Bird
bird
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.
Rethinking Attention: Dynamic Prioritization
Decades of research on understanding the mechanisms of attentional selection have focused on identifying the units (representations) on which attention operates in order to guide prioritized sensory processing. These attentional units fit neatly to accommodate our understanding of how attention is allocated in a top-down, bottom-up, or historical fashion. In this talk, I will focus on attentional phenomena that are not easily accommodated within current theories of attentional selection – the “attentional platypuses,” as they allude to an observation that within biological taxonomies the platypus does not fit into either mammal or bird categories. Similarly, attentional phenomena that do not fit neatly within current attentional models suggest that current models need to be revised. I list a few instances of the ‘attentional platypuses” and then offer a new approach, the Dynamically Weighted Prioritization, stipulating that multiple factors impinge onto the attentional priority map, each with a corresponding weight. The interaction between factors and their corresponding weights determines the current state of the priority map which subsequently constrains/guides attention allocation. I propose that this new approach should be considered as a supplement to existing models of attention, especially those that emphasize categorical organizations.
Basal Ganglia in Songbirds
Adaptation via innovation in the animal kingdom
Over the course of evolution, the human race has achieved a number of remarkable innovations, that have enabled us to adapt to and benefit from the environment ever more effectively. The ongoing environmental threats and health disasters of our world have now made it crucial to understand the cognitive mechanisms behind innovative behaviours. In my talk, I will present two research projects with examples of innovation-based behavioural adaptation from the taxonomic kingdom of animals, serving as a comparative psychological model for mapping the evolution of innovation. The first project focuses on the challenge of overcoming physical disability. In this study, we investigated an injured kea (Nestor notabilis) that exhibits an efficient, intentional, and innovative tool-use behaviour to compensate his disability, showing evidence for innovation-based adaptation to a physical disability in a non-human species. The second project focuses on the evolution of fire use from a cognitive perspective. Fire has been one of the most dominant ecological forces in human evolution; however, it is still unknown what capabilities and environmental factors could have led to the emergence of fire use. In the core study of this project, we investigated a captive population of Japanese macaques (Macaca fuscata) that has been regularly exposed to campfires during the cold winter months for over 60 years. Our results suggest that macaques are able to take advantage of the positive effects of fire while avoiding the dangers of flames and hot ashes, and exhibit calm behaviour around the bonfire. In addition, I will present a research proposal targeting the foraging behaviour of predatory birds in parts of Australia frequently affected by bushfires. Anecdotal reports suggest that some birds use burning sticks to spread the flames, a behaviour that has not been scientifically observed and evaluated. In summary, the two projects explore innovative behaviours along three different species groups, three different habitats, and three different ecological drivers, providing insights into the cognitive and behavioural mechanisms of adaptation through innovation.
Intrinsic Geometry of a Combinatorial Sensory Neural Code for Birdsong
Understanding the nature of neural representation is a central challenge of neuroscience. One common approach to this challenge is to compute receptive fields by correlating neural activity with external variables drawn from sensory signals. But these receptive fields are only meaningful to the experimenter, not the organism, because only the experimenter has access to both the neural activity and knowledge of the external variables. To understand neural representation more directly, recent methodological advances have sought to capture the intrinsic geometry of sensory driven neural responses without external reference. To date, this approach has largely been restricted to low-dimensional stimuli as in spatial navigation. In this talk, I will discuss recent work from my lab examining the intrinsic geometry of sensory representations in a model vocal communication system, songbirds. From the assumption that sensory systems capture invariant relationships among stimulus features, we conceptualized the space of natural birdsongs to lie on the surface of an n-dimensional hypersphere. We computed composite receptive field models for large populations of simultaneously recorded single neurons in the auditory forebrain and show that solutions to these models define convex regions of response probability in the spherical stimulus space. We then define a combinatorial code over the set of receptive fields, realized in the moment-to-moment spiking and non-spiking patterns across the population, and show that this code can be used to reconstruct high-fidelity spectrographic representations of natural songs from evoked neural responses. Notably, we find that topological relationships among combinatorial codewords directly mirror acoustic relationships among songs in the spherical stimulus space. That is, the time-varying pattern of co-activity across the neural population expresses an intrinsic representational geometry that mirrors the natural, extrinsic stimulus space. Combinatorial patterns across this intrinsic space directly represent complex vocal communication signals, do not require computation of receptive fields, and are in a form, spike time coincidences, amenable to biophysical mechanisms of neural information propagation.
