Topic: Animal behavior

Briefing Chat: Orcas smashed a sunfish to bits — researchers aren’t sure why

Nature staff discuss a large survey of insect diversity in the Amazon and unusual orca behavior captured on video involving a sunfish.

Understanding reward-guided learning using large-scale datasets

Understanding the neural mechanisms of reward-guided learning is a long-standing goal of computational neuroscience. Recent methodological innovations enable us to collect ever larger neural and behavioral datasets. This presents opportunities to achieve greater understanding of learning in the brain at scale, as well as methodological challenges. In the first part of the talk, I will discuss our recent insights into the mechanisms by which zebra finch songbirds learn to sing. Dopamine has been long thought to guide reward-based trial-and-error learning by encoding reward prediction errors. However, it is unknown whether the learning of natural behaviours, such as developmental vocal learning, occurs through dopamine-based reinforcement. Longitudinal recordings of dopamine and bird songs reveal that dopamine activity is indeed consistent with encoding a reward prediction error during naturalistic learning. In the second part of the talk, I will talk about recent work we are doing at DeepMind to develop tools for automatically discovering interpretable models of behavior directly from animal choice data. Our method, dubbed CogFunSearch, uses LLMs within an evolutionary search process in order to "discover" novel models in the form of Python programs that excel at accurately predicting animal behavior during reward-guided learning. The discovered programs reveal novel patterns of learning and choice behavior that update our understanding of how the brain solves reinforcement learning problems.

Understanding reward-guided learning using large-scale datasets

Understanding the neural mechanisms of reward-guided learning is a long-standing goal of computational neuroscience. Recent methodological innovations enable us to collect ever larger neural and behavioral datasets. This presents opportunities to achieve greater understanding of learning in the brain at scale, as well as methodological challenges. In the first part of the talk, I will discuss our recent insights into the mechanisms by which zebra finch songbirds learn to sing. Dopamine has been long thought to guide reward-based trial-and-error learning by encoding reward prediction errors. However, it is unknown whether the learning of natural behaviours, such as developmental vocal learning, occurs through dopamine-based reinforcement. Longitudinal recordings of dopamine and bird songs reveal that dopamine activity is indeed consistent with encoding a reward prediction error during naturalistic learning. In the second part of the talk, I will talk about recent work we are doing at DeepMind to develop tools for automatically discovering interpretable models of behavior directly from animal choice data. Our method, dubbed CogFunSearch, uses LLMs within an evolutionary search process in order to "discover" novel models in the form of Python programs that excel at accurately predicting animal behavior during reward-guided learning. The discovered programs reveal novel patterns of learning and choice behavior that update our understanding of how the brain solves reinforcement learning problems.

Mitochondrial diversity in the mouse and human brain

The basis of the mind, of mental states, and complex behaviors is the flow of energy through microscopic and macroscopic brain structures. Energy flow through brain circuits is powered by thousands of mitochondria populating the inside of every neuron, glial, and other nucleated cell across the brain-body unit. This seminar will cover emerging approaches to study the mind-mitochondria connection and present early attempts to map the distribution and diversity of mitochondria across brain tissue. In rodents, I will present convergent multimodal evidence anchored in enzyme activities, gene expression, and animal behavior that distinct behaviorally-relevant mitochondrial phenotypes exist across large-scale mouse brain networks. Extending these findings to the human brain, I will present a developing systematic biochemical and molecular map of mitochondrial variation across cortical and subcortical brain structures, representing a foundation to understand the origin of complex energy patterns that give rise to the human mind.

Learning produces a hippocampal cognitive map in the form of an orthogonalized state machine

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task representation that mirrored improved behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent struture of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such orthogonalized representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.

Self-perception: mechanosensation and beyond

Brain-organ communications play a crucial role in maintaining the body's physiological and psychological homeostasis, and are controlled by complex neural and hormonal systems, including the internal mechanosensory organs. However, the progress has been slow due to technical hurdles: the sensory neurons are deeply buried inside the body and are not readily accessible for direct observation, the projection patterns from different organs or body parts are complex rather than converging into dedicate brain regions, the coding principle cannot be directly adapted from that learned from conventional sensory pathways. Our lab apply the pipeline of "biophysics of receptors-cell biology of neurons-functionality of neural circuits-animal behaviors" to explore the molecular and neural mechanisms of self-perception. In the lab, we mainly focus on the following three questions: 1, The molecular and cellular basis for proprioception and interoception. 2, The circuit mechanisms of sensory coding and integration of internal and external information. 3, The function of interoception in regulating behavior homeostasis.

All for one? Consequences and challenges of group foraging

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.

