
Ann KennedyNorthwestern University | NU
Ann Kennedy
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Publications (50)
The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does this neural heterogeneity influence macroscopic neural dynamics, and how might it contribute to neural computation? In this work, we use a mean-field model to investigate computation in h...
Neurons are the basic information-encoding units in the brain. In contrast to information-encoding units in a computer, neurons are heterogeneous, i.e. they differ substantially in their electrophysiological properties. How does neural heterogeneity a effect the function of neural circuits? We derive and analyze a mathematical model of networks of...
The mathematical study of real-world dynamical systems relies on models composed of differential equations. Numerical methods for solving and analyzing differential equation systems are essential when complex biological problems have to be studied, such as the spreading of a virus, the evolution of competing species in an ecosystem, or the dynamics...
Objective. Intracortical brain–computer interfaces (iBCIs) aim to enable individuals with paralysis to control the movement of virtual limbs and robotic arms. Because patients’ paralysis prevents training a direct neural activity to limb movement decoder, most iBCIs rely on ‘observation-based’ decoding in which the patient watches a moving cursor w...
Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the 'decoder' at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requi...
Mean-field theory links the physiological properties of individual neurons to the emergent dynamics of neural population activity. These models provide an essential tool for studying brain function at different scales; however, for their application to neural populations on large scale, they need to account for differences between distinct neuron t...
Systems of differential equations are commonly used to model real-world dynamical systems. In most cases, numerical methods are needed to study these systems. There are many freely available software solutions that implement numerical methods for dynamical systems analysis. However, these different software solutions have different requirements for...
The hypothalamus regulates innate social behaviors, including mating and aggression. These behaviors can be evoked by optogenetic stimulation of specific neuronal subpopulations within MPOA and VMHvl, respectively. Here, we perform dynamical systems modeling of population neuronal activity in these nuclei during social behaviors. In VMHvl, unsuperv...
Quantifying motion in 3D is important for studying the behavior of humans and other animals, but manual pose annotations are expensive and time-consuming to obtain. Self-supervised keypoint discovery is a promising strategy for estimating 3D poses without annotations. However, current keypoint discovery approaches commonly process single 2D views a...
Quantifying motion in 3D is important for studying the behavior of humans and other animals, but manual pose annotations are expensive and time-consuming to obtain. Self-supervised keypoint discovery is a promising strategy for estimating 3D poses without annotations. However, current keypoint discovery approaches commonly process single 2D views a...
Intracortical brain-computer interfaces (iBCIs) enable paralyzed persons to generate movement, but current methods require large amounts of both neural and movement-related data to be collected from the iBCI user for supervised decoder training. We hypothesized that the low-dimensional latent neural representations of motor behavior, known to be pr...
Mean-field theory links the physiological properties of individual neurons to the emergent dynamics of neural population activity. These models provide an essential tool for studying brain function at different scales; however, for their application to neural populations on large scale, they need to account for differences between distinct neuron t...
Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the “decoder” at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requi...
Real-world behavior is often shaped by complex interactions between multiple agents. To scalably study multi-agent behavior, advances in unsupervised and self-supervised learning have enabled a variety of different behavioral representations to be learned from trajectory data. To date, there does not exist a unified set of benchmarks that can enabl...
Real-world behavior is often shaped by complex interactions between multiple agents. To scalably study multi-agent behavior, advances in unsupervised and self-supervised learning have enabled a variety of different behavioral representations to be learned from trajectory data. To date, there does not exist a unified set of benchmarks that can enabl...
The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does neural heterogeneity affect macroscopic neural dynamics and how does it contribute to neurodynamic functions? In this letter, we address these questions by studying the macroscopic dynami...
In the past few years, advances in machine learning have fueled an explosive growth of descriptive and generative behavior models of animal behavior. These new approaches offer higher levels of detail and granularity than has previously been possible, allowing for fine-grained segmentation of animals' actions and precise quantitative mappings betwe...
We propose a method for learning the posture and structure of agents from unlabelled behavioral videos. Starting from the observation that behaving agents are generally the main sources of movement in behavioral videos, our method, Behavioral Keypoint Discovery (B-KinD), uses an encoder-decoder architecture with a geometric bottleneck to reconstruc...
The hypothalamus plays a key role in regulating innate behaviors. It is widely believed to function as a system of "labeled lines", containing behavior-specific neurons with characteristic transcriptomic and connectomic profiles. This view however fails to explain why, although activation of estrogen receptor-1 (Esr1) expressing neurons in the vent...
Ingested food and water stimulate sensory systems in the oropharyngeal and gastrointestinal areas before absorption1,2. These sensory signals modulate brain appetite circuits in a feed-forward manner3–5. Emerging evidence suggests that osmolality sensing in the gut rapidly inhibits thirst neurons upon water intake. Nevertheless, it remains unclear...
In this issue of Neuron, Hattori and Komiyama, 2021 unravel persistent neural encoding of value in mouse retrosplenial cortex, using a demixed dimensionality reduction algorithm. The cylindrical structure they uncover supports untangled encoding of value in both brains and RNNs.
We propose a method for learning the posture and structure of agents from unlabelled behavioral videos. Starting from the observation that behaving agents are generally the main sources of movement in behavioral videos, our method uses an encoder-decoder architecture with a geometric bottleneck to reconstruct the difference between video frames. By...
We propose a method for learning the posture and structure of agents from unlabelled behavioral videos. Starting from the observation that behaving agents are generally the main sources of movement in behavioral videos, our method uses an encoder-decoder architecture with a geometric bottleneck to reconstruct the difference between video frames. By...