A multi-level account of hippocampal function in concept learning from behavior to neurons
A complete neuroscience requires multi-level theories that address phenomena ranging from higher-level cognitive behaviors to activities within a cell. Unfortunately, we don't have cognitive models of behavior whose components can be decomposed into the neural dynamics that give rise to behavior, leaving an explanatory gap. Here, we decompose SUSTAIN, a clustering model of concept learning, into neuron-like units (SUSTAIN-d; decomposed). Instead of abstract constructs (clusters), SUSTAIN-d has a pool of neuron-like units. With millions of units, a key challenge is how to bridge from abstract constructs such as clusters to neurons, whilst retaining high-level behavior. How does the brain coordinate neural activity during learning? Inspired by algorithms that capture flocking behavior in birds, we introduce a neural flocking learning rule to coordinate units that collectively form higher-level mental constructs ("virtual clusters"), neural representations (concept, place and grid cell-like assemblies), and parallels recurrent hippocampal activity. The decomposed model shows how brain-scale neural populations coordinate to form assemblies encoding concept and spatial representations, and why many neurons are required for robust performance. Our account provides a multi-level explanation for how cognition and symbol-like representations are supported by coordinated neural assemblies formed through learning.
The evolution of computation in the brain: Insights from studying the retina
The retina is probably the most accessible part of the vertebrate central nervous system. Its computational logic can be interrogated in a dish, from patterns of lights as the natural input, to spike trains on the optic nerve as the natural output. Consequently, retinal circuits include some of the best understood computational networks in neuroscience. The retina is also ancient, and central to the emergence of neurally complex life on our planet. Alongside new locomotor strategies, the parallel evolution of image forming vision in vertebrate and invertebrate lineages is thought to have driven speciation during the Cambrian. This early investment in sophisticated vision is evident in the fossil record and from comparing the retina’s structural make up in extant species. Animals as diverse as eagles and lampreys share the same retinal make up of five classes of neurons, arranged into three nuclear layers flanking two synaptic layers. Some retina neuron types can be linked across the entire vertebrate tree of life. And yet, the functions that homologous neurons serve in different species, and the circuits that they innervate to do so, are often distinct to acknowledge the vast differences in species-specific visuo-behavioural demands. In the lab, we aim to leverage the vertebrate retina as a discovery platform for understanding the evolution of computation in the nervous system. Working on zebrafish alongside birds, frogs and sharks, we ask: How do synapses, neurons and networks enable ‘function’, and how can they rearrange to meet new sensory and behavioural demands on evolutionary timescales?
Foraging for the future: Food caching in squirrels and birds
Recurrent brainstem-forebrain loops in the control of vocal production in songbirds
Neural representations of space in the hippocampus of a food-caching bird
Spatial memory in vertebrates requires brain regions homologous to the mammalian hippocampus. Between vertebrate clades, however, these regions are anatomically distinct and appear to produce different spatial patterns of neural activity. We asked whether hippocampal activity is fundamentally different even between distant vertebrates that share a strong dependence on spatial memory. We studied tufted titmice – food-caching birds capable of remembering many concealed food locations. We found mammalian-like neural activity in the titmouse hippocampus, including sharp-wave ripples and anatomically organized place cells. In a non-food-caching bird species, spatial firing was less informative and was exhibited by fewer neurons. These findings suggest that hippocampal circuit mechanisms are similar between birds and mammals, but that the resulting patterns of activity may vary quantitatively with species-specific ethological needs.