Interpersonal synchrony of body/brain, Solo & Team Flow

Flow is defined as an altered state of consciousness with excessive attention and enormous sense of pleasure, when engaged in a challenging task, first postulated by a psychologist, the late M. Csikszentmihayli. The main focus of this talk will be “Team Flow,” but there were two lines of previous studies in our laboratory as its background. First is inter-body and inter-brain coordination/synchrony between individuals. Considering various rhythmic echoing/synchronization phenomena in animal behavior, it could be regarded as the biological, sub-symbolic and implicit origin of social interactions. The second line of precursor research is on the state of Solo Flow in game playing. We employed attenuation of AEP (Auditory Evoked Potential) to task-irrelevant sound probes as an objective-neural indicator of such a Flow status, and found that; 1) Mutual link between the ACC & the TP is critical, and 2) overall, top-down influence is enhanced while bottom-up causality is attenuated. Having these as the background, I will present our latest study of Team Flow in game playing. We found that; 3) the neural correlates of Team Flow is distinctively different from those of Solo Flow nor of non-flow social, 4) the left medial temporal cortex seems to form an integrative node for Team Flow, receiving input related to Solo Flow state from the right PFC and input related to social state from the right IFC, and 5) Intra-brain (dis)similarity of brain activity well predicts (dis)similarity of skills/cognition as well as affinity for inter-brain coherence.

The Geometry of Decision-Making

Choosing among spatially distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges, to choosing with whom to associate. Here, using an integrated theoretical and experimental approach (employing immersive Virtual Reality), with both invertebrate and vertebrate models—the fruit fly, desert locust and zebrafish—we consider the recursive interplay between movement and collective vectorial integration in the brain during decision-making regarding options (potential ‘targets’) in space. We reveal that the brain repeatedly breaks multi-choice decisions into a series of abrupt (critical) binary decisions in space-time where organisms switch, spontaneously, from averaging vectorial information among, to suddenly excluding one of, the remaining options. This bifurcation process repeats until only one option—the one ultimately selected—remains. Close to each bifurcation the ‘susceptibility’ of the system exhibits a sharp increase, inevitably causing small differences among the remaining options to become amplified; a property that both comes ‘for free’ and is highly desirable for decision-making. This mechanism facilitates highly effective decision-making, and is shown to be robust both to the number of options available, and to context, such as whether options are static (e.g. refuges) or mobile (e.g. other animals). In addition, we find evidence that the same geometric principles of decision-making occur across scales of biological organisation, from neural dynamics to animal collectives, suggesting they are fundamental features of spatiotemporal computation.

Expectation of self-generated sounds drives predictive processing in mouse auditory cortex

Sensory stimuli are often predictable consequences of one’s actions, and behavior exerts a correspondingly strong influence over sensory responses in the brain. Closed-loop experiments with the ability to control the sensory outcomes of specific animal behaviors have revealed that neural responses to self-generated sounds are suppressed in the auditory cortex, suggesting a role for prediction in local sensory processing. However, it is unclear whether this phenomenon derives from a precise movement-based prediction or how it affects the neural representation of incoming stimuli. We address these questions by designing a behavioral paradigm where mice learn to expect the predictable acoustic consequences of a simple forelimb movement. Neuronal recordings from auditory cortex revealed suppression of neural responses that was strongest for the expected tone and specific to the time of the sound-associated movement. Predictive suppression in the auditory cortex was layer-specific, preceded by the arrival of movement information, and unaffected by behavioral relevance or reward association. These findings illustrate that expectation, learned through motor-sensory experience, drives layer-specific predictive processing in the mouse auditory cortex.

Measuring behavior to measure the brain

Animals produce behavior by responding to a mixture of cues that arise both externally (sensory) and internally (neural dynamics and states). These cues are continuously produced and can be combined in different ways depending on the needs of the animal. However, the integration of these external and internal cues remains difficult to understand in natural behaviors. To address this gap, we have developed an unsupervised method to identify internal states from behavioral data, and have applied it to the study of a dynamic social interaction. During courtship, Drosophila melanogaster males pattern their songs using cues from their partner. This sensory-driven behavior dynamically modulates courtship directed at their partner. We use our unsupervised method to identify how the animal integrates sensory information into distinct underlying states. We then use this to identify the role of courtship neurons in either integrating incoming information or directing the production of the song, roles that were previously hidden. Our results reveal how animals compose behavior from previously unidentified internal states, a necessary step for quantitative descriptions of animal behavior that link environmental cues, internal needs, neuronal activity, and motor outputs.

Vision outside of the visual system (in Drosophila)

We seek to understand the control of behavior – by animals, their brains, and their neurons. Reiser and his team are focused on the fly visual system, using modern methods from the Drosophila toolkit to understand how visual pathways are involved in specific behaviors. Due to the recent connectomics explosion, they now study the brain-wide networks organizing visual information for behavior control. The team combines explorations of visually guided behaviors with functional investigations of specific cell types throughout the fly brain. The Reiser lab actively develops and disseminates new methods and instruments enabling increasingly precise quantification of animal behavior.

