary variation in their behaviour, yet little is known about the neural mechanisms that generate this diversity. My lab has been taking advantage of the rapid diversification of male courtship behaviours in Drosophila to gain insight into how evolution shapes the nervous system to generate species-specific behaviours. By translating neurogenetic tools from D. melanogaster to closely related Drosophila species, we have begun to directly compare the homologous neural circuits and pinpoint sites of adaptive change. Across species, P1 interneurons serve as a conserved and key node in regulating male courtship: these neurons are selectively activated by the sensory cues carried by an appropriate mate and their activation triggers enduring courtship displays. We have been examining how different sensory pathways converge onto P1 neurons to regulate a male’s state of arousal, honing his pursuit of a prospective partner. Moreover, by performing cross-species comparison of these circuits, we have begun to gain insight into how reweighting of sensory inputs to P1 neurons underlies species-specific mate recognition. Our results suggest how variation at flexible nodes within the nervous system can serve as a substrate for behavioural evolution, shedding light on the types of changes that are possible and preferable within brain circuits.