NMC4 Event: NMC For Kids
We at Neuromatch 4.0 wish to open up science conferences to everyone and that is why we have included a session for kids and the young at heart. The NMC for kids has three excellent speakers from around the globe to talk about the balance system from bird butts to space: 1. Birds balance with their butts” by Bing Wen Brunton (Associate Prof of Biology at University of Washington, Seattle) 2. “The brain in motion” by Jenifer L. Campos (Associate Prof, University of Toronto) 3. “Getting ready for Mars: what happens to the brain in space?” By Elisa R Ferre (Senior Lecturer, Birkbeck University of London)
Predator-prey interactions: the avian visual sensory perspective
My research interests are centered on animal ecology, and more specifically include the following areas: visual ecology, behavioral ecology, and conservation biology, as well as the interactions between them. My research is question-driven. I answer my questions in a comprehensive manner, using a combination of empirical, theoretical, and comparative approaches. My model species are usually birds, but I have also worked with fish, mammals, amphibians, and insects. I was fortunate to enrich my education by attending Universities in different parts of the world. I did my undergraduate, specialized in ecology and biodiversity, at the "Universidad Nacional de Cordoba", Argentina. My Ph.D. was in animal ecology and conservation biology at the "Universidad Complutense de Madrid", Spain. My two post-docs were focused on behavioral ecology; the first one at University of Oxford (United Kingdom), and the second one at University of Minnesota (USA). I was an Assistant Professor at California State University Long Beach for almost six years. I am now a Full Professor of Biological Sciences at Purdue University.
GED: A flexible family of versatile methods for hypothesis-driven multivariate decompositions
Does that title put you to sleep or pique your interest? The goal of my presentation is to introduce a powerful yet under-utilized mathematical equation that is surprisingly effective at uncovering spatiotemporal patterns that are embedded in data -- but that might be inaccessible in traditional analysis methods due to low SNR or sparse spatial distribution. If you flunked calculus, then don't worry: the math is really easy, and I'll spend most of the time discussing intuition, simulations, and applications in real data. I will also spend some time in the beginning of the talk providing a bird's-eye-view of the empirical research in my lab, which focuses on mesoscale brain dynamics associated with error monitoring and response competition.
A fresh look at the bird retina
I am working on the vertebrate retina, with a main focus on the mouse and bird retina. Currently my work is focused on three major topics: Functional and molecular analysis of electrical synapses in the retina Circuitry and functional role of retinal interneurons: horizontal cells Circuitry for light-dependent magnetoreception in the bird retina Electrical synapses Electrical synapses (gap junctions) permit fast transmission of electrical signals and passage of metabolites by means of channels, which directly connect the cytoplasm of adjoining cells. A functional gap junction channel consists of two hemichannels (one provided by each of the cells), each comprised of a set of six protein subunits, termed connexins. These building blocks exist in a variety of different subtypes, and the connexin composition determines permeability and gating properties of a gap junction channel, thereby enabling electrical synapses to meet a diversity of physiological requirements. In the retina, various connexins are expressed in different cell types. We study the cellular distribution of different connexins as well as the modulation induced by transmitter action or change of ambient light levels, which leads to altered electrical coupling properties. We are also interested in exploiting them as therapeutic avenue for retinal degeneration diseases. Horizontal cells Horizontal cells receive excitatory input from photoreceptors and provide feedback inhibition to photoreceptors and feedforward inhibition to bipolar cells. Because of strong electrical coupling horizontal cells integrate the photoreceptor input over a wide area and are thought to contribute to the antagonistic organization of bipolar cell and ganglion cell receptive fields and to tune the photoreceptor–bipolar cell synapse with respect to the ambient light conditions. However, the extent to which this influence shapes retinal output is unclear, and we aim to elucidate the functional importance of horizontal cells for retinal signal processing by studying various transgenic mouse models. Retinal circuitry for light-dependent magnetoreception in the bird We are studying which neuronal cell types and pathways in the bird retina are involved in the processing of magnetic signals. Likely, magnetic information is detected in cryptochrome-expressing photoreceptors and leaves the retina through ganglion cell axons that project via the thalamofugal pathway to Cluster N, a part of the visual wulst essential for the avian magnetic compass. Thus, we aim to elucidate the synaptic connections and retinal signaling pathways from putatively magnetosensitive photoreceptors to thalamus-projecting ganglion cells in migratory birds using neuroanatomical and electrophysiological techniques.
