About

Prof. Bartlett W. Mel received his B.S. in Electrical Engineering and Computer Science from the University of California at Berkeley in 1983 and his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1989. He spent five years as a post-doctoral fellow at Caltech working in the laboratory of Professor Christof Koch. He joined the Department of Biomedical Engineering at USC in the Fall of 1994, where he established the Laboratory for Neural Computation. Prof. Mel is a member of the Neuroscience Graduate Program and holds a joint appointment in the Department of Psychology. His research interests lie in Computational Neuroscience and Neural Engineering, focusing on using computer models to study brain function. His work involves detailed biophysical modeling to explore synaptic integration in active dendritic trees and their contribution to sensory and memory functions of nerve cells. He employs simulation packages such as NEURON and custom software developed by his lab members. Additionally, his research combines scientific and engineering goals, particularly in understanding the massively parallel computations in the visual cortex that enable rapid, accurate, and robust object recognition. His ongoing projects aim to understand the brain's mechanisms for learning features for recognition and to develop high-performance artificial vision systems for intelligent machines.

Research topics

• Computer Science
• Artificial Intelligence
• Neuroscience
• Biology
• Mathematics
• Computer vision
• Biological system
• Mathematical analysis
• Psychology

Selected publications

• ShapeY: A Principled Framework for Measuring Shape Recognition Capacity via Nearest-Neighbor Matching

  arXiv (Cornell University) · 2026-04-27

  preprintOpen accessSenior author

  Object recognition (OR) in humans relies heavily on shape cues and the ability to recognize objects across varying 3D viewpoints. Unlike humans, deep networks often rely on non-shape cues such as texture and background, leading to vulnerabilities in generalization and robustness. To address this gap, we introduce ShapeY, a novel and principled benchmarking framework designed to evaluate shape-based recognition capability in OR systems. ShapeY comprises 68,200 grayscale images of 200 3D objects rendered from multiple viewpoints and optionally subjected to non-shape ``appearance'' changes. Using a nearest-neighbor matching task, ShapeY specifically probes the fine-grained structure of an OR system's embedding space by evaluating whether object views are clustered by 3D shape similarity across varying 3D viewpoints and other non-shape changes. ShapeY provides a suite of quantitative and qualitative performance readouts, including error rate graphs, viewpoint tuning curves, histograms of positive and negative matching scores, and grids showing ordered best matches, which together offer a comprehensive evaluation of an OR system's shape understanding capability. Testing of 321 pre-trained networks with diverse architectures reveals significant challenges in achieving robust shape-based recognition: even state-of-the-art models struggle to generalize consistently across 3D viewpoint and appearance changes, and are prone to infrequent but egregious matches of objects of obviously completely different shape. ShapeY establishes a principled framework for advancing artificial vision systems toward human-like shape recognition capabilities, emphasizing the importance of disentangled and invariant object encodings.

  PublisherDOI

• ShapeY: A Principled Framework for Measuring Shape Recognition Capacity via Nearest-Neighbor Matching

  ArXiv.org · 2026-04-27

  articleOpen accessSenior author

  Object recognition (OR) in humans relies heavily on shape cues and the ability to recognize objects across varying 3D viewpoints. Unlike humans, deep networks often rely on non-shape cues such as texture and background, leading to vulnerabilities in generalization and robustness. To address this gap, we introduce ShapeY, a novel and principled benchmarking framework designed to evaluate shape-based recognition capability in OR systems. ShapeY comprises 68,200 grayscale images of 200 3D objects rendered from multiple viewpoints and optionally subjected to non-shape ``appearance'' changes. Using a nearest-neighbor matching task, ShapeY specifically probes the fine-grained structure of an OR system's embedding space by evaluating whether object views are clustered by 3D shape similarity across varying 3D viewpoints and other non-shape changes. ShapeY provides a suite of quantitative and qualitative performance readouts, including error rate graphs, viewpoint tuning curves, histograms of positive and negative matching scores, and grids showing ordered best matches, which together offer a comprehensive evaluation of an OR system's shape understanding capability. Testing of 321 pre-trained networks with diverse architectures reveals significant challenges in achieving robust shape-based recognition: even state-of-the-art models struggle to generalize consistently across 3D viewpoint and appearance changes, and are prone to infrequent but egregious matches of objects of obviously completely different shape. ShapeY establishes a principled framework for advancing artificial vision systems toward human-like shape recognition capabilities, emphasizing the importance of disentangled and invariant object encodings.