Using machine vision and learning to analyze animal behavior

On cognitive maps and reinforcement learning in large-scale animal behaviour

Bats are extreme aviators and amazing navigators. Many bat species nightly commute dozens of kilometres in search of food, and some bat species annually migrate over thousands of kilometres. Studying bats in their natural environment has always been extremely challenging because of their small size (mostly <50 gr) and agile nature. We have recently developed novel miniature technology allowing us to GPS-tag small bats, thus opening a new window to document their behaviour in the wild. We have used this technology to track fruit-bats pups over 5 months from birth to adulthood. Following the bats’ full movement history allowed us to show that they use novel short-cuts which are typical for cognitive-map based navigation. In a second study, we examined how nectar-feeding bats make foraging decisions under competition. We show that by relying on a simple reinforcement learning strategy, the bats can divide the resource between them without aggression or communication. Together, these results demonstrate the power of the large scale natural approach for studying animal behavior.

Neural dynamics underlying temporal inference

Animals possess the ability to effortlessly and precisely time their actions even though information received from the world is often ambiguous and is inadvertently transformed as it passes through the nervous system. With such uncertainty pervading through our nervous systems, we could expect that much of human and animal behavior relies on inference that incorporates an important additional source of information, prior knowledge of the environment. These concepts have long been studied under the framework of Bayesian inference with substantial corroboration over the last decade that human time perception is consistent with such models. We, however, know little about the neural mechanisms that enable Bayesian signatures to emerge in temporal perception. I will present our work on three facets of this problem, how Bayesian estimates are encoded in neural populations, how these estimates are used to generate time intervals, and how prior knowledge for these tasks is acquired and optimized by neural circuits. We trained monkeys to perform an interval reproduction task and found their behavior to be consistent with Bayesian inference. Using insights from electrophysiology and in silico models, we propose a mechanism by which cortical populations encode Bayesian estimates and utilize them to generate time intervals. Thereafter, I will present a circuit model for how temporal priors can be acquired by cerebellar machinery leading to estimates consistent with Bayesian theory. Based on electrophysiology and anatomy experiments in rodents, I will provide some support for this model. Overall, these findings attempt to bridge insights from normative frameworks of Bayesian inference with potential neural implementations for the acquisition, estimation, and production of timing behaviors.

The Dark Side of Vision: Resolving the Neural Code

All sensory information – like what we see, hear and smell – gets encoded in spike trains by sensory neurons and gets sent to the brain. Due to the complexity of neural circuits and the difficulty of quantifying complex animal behavior, it has been exceedingly hard to resolve how the brain decodes these spike trains to drive behavior. We now measure quantal signals originating from sparse photons through the most sensitive neural circuits of the mammalian retina and correlate the retinal output spike trains with precisely quantified behavioral decisions. We utilize a combination of electrophysiological measurements on the most sensitive ON and OFF retinal ganglion cell types and a novel deep-learning based tracking technology of the head and body positions of freely-moving mice. We show that visually-guided behavior relies on information from the retinal ON pathway for the dimmest light increments and on information from the retinal OFF pathway for the dimmest light decrements (“quantal shadows”). Our results show that the distribution of labor between ON and OFF pathways starts already at starlight supporting distinct pathway-specific visual computations to drive visually-guided behavior. These results have several fundamental consequences for understanding how the brain integrates information across parallel information streams as well as for understanding the limits of sensory signal processing. In my talk, I will discuss some of the most eminent consequences including the extension of this “Quantum Behavior” paradigm from mouse vision to monkey and human visual systems.

Linking dimensionality to computation in neural networks

The link between behavior, learning and the underlying connectome is a fundamental open problem in neuroscience. In my talk I will show how it is possible to develop a theory that bridges across these three levels (animal behavior, learning and network connectivity) based on the geometrical properties of neural activity. The central tool in my approach is the dimensionality of neural activity. I will link animal complex behavior to the geometry of neural representations, specifically their dimensionality; I will then show how learning shapes changes in such geometrical properties and how local connectivity properties can further regulate them. As a result, I will explain how the complexity of neural representations emerges from both behavioral demands (top-down approach) and learning or connectivity features (bottom-up approach). I will build these results regarding neural dynamics and representations starting from the analysis of neural recordings, by means of theoretical and computational tools that blend dynamical systems, artificial intelligence and statistical physics approaches.