The 2021 Annual Bioengineering Lecture + Bioinspired Guidance, Navigation and Control Symposium
Join the Department of Bioengineering on the 26th May at 9:00am for The 2021 Annual Bioengineering Lecture + Bioinspired Guidance, Navigation and Control Symposium. This year’s lecture speaker will be distinguished bioengineer and neuroscientist Professor Mandyam V. Srinivasan AM FRS, from the University of Queensland. Professor Srinivasan studies visual systems, particularly those of bees and birds. His research has revealed how flying insects negotiate narrow gaps, regulate the height and speed of flight, estimate distance flown, and orchestrate smooth landings. Apart from enhancing fundamental knowledge, these findings are leading to novel, biologically inspired approaches to the design of guidance systems for unmanned aerial vehicles with applications in the areas of surveillance, security and planetary exploration. Following Professor Srinivasan’s lecture will be the Bioinspired GNC Mini Symposium with guest speakers from Google Deepmind, Imperial College London, the University of Würzburg and the University of Konstanz giving talks on their research into autonomous robot navigation, neural mechanisms of compass orientation in insects and computational approaches to motor control.
Cellular homologies of birdsong control circuits
Variability, maintenance and learning in birdsong
The songbird zebra finch is an exemplary model system in which to study trial-and-error learning, as the bird learns its single song gradually through the production of many noisy renditions. It is also a good system in which to study the maintenance of motor skills, as the adult bird actively maintains its song and retains some residual plasticity. Motor learning occurs through the association of timing within the song, represented by sparse firing in nucleus HVC, with motor output, driven by nucleus RA. Here we show through modeling that the small level of observed variability in HVC can result in a network which is more easily able to adapt to change, and is most robust to cell damage or death, than an unperturbed network. In collaboration with Carlos Lois’ lab, we also consider the effect of directly perturbing HVC through viral injection of toxins that affect the firing of projection neurons. Following these perturbations, the song is profoundly affected but is able to almost perfectly recover. We characterize the changes in song acoustics and syntax, and propose models for HVC architecture and plasticity that can account for some of the observed effects. Finally, we suggest a potential role for inputs from nucleus Uva in helping to control timing precision in HVC.
Foraging at the Limit: Cognitive capabilities of birds and bees
Young IBRO NextInNeuro Webinar - The retinal basis of colour vision: from fish to humans
Colour vision is based on circuit-level comparison of the signals from spectral distinct types of photoreceptors. In our own eyes, the presence of three types of cones enable trichromatic colour vision. However, many phylogenetically ‘older’ vertebrates have four or more cone types, and in almost all their cases the circuits that enable tetra- or possibly even pentachromatic colour vision are not known. This includes the majority of birds, reptiles, amphibians, and bony fish. In the lab we study neuronal circuits for colour vision in non-mammalian vertebrates, with a focus on zebrafish, a tetrachromatic surface dwelling species of teleost. I will discuss how in the case of zebrafish, retinal colour computations are implemented in a fundamentally different, and probably much more efficient way compared to how they are thought to work in humans. I will then highlight how these fish circuits might be linked with those in mammals, possibly providing a new way of thinking about how circuits for colour vision are organized in vertebrates.
Why birds are smart
Brain Awareness Week by IIT Gandhinagar
The Brain Awareness Week by the Centre for Cognitive and Brain Sciences, IIT Gandhinagar spans across 7 days and invites you for a series of talks, panel discussions, competitions and workshops on topics ranging from 'Using songbirds to understand how the brain initiates movements' to 'Cognitive Science and UX in Game Design' by speakers from prestigious Indian and International institutes. Explore the marvels of the brain by joining us on 15th March. Free Registration.
Neural Mechanisms of Coordination in Duetting Wrens
To communicate effectively, two individuals must take turns to prevent overlap in their signals. How does the nervous system coordinate vocalizations between two individuals? Female and male plain-tailed wrens sing a duet in which they alternate syllable production so rapidly and precisely it sounds as if a single bird is singing. I will talk about experiments that examine the interaction between sensory cues and motor activity, using behavioral manipulations and neurophysiological recordings from pairs of awake, duetting wrens. I will show evidence that auditory cues link the brains of the wrens by modulating motor circuits.
