About

William DeBello is an Associate Professor in the Department of Neurobiology, Physiology and Behavior at the University of California, Davis. His research focuses on systems and sensory neuroscience, brain development, evolution, and plasticity. He is affiliated with the Center for Neuroscience and is based in Briggs Hall within the College of Biological Sciences. His work involves understanding the neural mechanisms underlying sensory processing and brain development, contributing to the broader field of neurobiology.

Research topics

• Computer Science
• Biology
• Neuroscience
• Artificial Intelligence
• Psychology

Selected publications

• MASCAF: A Cable Model Fitting Pipeline for Topologically Complex Surface Meshes

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-13

  articleOpen access

  Abstract We present a free and open-source, semi-automated, topologically robust pipeline for fitting cable models to 3D surface mesh morphology data of neuronal membranes, particularly suited to structures with complex shapes and topological holes. The motivation for this work is the discovery of morphologically complex neural spines on the auditory space-specific neurons of the barn owl (Tyto alba, Tyto furcata), dubbed “toric spines”, notable for their high curvature, branching density, and holes/loops. Multicompartmental simulation software requires morphology to be represented as cable models (e.g., SWC format), yet existing software tools for fitting cable models to complex 3D surface meshes have not produced satisfactory results for toric spines, and loops are generally unsupported. We present the Mesh and Skeleton Cable Fitting ( MASCAF ) pipeline and software, which fits a cable model (e.g., SWC format) to a surface mesh using mean-curvature flow skeletonization. In this paper, we demonstrate how MASCAF is applied to fit cable models, how loops can be reconstructed in simulations with the Arbor and NEURON simulation software, and how the results can be validated using geometry and simulator-based methods. While non-tree morphologies such as toric spines are neuroanatomically special, our software pipeline provides a cable-model fitting approach for surface mesh data that is topologically robust, deterministic, open-source, and applicable to general morphologies, thereby closing a crucial gap between neuronal imaging and high-resolution simulation.

  PublisherOA PDFDOI

• High-resolution computational simulation of sound localization neurons in the barn owl

  Open MIND · 2025-11-17

  article

  Abstract: The auditory pathways of the barn owl (Tyto alba) provide a unique system for studying neural coding and computation underlying hearing and sound localization. In this work, we develop high-resolution, morphologically accurate computational models of neurons in the avian midbrain for which we have broad image and functional data, but where models combining the two have yet to be implemented and studied. This project contributes to the broader research endeavor, informing our understanding of basic levels of neural computations and the potential prevention and treatment of auditory and communicative disorders in humans. Recent anatomical studies of space-specific neurons (SSNs) found in the inferior colliculus of the barn owl have used high-resolution electron (EM) and stimulated emission depletion (STED) microscopy techniques to reveal dense arborization and complex dendritic structures called toric spines. While the complex morphology of these structures is hypothesized to support frequency and binaural cue integration, the exact nature of this integration is unknown. We use this imaging data to develop morphologically accurate compartmental models using the Arbor simulation codebase, then optimize the model to be consistent with patch-clamp experiments using simulation-based inference, a combination of Bayesian statistical modeling and machine learning. In this way, we explore and constrain the electrophysiological parameterizations that are consistent with in vitro spiking data. The resulting model is used to infer the integration properties of SSNs, such as linearity or nonlinearity, and thus answer key questions about their nature and role in models of sound localization.

  DOI

• High-resolution computational simulation of sound localization neurons in the barn owl

  Zenodo (CERN European Organization for Nuclear Research) · 2025-11-17

  articleOpen access

  Abstract: The auditory pathways of the barn owl (Tyto alba) provide a unique system for studying neural coding and computation underlying hearing and sound localization. In this work, we develop high-resolution, morphologically accurate computational models of neurons in the avian midbrain for which we have broad image and functional data, but where models combining the two have yet to be implemented and studied. This project contributes to the broader research endeavor, informing our understanding of basic levels of neural computations and the potential prevention and treatment of auditory and communicative disorders in humans. Recent anatomical studies of space-specific neurons (SSNs) found in the inferior colliculus of the barn owl have used high-resolution electron (EM) and stimulated emission depletion (STED) microscopy techniques to reveal dense arborization and complex dendritic structures called toric spines. While the complex morphology of these structures is hypothesized to support frequency and binaural cue integration, the exact nature of this integration is unknown. We use this imaging data to develop morphologically accurate compartmental models using the Arbor simulation codebase, then optimize the model to be consistent with patch-clamp experiments using simulation-based inference, a combination of Bayesian statistical modeling and machine learning. In this way, we explore and constrain the electrophysiological parameterizations that are consistent with in vitro spiking data. The resulting model is used to infer the integration properties of SSNs, such as linearity or nonlinearity, and thus answer key questions about their nature and role in models of sound localization.

