Cooperative Physics of Fly Swarms: An Emergent Behavior

We have simulated the behavior of several artificial flies, interacting visually with each other. Each fly is described by a simple tracking system (Poggio and Reichardt, 1973; Land and Collett, 1974) which summarizes behavioral experiments in which individual flies fixate a target. Our main finding is that the interaction of theses implemodules gives rise to a variety of relatively complex behaviors. In particular, we observe a swarm-like behavior of a group of many artificial flies for certain reasonable ranges of our tracking system parameters

Routing Thoughts

This working paper is has been brought to you by the modern wonders of microcassette dictating equipment, through which Professor Poggio can now cough up working papers while doing something else more important.In a parallel machine with many thousands of processors the routing of information between processors is a key task, which turns out to require as much hardware and perhaps more sophistication than local computing itself. There are at least two basic engineering solutions to the routing problem: one followed by most research projects is of the "packet switching" type, that behaves as a mail service, with data carrying addresses to route the packet through the system. The other, more similar to a traditional telephone system, has connections made and broken (or enabled and disabled) as required for exchanging information. These solutions, based on silicon technology and digital electronic, may be quite different from the routing solutions used by the prototypical parallel machine — the brain. This paper asks questions concerning routing information in parallel machines with an eye to biological wetware. It is divided in four disconnected parts, that do not contain finished results but consist of suggestions for future speculations: 1) How to make Infinity Small. 2) Routers and Brains 3) Classifying Parallel Machines 3) The Problem of RemappingMIT Artificial Intelligence Laborator

The Effect of Family Separation and Reunification on the Educational Success of Immigrant Children in the United States

For many immigrants, especially those from Central America and Mexico, it is common for a mother or father (or both) to migrate to the United States and leave their children behind. Then, after the parent(s) have achieved some degree of stability in the United States, the children follow. Using qualitative and quantitative methods, we examined the hypothesis that separation during migration results in problems at school after re-unification. We find that children separated from parents during migration are more likely to be behind others their age in school and are more likely to drop out of high school.immigrant children, education, family separation

Differential Operators for Edge Detection

We present several results characterizing two differential operators used for edge detection: the Laplacian and the second directional derivative along the gradient. In particular, (a)we give conditions for coincidence of the zeros of the two operators, and (b) we show that the second derivative along the gradient has the same zeros of the normal curvature in the gradient direction. Biological implications are also discussed. An experiment is suggested to test which of the two operators may be used by the human visual system.MIT Artificial Intelligence Laborator

Representation Learning in Sensory Cortex: A Theory

We review and apply a computational theory based on the hypothesis that the feedforward path of the ventral stream in visual cortex's main function is the encoding of invariant representations of images. A key justification of the theory is provided by a result linking invariant representations to small sample complexity for image recognition - that is, invariant representations allow learning from very few labeled examples. The theory characterizes how an algorithm that can be implemented by a set of "simple" and "complex" cells - a "Hubel Wiesel module" - provides invariant and selective representations. The invariance can be learned in an unsupervised way from observed transformations. Our results show that an invariant representation implies several properties of the ventral stream organization, including the emergence of Gabor receptive filelds and specialized areas. The theory requires two stages of processing: the first, consisting of retinotopic visual areas such as V1, V2 and V4 with generic neuronal tuning, leads to representations that are invariant to translation and scaling; the second, consisting of modules in IT (Inferior Temporal cortex), with class- and object-specific tuning, provides a representation for recognition with approximate invariance to class specific transformations, such as pose (of a body, of a face) and expression. In summary, our theory is that the ventral stream's main function is to implement the unsupervised learning of "good" representations that reduce the sample complexity of the final supervised learning stage

Spatial Reference Frames for Object Recognition: Tuning for Rotations in Depth

The inferior temporal cortex (IT) of monkeys is thought to play an essential role in visual object recognition. Inferotemporal neurons are known to respond to complex visual stimuli, including patterns like faces, hands, or other body parts. What is the role of such neurons in object recognition? The present study examines this question in combined psychophysical and electrophysiological experiments, in which monkeys learned to classify and recognize novel visual 3D objects. A population of neurons in IT were found to respond selectively to such objects that the monkeys had recently learned to recognize. A large majority of these cells discharged maximally for one view of the object, while their response fell off gradually as the object was rotated away from the neuron"s preferred view. Most neurons exhibited orientation-dependent responses also during view-plane rotations. Some neurons were found tuned around two views of the same object, while a very small number of cells responded in a view- invariant manner. For five different objects that were extensively used during the training of the animals, and for which behavioral performance became view-independent, multiple cells were found that were tuned around different views of the same object. No selective responses were ever encountered for views that the animal systematically failed to recognize. The results of our experiments suggest that neurons in this area can develop a complex receptive field organization as a consequence of extensive training in the discrimination and recognition of objects. Simple geometric features did not appear to account for the neurons" selective responses. These findings support the idea that a population of neurons -- each tuned to a different object aspect, and each showing a certain degree of invariance to image transformations -- may, as an assembly, encode complex 3D objects. In such a system, several neurons may be active for any given vantage point, with a single unit acting like a blurred template for a limited neighborhood of a single view

Viewer-Centered Object Recognition in Monkeys

How does the brain recognize three-dimensional objects? We trained monkeys to recognize computer rendered objects presented from an arbitrarily chosen training view, and subsequently tested their ability to generalize recognition for other views. Our results provide additional evidence in favor of with a recognition model that accomplishes view-invariant performance by storing a limited number of object views or templates together with the capacity to interpolate between the templates (Poggio and Edelman, 1990)

A regularized solution to edge detection

AbstractWe assume that edge detection is the task of measuring and localizing changes of light intensity in the image. As discussed by V. Torre and T. Poggio (1984), “On Edge Detection,” AI Memo 768, MIT AI Lab), edge detection, when defined in this way, is λ problem of numerical differentiation, which is ill posed. This paper shows that simple regularization methods lead to filtering the image prior to an appropriate differentiation operation. In particular, we prove (1) that the variational formulation of Tikhonov regularization leads to λ convolution filter, (2) that the form of this filter is similar to the Gaussian filter, and (3) that the regularizing parameter λ in the variational principle effectively controls the scale of the filter