Low dimensional models and electrophysiological experiments to study neural dynamics in songbirds
Birdsong emerges when a set of highly interconnected brain areas manage to generate a complex output. The similarities between birdsong production and human speech have positioned songbirds as unique animal models for studying learning and production of this complex motor skill. In this work, we developed a low dimensional model for a neural network in which the variables were the average activities of different neural populations within the nuclei of the song system. This neural network is active during production, perception and learning of birdsong. We performed electrophysiological experiments to record neural activity from one of these nuclei and found that the low dimensional model could reproduce the neural dynamics observed during the experiments. Also, this model could reproduce the respiratory motor patterns used to generate song. We showed that sparse activity in one of the neural nuclei could drive a more complex activity downstream in the neural network. This interdisciplinary work shows how low dimensional neural models can be a valuable tool for studying the emergence of complex motor tasks
A journey through connectomics: from manual tracing to the first fully automated basal ganglia connectomes
The "mind of the worm", the first electron microscopy-based connectome of C. elegans, was an early sign of where connectomics is headed, followed by a long time of little progress in a field held back by the immense manual effort required for data acquisition and analysis. This changed over the last few years with several technological breakthroughs, which allowed increases in data set sizes by several orders of magnitude. Brain tissue can now be imaged in 3D up to a millimeter in size at nanometer resolution, revealing tissue features from synapses to the mitochondria of all contained cells. These breakthroughs in acquisition technology were paralleled by a revolution in deep-learning segmentation techniques, that equally reduced manual analysis times by several orders of magnitude, to the point where fully automated reconstructions are becoming useful. Taken together, this gives neuroscientists now access to the first wiring diagrams of thousands of automatically reconstructed neurons connected by millions of synapses, just one line of program code away. In this talk, I will cover these developments by describing the past few years' technological breakthroughs and discuss remaining challenges. Finally, I will show the potential of automated connectomics for neuroscience by demonstrating how hypotheses in reinforcement learning can now be tackled through virtual experiments in synaptic wiring diagrams of the songbird basal ganglia.
Male songbirds turn off their self-evaluation systems when they sing to females
Attending to mistakes while practicing alone provides opportunities for learning but self-evaluation during audience-directed performance could distract from ongoing execution. It remains unknown how animals switch between practice and performance modes, and how evaluation systems process errors across distinct performance contexts. We recorded from striatal-projecting dopamine (DA) neurons as male songbirds transitioned from singing alone to singing female-directed courtship song. In the presence of the female, singing-related performance error signals were reduced or gated off and DA neurons were instead phasically activated by female vocalizations. Mesostriatal DA neurons can thus dynamically change their tuning with changes in social context.
Neuroscience Investigations in the Virgin Lands of African Biodiversity
Africa is blessed with a rich diversity and abundance in rodent and avian populations. This natural endowment on the continent portends research opportunities to study unique anatomical profiles and investigate animal models that may confer better neural architecture to study neurodegenerative diseases, adult neurogenesis, stroke and stem cell therapies. To this end, African researchers are beginning to pay closer attention to some of her indigenous rodents and birds in an attempt to develop spontaneous laboratory models for homegrown neuroscience-based research. For this presentation, I will be showing studies in our lab, involving cellular neuroanatomy of two rodents, the African giant rat (AGR) and Greater cane rat (GCR), Eidolon Bats (EB) and also the Striped Owl (SO). Using histological stains (Cresyl violet and Rapid Golgi) and immunohistochemical biomarkers (GFAP, NeuN, CNPase, Iba-1, Collagen 2, Doublecortin, Ki67, Calbindin, etc), and Electron Microscopy, morphology and functional organizations of neuronal and glial populations of the AGR , GCR, EB and SO brains have been described, with our work ongoing. In addition, the developmental profiles of the prenatal GCR brains have been chronicled across its entire gestational period. Brains of embryos/foetuses were harvested for gross morphological descriptions and then processed using immunofluorescence biomarkers to determine the pattern, onset, duration and peak of neurogenesis (Pax6, Tbr1, Tbr2, NF, HuCD, MAP2) and the onset and peak of glial cell expressions and myelination in the prenatal GCR. The outcome of these research efforts has shown unique neuroanatomical expressions and networks amongst Africa’s rich biodiversity. It is hopeful that continuous effort in this regard will provide sufficient basic research data on neural developments and cellular neuroanatomy with subsequent translational consequences.
