Biomedical subjects

Tomaso Poggio

Publications and source records attributed to Tomaso Poggio.

15 recordsLinked to original sources

PubMed2007

Robust object recognition with cortex-like mechanisms.

We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating between a template matching and a maximum pooling operation. We demonstrate the strength of the approach on a range of recognition tasks: From invariant single object recognition in clutter to multiclass categorization problems and complex scene understanding tasks that rely on the recognition of both shape-based as well as texture-based objects. Given the biological constraints that the system had to satisfy, the approach performs surprisingly well: It has the capability of learning from only a few training examples and competes with state-of-the-art systems. We also discuss the existence of a universal, redundant dictionary of features that could handle the recognition of most object categories. In addition to its relevance for computer vision, the success of this approach suggests a plausibility proof for a class of feedforward models of object recognition in cortex.

Algorithms↗

PubMed2006

Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex.

Local field potentials (LFPs) arise largely from dendritic activity over large brain regions and thus provide a measure of the input to and local processing within an area. We characterized LFPs and their relationship to spikes (multi and single unit) in monkey inferior temporal cortex (IT). LFP responses in IT to complex objects showed strong selectivity at 44% of the sites and tolerance to retinal position and size. The LFP preferences were poorly predicted by the spike preferences at the same site but were better explained by averaging spikes within approximately 3 mm. A comparison of separate sites suggests that selectivity is similar on a scale of approximately 800 microm for spikes and approximately 5 mm for LFPs. These observations imply that inputs to IT neurons convey selectivity for complex shapes and that such input may have an underlying organization spanning several millimeters.

Action Potentials↗

PubMed2005

Experience-dependent sharpening of visual shape selectivity in inferior temporal cortex.

Whereas much is known about the visual shape selectivity of neurons in the inferior temporal cortex (ITC), less is known about the role of visual learning in the development and refinement of ITC shape selectivity. To address this, we trained monkeys to perform a visual categorization task with a parametric set of highly familiar stimuli. During training, the stimuli were always presented at the same orientation. In this experiment, we recorded from ITC neurons while monkeys viewed the trained stimuli in addition to image-plane rotated versions of those stimuli. We found that, concomitant with the monkeys' behavioral performance, neuronal stimulus selectivity was stronger for stimuli presented at the trained orientation than for rotated versions of the same stimuli. We also recorded from ITC neurons while monkeys viewed sets of novel and familiar (but not explicitly trained) randomly chosen complex stimuli. We again found that ITC stimulus selectivity was sharper for familiar than novel stimuli, suggesting that enhanced shape tuning in ITC can arise for both passively experienced and explicitly trained stimuli.

Algorithms↗

PubMed2005

Fast readout of object identity from macaque inferior temporal cortex.

Understanding the brain computations leading to object recognition requires quantitative characterization of the information represented in inferior temporal (IT) cortex. We used a biologically plausible, classifier-based readout technique to investigate the neural coding of selectivity and invariance at the IT population level. The activity of small neuronal populations (approximately 100 randomly selected cells) over very short time intervals (as small as 12.5 milliseconds) contained unexpectedly accurate and robust information about both object "identity" and "category." This information generalized over a range of object positions and scales, even for novel objects. Coarse information about position and scale could also be read out from the same population.

Action Potentials↗

PubMed2005

Identification and analysis of alternative splicing events conserved in human and mouse.

Alternative pre-mRNA splicing affects a majority of human genes and plays important roles in development and disease. Alternative splicing (AS) events conserved since the divergence of human and mouse are likely of primary biological importance, but relatively few of such events are known. Here we describe sequence features that distinguish exons subject to evolutionarily conserved AS, which we call alternative conserved exons (ACEs), from other orthologous human/mouse exons and integrate these features into an exon classification algorithm, acescan. Genome-wide analysis of annotated orthologous human-mouse exon pairs identified approximately 2,000 predicted ACEs. Alternative splicing was verified in both human and mouse tissues by using an RT-PCR-sequencing protocol for 21 of 30 (70%) predicted ACEs tested, supporting the validity of a majority of acescan predictions. By contrast, AS was observed in mouse tissues for only 2 of 15 (13%) tested exons that had EST or cDNA evidence of AS in human but were not predicted ACEs, and AS was never observed for 11 negative control exons in human or mouse tissues. Predicted ACEs were much more likely to preserve the reading frame and less likely to disrupt protein domains than other AS events and were enriched in genes expressed in the brain and in genes involved in transcriptional regulation, RNA processing, and development. Our results also imply that the vast majority of AS events represented in the human EST database are not conserved in mouse.

Alternative Splicing↗

PubMed2004

Tomaso Poggio.

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Career Choice↗

PubMed2004

Generalization in vision and motor control.

Learning is more than memory. It is not simply the building of a look-up table of labelled images, or a phone-directory-like list of motor acts and the corresponding sequences of muscle activation. Central to learning and intelligence is the ability to predict, that is, to generalize to new situations, beyond the memory of specific examples. The key to generalization, in turn, is the architecture of the system, more than the rules of synaptic plasticity. We propose a specific architecture for generalization for both the motor and the visual systems, and argue for a canonical microcircuit underlying visual and motor learning.

Animals↗

PubMed2004

Intracellular measurements of spatial integration and the MAX operation in complex cells of the cat primary visual cortex.