  PublisherDOI

• Noise correlations in the owl optic tectum are differentially impacted by unimodal vs crossmodal competitors

  bioRxiv (Cold Spring Harbor Laboratory) · 2022

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Neuroscience

  ABSTRACT Multisensory integration in mammalian superior colliculus and its avian homolog, the optic tectum, is essential for rapid orienting towards salient stimuli. The benefits of combining across modalities is expected to depend in part on interneuronal noise correlations (NCs) and trial-to-trial response variance (Fano factor; FF), yet there is scant data on these topics. Here we used multielectrode arrays (MEAs) to record simultaneously from cohorts of single units in the deep layers of the owl optic tectum (OTd). Stimuli were presented individually or simultaneously as spatially aligned or non-aligned competitors. NC values varied from -0.35 to 0.94, an unexpectedly large range, and decreased only modestly as receptive fields diverged. Spatially aligned bimodal stimuli summed in a largely additive fashion over a wide range of response magnitudes. Most of the observed variance in bimodal NCs and FFs could be accounted for by an additive rule without accounting for internal noise. For non-aligned stimuli, crossmodal competitors decreased FFs, whereas unimodal competitors did not. Most striking, visual competitors decreased NCs for auditory drivers but increased NCs for visual drivers, indicating that the OTd network is differentially wired to process bimodal competitors. In total, these data provide novel descriptive information regarding the correlational structure of OTd neurons underlying multisensory processing. SIGNIFICANCE STATEMENT In the owl auditory midbrain and its mammalian homologs, little is known regarding the ensemble response properties during multisensory integration. Using microelectrode arrays we found that noise correlations between pairs of neurons evoked by auditory, visual and bimodal stimuli were large and variable. When two non-aligned (competitor) stimuli were presented simultaneously, response reliability and noise correlations were driven down for crossmodal but not unimodal competitors, which suggests distinct processing strategies. This novel finding predicts that visual competitors drive an increase in localization accuracy for auditory targets.

  PublisherOA PDFDOI

• Diverse processing underlying frequency integration in midbrain neurons of barn owls

  PLoS Computational Biology · 2021 · 2 citations

  • Computer Science
  • Neuroscience
  • Biology

  Emergent response properties of sensory neurons depend on circuit connectivity and somatodendritic processing. Neurons of the barn owl's external nucleus of the inferior colliculus (ICx) display emergence of spatial selectivity. These neurons use interaural time difference (ITD) as a cue for the horizontal direction of sound sources. ITD is detected by upstream brainstem neurons with narrow frequency tuning, resulting in spatially ambiguous responses. This spatial ambiguity is resolved by ICx neurons integrating inputs over frequency, a relevant processing in sound localization across species. Previous models have predicted that ICx neurons function as point neurons that linearly integrate inputs across frequency. However, the complex dendritic trees and spines of ICx neurons raises the question of whether this prediction is accurate. Data from in vivo intracellular recordings of ICx neurons were used to address this question. Results revealed diverse frequency integration properties, where some ICx neurons showed responses consistent with the point neuron hypothesis and others with nonlinear dendritic integration. Modeling showed that varied connectivity patterns and forms of dendritic processing may underlie observed ICx neurons' frequency integration processing. These results corroborate the ability of neurons with complex dendritic trees to implement diverse linear and nonlinear integration of synaptic inputs, of relevance for adaptive coding and learning, and supporting a fundamental mechanism in sound localization.