The evolutionary origins of cortical cell types
In the last 500 million years, the dorsal telencephalon changed like no other region of the vertebrate brain. Differences range from the six-layered neocortex of mammals, to the small three-layered cortex of reptiles, and the complete absence of lamination in birds. These anatomical differences have prompted endless discussions on the origins and evolution of the cerebral cortex. We have approached this problem from a cell type and transcriptomics perspective. This reveals a more granular picture, where different cell types and classes have followed independent trajectories of evolutionary change. In this presentation, I will discuss how the molecular analysis of cell types in the brains of turtles, lizards and amphibians is updating our views on the evolution of the cerebral cortex, and the new questions emerging from these results.
Neural control of vocal interactions in songbirds
During conversations we rapidly switch between listening and speaking which often requires withholding or delaying our speech in order to hear others and avoid overlapping. This capacity for vocal turn-taking is exhibited by non-linguistic species as well, however the neural circuit mechanisms that enable us to regulate the precise timing of our vocalizations during interactions are unknown. We aim to identify the neural mechanisms underlying the coordination of vocal interactions. Therefore, we paired zebra finches with a vocal robot (1Hz call playback) and measured the bird’s call response times. We found that individual birds called with a stereotyped delay in respect to the robot call. Pharmacological inactivation of the premotor nucleus HVC revealed its necessity for the temporal coordination of calls. We further investigated the contributing neural activity within HVC by performing intracellular recordings from premotor neurons and inhibitory interneurons in calling zebra finches. We found that inhibition is preceding excitation before and during call onset. To test whether inhibition guides call timing we pharmacologically limited the impact of inhibition on premotor neurons. As a result zebra finches converged on a similar delay time i.e. birds called more rapidly after the vocal robot call suggesting that HVC inhibitory interneurons regulate the coordination of social contact calls. In addition, we aim to investigate the vocal turn-taking capabilities of the common nightingale. Male nightingales learn over 100 different song motifs which are being used in order to attract mates or defend territories. Previously, it has been shown that nightingales counter-sing with each other following a similar temporal structure to human vocal turn-taking. These animals are also able to spontaneously imitate a motif of another nightingale. The neural mechanisms underlying this behaviour are not yet understood. In my lab, we further probe the capabilities of these animals in order to access the dynamic range of their vocal turn taking flexibility.
Neural code for episodic memories in a food-caching bird
Bernstein Conference 2024
Contextual motor learning in birdsong reflects two distinct neural processes
COSYNE 2022
Dual pathway architecture in songbirds boosts sensorimotor learning
COSYNE 2022
Engagement of the respiratory CPG for songbird vocalizations
COSYNE 2022
Temporal Dynamics in an Attractor Model of the Songbird’s Premotor Nucleus.
COSYNE 2022
Temporal Dynamics in an Attractor Model of the Songbird’s Premotor Nucleus.
COSYNE 2022
A new tool for automated annotation of complex birdsong reveals dynamics of canary syntax rules
COSYNE 2022
A new tool for automated annotation of complex birdsong reveals dynamics of canary syntax rules
COSYNE 2022
Hippocampal coding during visual deliberation in freely moving birds
COSYNE 2023
Thalamic maintenance of a complex sequential learned behavior: birdsong
COSYNE 2023
Decoding Temporal Features of Birdsong Through Neural Activity Analysis
COSYNE 2025
TweetyBERT, a self-supervised vision transformer to automate birdsong annotation
COSYNE 2025
Neuronal activity in avian basal ganglia-cortical loop related to birdsong acoustic variation in zebra finches
FENS Forum 2024
A novel songbird model of autism
FENS Forum 2024
A novel view on basal ganglia pathways: Insights from synaptic-resolution connectomics in songbirds
FENS Forum 2024