  PublisherOA PDF

• Classical-Contextual Interactions in V1 May Rely on Dendritic Computations

  Neuroscience · 2022 · 4 citations

  Senior authorCorresponding
  • Computer Science
  • Neuroscience
  • Computer Science
  PublisherOA PDFDOI

• Object Boundary Detection in Natural Images May Depend on “Incitatory” Cell–Cell Interactions

  Journal of Neuroscience · 2022

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Simple cells in primary visual cortex (V1) respond to oriented edges and have long been supposed to detect object boundaries, yet the prevailing model of a simple cell-a divisively normalized linear filter-is a surprisingly poor natural boundary detector. To understand why, we analyzed image statistics on and off object boundaries, allowing us to characterize the neural-style computations needed to perform well at this difficult natural classification task. We show that a simple circuit motif known to exist in V1 is capable of extracting high-quality boundary probability signals from local populations of simple cells. Our findings suggest a new, more general way of conceptualizing cell-cell interconnections in the cortex.

  PublisherOA PDFDOI

• Optimizing a Neuron for Reliable Dendritic Subunit Pooling

  Neuroscience · 2021 · 4 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Neuroscience
  PublisherDOI

• ShapeY: Measuring Shape Recognition Capacity Using Nearest Neighbor Matching

  arXiv (Cornell University) · 2021-11-16

  preprintOpen accessSenior author

  Object recognition in humans depends primarily on shape cues. We have developed a new approach to measuring the shape recognition performance of a vision system based on nearest neighbor view matching within the system's embedding space. Our performance benchmark, ShapeY, allows for precise control of task difficulty, by enforcing that view matching span a specified degree of 3D viewpoint change and/or appearance change. As a first test case we measured the performance of ResNet50 pre-trained on ImageNet. Matching error rates were high. For example, a 27 degree change in object pitch led ResNet50 to match the incorrect object 45% of the time. Appearance changes were also highly disruptive. Examination of false matches indicates that ResNet50's embedding space is severely "tangled". These findings suggest ShapeY can be a useful tool for charting the progress of artificial vision systems towards human-level shape recognition capabilities.

  PublisherOA PDFDOI

• How Dendrites Affect Online Recognition Memory

  PLoS Computational Biology · 2019-05-03 · 11 citations

  articleOpen accessSenior authorCorresponding

  In order to record the stream of autobiographical information that defines our unique personal history, our brains must form durable memories from single brief exposures to the patterned stimuli that impinge on them continuously throughout life. However, little is known about the computational strategies or neural mechanisms that underlie the brain's ability to perform this type of "online" learning. Based on increasing evidence that dendrites act as both signaling and learning units in the brain, we developed an analytical model that relates online recognition memory capacity to roughly a dozen dendritic, network, pattern, and task-related parameters. We used the model to determine what dendrite size maximizes storage capacity under varying assumptions about pattern density and noise level. We show that over a several-fold range of both of these parameters, and over multiple orders-of-magnitude of memory size, capacity is maximized when dendrites contain a few hundred synapses-roughly the natural number found in memory-related areas of the brain. Thus, in comparison to entire neurons, dendrites increase storage capacity by providing a larger number of better-sized learning units. Our model provides the first normative theory that explains how dendrites increase the brain's capacity for online learning; predicts which combinations of parameter settings we should expect to find in the brain under normal operating conditions; leads to novel interpretations of an array of existing experimental results; and provides a tool for understanding which changes associated with neurological disorders, aging, or stress are most likely to produce memory deficits-knowledge that could eventually help in the design of improved clinical treatments for memory loss.

  PublisherDOI

• NMDA spikes mediate amplification of odor pathway information in the piriform cortex

  bioRxiv (Cold Spring Harbor Laboratory) · 2018-06-13 · 1 citations

  preprintOpen accessCorresponding

  Abstract The piriform cortex (PCx) receives direct input from the olfactory bulb (OB) and is the brain’s main station for odor recognition and memory. The transformation of the odor code from OB to PCx is profound: mitral and tufted cells in olfactory glomeruli respond to individual odorant molecules, whereas pyramidal neurons (PNs) in the PCx responds to multiple, apparently random combinations of activated glomeruli. How these “discontinuous” receptive fields are formed from OB inputs remains unknown. Counter to the prevailing view that olfactory PNs sum their inputs passively, we show for the first time that NMDA spikes within individual dendrites can both amplify OB inputs and impose combination selectivity upon them, while their ability to compartmentalize voltage signals allows different dendrites to represent different odorant combinations. Thus, the 2-layer integrative behavior of olfactory PN dendrites provides a parsimonious account for the nonlinear remapping of the odor code from bulb to cortex.