  PublisherOA PDFDOI

• Toric Spines at a Site of Learning

  eNeuro · 2019-12-10 · 7 citations

  articleOpen accessSenior author

  Abstract We discovered a new type of dendritic spine. It is found on space-specific neurons in the barn owl inferior colliculus, a site of experience-dependent plasticity. Connectomic analysis revealed dendritic protrusions of unusual morphology including topological holes, hence termed “toric” spines ( n = 76). More significantly, presynaptic terminals converging onto individual toric spines displayed numerous active zones (up to 49) derived from multiple axons (up to 11) with incoming trajectories distributed widely throughout 3D space. This arrangement is suited to integrate input sources. Dense reconstruction of two toric spines revealed that they were unconnected with the majority (∼84%) of intertwined axons, implying a high capacity for information storage. We developed an ex vivo slice preparation and provide the first published data on space-specific neuron intrinsic properties, including cellular subtypes with and without toric-like spines. We propose that toric spines are a cellular locus of sensory integration and behavioral learning.

  PublisherOA PDFDOI

• Hunting Increases Phosphorylation of Calcium/Calmodulin-Dependent Protein Kinase Type II in Adult Barn Owls

  Neural Plasticity · 2015-01-01

  articleOpen accessSenior authorCorresponding

  Juvenile barn owls readily adapt to prismatic spectacles, whereas adult owls living under standard aviary conditions do not. We previously demonstrated that phosphorylation of the cyclic-AMP response element-binding protein (CREB) provides a readout of the instructive signals that guide plasticity in juveniles. Here we investigated phosphorylation of calcium/calmodulin-dependent protein kinase II (pCaMKII) in both juveniles and adults. In contrast to CREB, we found no differences in pCaMKII expression between prism-wearing and control juveniles within the external nucleus of the inferior colliculus (ICX), the major site of plasticity. For prism-wearing adults that hunted live mice and are capable of adaptation, expression of pCaMKII was increased relative to prism-wearing adults that fed passively on dead mice and are not capable of adaptation. This effect did not bear the hallmarks of instructive information: it was not localized to rostral ICX and did not exhibit a patchy distribution reflecting discrete bimodal stimuli. These data are consistent with a role for CaMKII as a permissive rather than an instructive factor. In addition, the paucity of pCaMKII expression in passively fed adults suggests that the permissive default setting is "off" in adults.

  PublisherOA PDFDOI

• Input clustering in the normal and learned circuits of adult barn owls

  Neurobiology of Learning and Memory · 2015-02-18 · 10 citations

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

• Input clustering and the microscale structure of local circuits

  Frontiers in Neural Circuits · 2014-09-12 · 42 citations

  reviewOpen access1st authorCorresponding

  The recent development of powerful tools for high-throughput mapping of synaptic networks promises major advances in understanding brain function. One open question is how circuits integrate and store information. Competing models based on random versus structured connectivity make distinct predictions regarding the dendritic addressing of synaptic inputs. In this article we review recent experimental tests of one of these models, the input clustering hypothesis. Across circuits, brain regions and species, there is growing evidence of a link between synaptic co-activation and dendritic location, although this finding is not universal. The functional implications of input clustering and future challenges are discussed.

  PublisherOA PDFDOI

• Auditory Processing, Plasticity, and Learning in the Barn Owl

  ILAR Journal · 2010-01-01 · 23 citations

  reviewOpen accessSenior author

  The human brain has accumulated many useful building blocks over its evolutionary history, and the best knowledge of these has often derived from experiments performed in animal species that display finely honed abilities. In this article we review a model system at the forefront of investigation into the neural bases of information processing, plasticity, and learning: the barn owl auditory localization pathway. In addition to the broadly applicable principles gleaned from three decades of work in this system, there are good reasons to believe that continued exploration of the owl brain will be invaluable for further advances in understanding of how neuronal networks give rise to behavior.

  PublisherOA PDFDOI

Recent grants

• From microscale structure to population coding of normal and learned behavior

  NIH · $3.3M · 2017–2023

• The synaptic basis of learning in the auditory system

  NIH · $3.6M · 2002–2016

Frequent coauthors

• George J Augustine

  73 shared

• Felix E. Schweizer

  57 shared

• Thomas Dresbach

  Universitätsmedizin Göttingen

  37 shared

• Heinrich Betz

  Max Planck Institute for Medical Research

  34 shared

• Vincent O’Connor

  University of Southampton

  33 shared

• Marie E. Burns

  University of California, Davis

  29 shared

• George J. Augustine

  Nanyang Technological University

  21 shared

• Samuel S.‐H. Wang

  Princeton University

  12 shared