Jellyfish are radially symmetric organisms without a brain that arose more than 500 million years ago. They achieve organismal behaviors through coordinated interactions between autonomously functioning body parts. Jellyfish neurons have been studied electrophysiologically, but not at the systems level. We introduce Clytia hemisphaerica as a transp...
We present a framework for the unsupervised learning of neurosymbolic encoders, i.e., encoders obtained by composing neural networks with symbolic programs from a domain-specific language. Such a framework can naturally incorporate symbolic expert knowledge into the learning process and lead to more interpretable and factorized latent representatio...
We present a framework for the unsupervised learning of neurosymbolic encoders, i.e., encoders obtained by composing neural networks with symbolic programs from a domain-specific language. Such a framework can naturally incorporate symbolic expert knowledge into the learning process and lead to more interpretable and factorized latent representatio...
Hand-annotated data can vary due to factors such as subjective differences, intra-rater variability, and differing annotator expertise. We study annotations from different experts who labelled the same behavior classes on a set of animal behavior videos, and observe a variation in annotation styles. We propose a new method using program synthesis t...
Hand-annotated data can vary due to factors such as subjective differences, intra-rater variability, and differing annotator expertise. We study annotations from different experts who labelled the same behavior classes on a set of animal behavior videos, and observe a variation in annotation styles. We propose a new method using program synthesis t...
Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from domain experts. This issue arises prominently in automated behavior analysis, in which agent movements or actions of interest are detected from video tracking data. To reduce annotation...
Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social Interactions (CalMS21) Dataset. Our dataset consists of the social interactions between freely behaving mice in a standard resident-intruder assay. The CalMS21 dataset i...
Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social Interactions (CalMS21) Dataset. Our dataset consists of trajectory data of social interactions, recorded from videos of freely behaving mice in a standard resident-intru...
Jellyfish are free-swimming, radially symmetric organisms with complex behaviors that arise from coordinated interactions between distinct, autonomously functioning body parts. This behavioral complexity evolved without a corresponding cephalization of the nervous system. The systems-level neural mechanisms through which such decentralized control...
Jellyfish are free-swimming, radially symmetric organisms with complex behaviors that arise from coordinated interactions between distinct, autonomously functioning body parts. This behavioral complexity evolved without a corresponding cephalization of the nervous system. The systems-level neural mechanisms through which such decentralized control...
A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-03143-1
Animal behaviours that are superficially similar can express different intents in different contexts, but how this flexibility is achieved at the level of neural circuits is not understood. For example, males of many species can exhibit mounting behaviour towards same- or opposite-sex conspecifics, but it is unclear whether the intent and neural en...
Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from domain experts. This issue arises prominently in automated behavior analysis, in which agent movements or actions of interest are detected from video tracking data. To reduce annotation...
Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from domain experts. This issue arises prominently in automated behavior analysis, in which agent movements or actions of interest are detected from video tracking data. To reduce annotation...
By building a richer behavioral vocabulary, Wiltschko et al. tease apart subtle differences in how pharmacological agents affect animal behavior, mapping on- and off-target effects of drugs with improved precision.
Persistent neural activity in cortical, hippocampal, and motor networks has been described as mediating working memory for transiently encountered stimuli1,2. Internal emotional states, such as fear, also persist following exposure to an inciting stimulus³, but it is unclear whether slow neural dynamics are involved in this process. Neurons in the...
The study of naturalistic social behavior requires quantification of animals’ interactions. This is generally done through manual annotation—a highly time consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely-behaving animals. However, automatically and accurately classifying complex social b...
The study of naturalistic social behavior requires quantification of animals’ interactions. This is generally done through manual annotation—a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. However, automatically and accurately classifying complex social b...
Persistent neural activity has been described in cortical, hippocampal, and motor networks as mediating short-term working memory of transiently encountered stimuli. Internal emotion states such as fear also exhibit persistence following exposure to an inciting stimulus, but such persistence is typically attributed to circulating stress hormones; w...
Persistent neural activity has been described in cortical, hippocampal, and motor networks as mediating short-term working memory of transiently encountered stimuli. Internal emotion states such as fear also exhibit persistence following exposure to an inciting stimulus, but such persistence is typically attributed to circulating stress hormones; w...
Innate behaviors involve both reflexive motor programs and internal states. In Drosophila, optogenetic activation of male-specific P1 interneurons triggers courtship song, as well as a persistent behavioral state that prolongs courtship and enhances aggressiveness. Here we identify pCd neurons as persistently activated by repeated P1 stimulation. p...
Innate behaviors involve both reflexive motor programs and internal states. In Drosophila, optogenetic activation of male-specific P1 interneurons triggers courtship song, as well as a persistent behavioral state that prolongs courtship and enhances aggressiveness. Here we identify pCd neurons as persistently activated by repeated P1 stimulation. p...
All animals possess a repertoire of innate (or instinctive) behaviours, which can be performed without training. Whether such behaviours are mediated by anatomically distinct and/or genetically specified neural pathways remains unknown. Here we report that neural representations within the mouse hypothalamus, that underlie innate social behaviours,...
Significance
Accurate, quantitative measurement of animal social behaviors is critical, not only for researchers in academic institutions studying social behavior and related mental disorders, but also for pharmaceutical companies developing drugs to treat disorders affecting social interactions, such as autism and schizophrenia. Here we describe a...
Social interactions, such as an aggressive encounter between two conspecific males or a mating encounter between a male and a female, typically progress from an initial appetitive or motivational phase, to a final consummatory phase. This progression involves both changes in the intensity of the animals' internal state of arousal or motivation and...