  PublisherOA PDFDOI

• NMDA spikes mediate amplification of inputs in the rat piriform cortex

  eLife · 2018-12-21 · 58 citations

  articleOpen access

  The piriform cortex (PCx) receives direct input from the olfactory bulb (OB) and is the brain's main station for odor recognition and memory. The transformation of the odor code from OB to PCx is profound: mitral and tufted cells in olfactory glomeruli respond to individual odorant molecules, whereas pyramidal neurons (PNs) in the PCx responds to multiple, apparently random combinations of activated glomeruli. How these 'discontinuous' receptive fields are formed from OB inputs remains unknown. Counter to the prevailing view that olfactory PNs sum their inputs passively, we show for the first time that NMDA spikes within individual dendrites can both amplify OB inputs and impose combination selectivity upon them, while their ability to compartmentalize voltage signals allows different dendrites to represent different odorant combinations. Thus, the 2-layer integrative behavior of olfactory PN dendrites provides a parsimonious account for the nonlinear remapping of the odor code from bulb to cortex.

  PublisherOA PDFDOI

• Object boundary detection in natural images may depend on ‘incitatory’ cell-cell interactions

  bioRxiv (Cold Spring Harbor Laboratory) · 2018-10-07 · 2 citations

  preprintOpen accessSenior author

  Abstract Detecting object boundaries is crucial for recognition, but how the process unfolds in visual cortex remains unknown. To study the problem faced by a hypothetical boundary cell, and to predict how cortical circuitry could produce a boundary cell from a population of conventional “simple cells”, we labeled 30,000 natural image patches and used Bayes’ rule to help determine how a simple cell should influence a nearby boundary cell depending on its relative offset in receptive field position and orientation. We identified three basic types of cell-cell interactions: rising and falling interactions with a range of slopes and saturation rates, as well as non-monotonic (bump-shaped) interactions with varying modes and amplitudes. Using simple models we show that a ubiquitous cortical circuit motif consisting of direct excitation and indirect inhibition – a compound effect we call “incitation” – can produce the entire spectrum of simple cell-boundary cell interactions found in our dataset. Moreover, we show that the synaptic weights that parameterize an incitation circuit can be learned by a single-layer “delta” rule. We conclude that incitatory interconnections are a generally useful computing mechanism that the cortex may exploit to help solve difficult natural classification problems. Significance statement: Simple cells in primary visual cortex (V1) respond to oriented edges, and have long been supposed to detect object boundaries, yet the prevailing model of a simple cell – a divisively normalized linear filter – is a surprisingly poor natural boundary detector. To understand why, we analyzed image statistics on and off object boundaries, allowing us to characterize the neural-style computations needed to perform well at this difficult natural classification task. We show that a simple circuit motif known to exist in V1 is capable of extracting high-quality boundary probability signals from local populations of simple cells. Our findings suggest a new, more general way of conceptualizing cell-cell interconnections in the cortex.

  PublisherOA PDFDOI

Recent grants

• Anatomical, Physiological, and Modeling Studies of Memory-Related Neural Form and Function

  NSF · $626k · 2006–2010

• NIH Grant R01MH065918

  NIH · $1.3M · 2009

Frequent coauthors

• Chaithanya Ramachandra

  Eyenuk (United States)

  11 shared

• Alon Poleg-Polsky

  University of Colorado Anschutz Medical Campus

  9 shared

• Jackie Schiller

  Technion – Israel Institute of Technology

  9 shared

• Bardia F. Behabadi

  Qualcomm (United States)

  9 shared

• Panayiota Poirazi

  9 shared

• Kevin A. Archie

  Washington University in St. Louis

  6 shared

• Daniel Ruderman

  4 shared

• Jong Woo Nam

  Harvard University

  4 shared

Education

• Ph.D., Biomedical Engineering

  University of Southern California

  1990

• M.S., Biomedical Engineering

  University of Southern California

  1986

• B.S., Biomedical Engineering

  University of Southern California

  1984

Awards & honors

• 1998 NSF NSF Career Award
• 1992 McDonnell Pew McDonnell Pew Fellowship
• 1990 NIH National Research Service Award
• 1987 University of Illinois Cognitive Science/AI Fellowship
• 1983 Hewlett-Packard Hewlett-Packard Faculty Development Fel…