We have examined the spatial integration properties of complex cells to determine whether some of their responses can be described by a maximum operation (MAX)-like computation, as suggested by Riesenhuber and Poggio's model of object recognition. Membrane potential was recorded from anesthetized cats while optimally oriented bars were presented, either alone or in pairs, in different parts of the cells' receptive field. In most cells, the membrane potential response to two bars presented simultaneously could not be predicted by the sum of the responses to individual bars. In many cells, however, the responses closely approximated a MAX-like model. That is, the response of the cell to two bars was similar to the larger of the two individual responses ("soft-MAX"). The degree of nonlinear summation varied from cell to cell and varied within single cells from one stimulus configuration to another but on average fit most closely to the MAX model. The firing response of the cells was also well predicted by the MAX-like model. The MAX-like behavior was independent of the distance between the bars (orthogonal to the preferred orientation), independent of the relative amplitude of the responses, and slightly less pronounced at low levels of contrast. This MAX-like behavior of a subset of complex cells may play an important role in invariant object recognition in clutter.

Animals↗

PubMed2004

General conditions for predictivity in learning theory.

Developing theoretical foundations for learning is a key step towards understanding intelligence. 'Learning from examples' is a paradigm in which systems (natural or artificial) learn a functional relationship from a training set of examples. Within this paradigm, a learning algorithm is a map from the space of training sets to the hypothesis space of possible functional solutions. A central question for the theory is to determine conditions under which a learning algorithm will generalize from its finite training set to novel examples. A milestone in learning theory was a characterization of conditions on the hypothesis space that ensure generalization for the natural class of empirical risk minimization (ERM) learning algorithms that are based on minimizing the error on the training set. Here we provide conditions for generalization in terms of a precise stability property of the learning process: when the training set is perturbed by deleting one example, the learned hypothesis does not change much. This stability property stipulates conditions on the learning map rather than on the hypothesis space, subsumes the classical theory for ERM algorithms, and is applicable to more general algorithms. The surprising connection between stability and predictivity has implications for the foundations of learning theory and for the design of novel algorithms, and provides insights into problems as diverse as language learning and inverse problems in physics and engineering.

Algorithms↗

PubMed2003

A comparison of primate prefrontal and inferior temporal cortices during visual categorization.

Previous studies have suggested that both the prefrontal cortex (PFC) and inferior temporal cortex (ITC) are involved in high-level visual processing and categorization, but their respective roles are not known. To address this, we trained monkeys to categorize a continuous set of visual stimuli into two categories, "cats" and "dogs." The stimuli were parametrically generated using a computer graphics morphing system (Sheltonelton, 2000) that allowed precise control over stimulus shape. After training, we recorded neural activity from the PFC and the ITC of monkeys while they performed a category-matching task. We found that the PFC and the ITC play distinct roles in category-based behaviors: the ITC seems more involved in the analysis of currently viewed shapes, whereas the PFC showed stronger category signals, memory effects, and a greater tendency to encode information in terms of its behavioral meaning.

Analysis of Variance↗

PubMed2003

Neural mechanisms for the recognition of biological movements.

The visual recognition of complex movements and actions is crucial for the survival of many species. It is important not only for communication and recognition at a distance, but also for the learning of complex motor actions by imitation. Movement recognition has been studied in psychophysical, neurophysiological and imaging experiments, and several cortical areas involved in it have been identified. We use a neurophysiologically plausible and quantitative model as a tool for organizing and making sense of the experimental data, despite their growing size and complexity. We review the main experimental findings and discuss possible neural mechanisms, and show that a learning-based, feedforward model provides a neurophysiologically plausible and consistent summary of many key experimental results.

Animals↗

PubMed2002

Prediction of central nervous system embryonal tumour outcome based on gene expression.

Embryonal tumours of the central nervous system (CNS) represent a heterogeneous group of tumours about which little is known biologically, and whose diagnosis, on the basis of morphologic appearance alone, is controversial. Medulloblastomas, for example, are the most common malignant brain tumour of childhood, but their pathogenesis is unknown, their relationship to other embryonal CNS tumours is debated, and patients' response to therapy is difficult to predict. We approached these problems by developing a classification system based on DNA microarray gene expression data derived from 99 patient samples. Here we demonstrate that medulloblastomas are molecularly distinct from other brain tumours including primitive neuroectodermal tumours (PNETs), atypical teratoid/rhabdoid tumours (AT/RTs) and malignant gliomas. Previously unrecognized evidence supporting the derivation of medulloblastomas from cerebellar granule cells through activation of the Sonic Hedgehog (SHH) pathway was also revealed. We show further that the clinical outcome of children with medulloblastomas is highly predictable on the basis of the gene expression profiles of their tumours at diagnosis.

Adolescent↗

PubMed2002

Neural mechanisms of object recognition.

Single-unit recordings from behaving monkeys and human functional magnetic resonance imaging studies have continued to provide a host of experimental data on the properties and mechanisms of object recognition in cortex. Recent advances in object recognition, spanning issues regarding invariance, selectivity, representation and levels of recognition have allowed us to propose a putative model of object recognition in cortex.

Animals↗

PubMed2002

'United' we stand.

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Facial Expression↗

PubMed2002

Visual categorization and the primate prefrontal cortex: neurophysiology and behavior.

The ability to group stimuli into meaningful categories is a fundamental cognitive process. To explore its neuronal basis, we trained monkeys to categorize computer-generated stimuli as "cats" and "dogs." A morphing system was used to systematically vary stimulus shape and precisely define a category boundary. Psychophysical testing and analysis of eye movements suggest that the monkeys categorized the stimuli by attending to multiple stimulus features. Neuronal activity in the lateral prefrontal cortex reflected the category of visual stimuli and changed with learning when a monkey was retrained with the same stimuli assigned to new categories. Further, many neurons showed activity that appeared to reflect the monkey's decision about whether two stimuli were from the same category or not. These results suggest that the lateral prefrontal cortex is an important part of the neuronal circuitry underlying category learning and category-based behaviors.

Action Potentials↗