Interactions between the microbiome and nervous system during early development

The gut microbiota is emerging as an important modulator of brain function and behavior, as several recent discoveries reveal substantial effects of the microbiome on neurophysiology, neuroimmunity and animal behavior. Despite these findings supporting a “microbiome-gut-brain axis”, the molecular and cellular mechanisms that underlie interactions between the gut microbiota and brain remain poorly understood. To uncover these, the Hsiao laboratory is mining the human microbiota for microbial modulators of host neuroactive molecules, investigating the impact of microbiota-immune system interactions on neurodevelopment and examining the microbiome as an interface between gene-environment interactions in neurological diseases. In particular, our research on effects of the maternal microbiome on offspring development in utero are revealing novel interactions between microbiome-dependent metabolites and fetal thalamocortical axonogenesis. Overall, we aim to dissect biological pathways for communication between the gut microbiota and nervous system, toward understanding fundamental interactions between physiological systems that impact brain and behavior.

On cognitive maps and reinforcement learning in large-scale animal behaviour

Bats are extreme aviators and amazing navigators. Many bat species nightly com-mute dozens of kilometres in search of food, and some bat species annually migrate over thousands of kilometres. Studying bats in their natural environment has al-ways been extremely challenging because of their small size (mostly <50 gr) and agile nature. We have recently developed novel miniature technology allowing us to GPS-tag small bats, thus opening a new window to document their behaviour in the wild. We have used this technology to track fruit-bats pups over 5 months from birth to adulthood. Following the bats’ full movement history allowed us to show that they use novel short-cuts which are typical for cognitive-map based naviga-tion. In a second study, we examined how nectar-feeding bats make foraging deci-sions under competition. We show that by relying on a simple reinforcement learn-ing strategy, the bats can divide the resource between them without aggression or communication. Together, these results demonstrate the power of the large scale natural approach for studying animal behavior.

Measuring the hidden dynamics of animal behavior

Neuroscience in the mud: interplay between lab and field research for understanding animal behavior

Investigations of the neurophysiological processes underlying animal behaviors are almost exclusively done inside the laboratory, typically using few animal models born and reared under artificially stabilized conditions. Yet, animals living in the wild have to cope with much complex and variable environments. Thus, while the laboratory provides the technical possibilities for physiological research, the field offers a more realistic perspective about the animal´s behavioral abilities. We study neural circuits underlying the visually guided prey and predator behaviors in a semiterrestrial crab. By combining lab and field experiments we have, for example, found that the level of predation risk experienced by the animals in the wild affects the responsiveness of identified neurons involved in the animal escape response. Using this and other results from my lab I will illustrate and discuss the importance of complementing lab with field studies in wild animals for understanding the neural mechanisms subserving behavior.

Motor Cortical Control of Vocal Interactions in a Neotropical Singing Mouse

Using sounds for social interactions is common across many taxa. Humans engaged in conversation, for example, take rapid turns to go back and forth. This ability to act upon sensory information to generate a desired motor output is a fundamental feature of animal behavior. How the brain enables such flexible sensorimotor transformations, for example during vocal interactions, is a central question in neuroscience. Seeking a rodent model to fill this niche, we are investigating neural mechanisms of vocal interaction in Alston’s singing mouse (Scotinomys teguina) – a neotropical rodent native to the cloud forests of Central America. We discovered sub-second temporal coordination of advertisement songs (counter-singing) between males of this species – a behavior that requires the rapid modification of motor outputs in response to auditory cues. We leveraged this natural behavior to probe the neural mechanisms that generate and allow fast and flexible vocal communication. Using causal manipulations, we recently showed that an orofacial motor cortical area (OMC) in this rodent is required for vocal interactions (Okobi*, Banerjee* et. al, 2019). Subsequently, in electrophysiological recordings, I find neurons in OMC that track initiation, termination and relative timing of songs. Interestingly, persistent neural dynamics during song progression stretches or compresses on every trial to match the total song duration (Banerjee et al, in preparation). These results demonstrate robust cortical control of vocal timing in a rodent and upends the current dogma that motor cortical control of vocal output is evolutionarily restricted to the primate lineage.

Neuroscience tools for the 99%: On the low-fi development of high-tech lab gear for hands-on neuroscience labs and exploratory research

The public has a fascination with the brain, but little attention is given to neuroscience education prior to graduate studies in brain-related fields. One reason may be the lack of low cost and engaging teaching materials. To address this, we have developed a suite of open-source tools which are appropriate for amateurs and for use in high school, undergraduate, and graduate level educational and research programs. This lecture will provide an overview of our mission to re-engineer research-grade lab equipment using first principles and will highlight basic principles of neuroscience in a "DIY" fashion: neurophysiology, functional electrical stimulation, micro-stimulation effect on animal behavior, neuropharmacology, even neuroprosthesis and optogenetics! Finally, with faculty academic positions becoming a scarce resource, I will discuss an alternative academic career path: entrepreneurship. It is possible to be an academic, do research, publish papers, present at conferences and train students all outside the traditional university setting. I will close by discussing my career path from graduate student to PI/CEO of a startup neuroscience company.