1 / 62

Neuroscience II T. Poggio

Neuroscience II T. Poggio. Neuroscience. Brain Overview?. The Ventral Visual Pathway. modified from Ungerleider and Haxby, 1994. Visual Areas. Face-tuned cells in IT. VIEW ANGLE. S. Model of view-invariant recognition: learning from views. A graphical rewriting of mathematics of

sorcha
Download Presentation

Neuroscience II T. Poggio

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Neuroscience IIT. Poggio

  2. Neuroscience Brain Overview?

  3. The Ventral Visual Pathway modified from Ungerleider and Haxby, 1994

  4. Visual Areas

  5. Face-tuned cells in IT

  6. VIEW ANGLE S Model of view-invariant recognition: learning from views A graphical rewriting of mathematics of regularization (GRBF), a learning technique Poggio, Edelman Nature, 1990.

  7. Examples of Visual Stimuli Learning to Recognize3D Objects in IT Cortex After human psychophysics (Buelthoff, Edelman, Tarr, Sinha,…), which supports models based on view-tuned units...physiology! Logothetis, Pauls, Poggio 1995

  8. Stimulus Right Lever Blue Fixspot Left Lever Response T = Target D = Distractor Stimulus Yellow Fixspot Response Task Description Logothetis, Pauls, Poggio 1995

  9. Recording Sites in Anterior IT Logothetis, Pauls, and Poggio, 1995; Logothetis, Pauls, 1995

  10. Model’s predictions: View-tuned Neurons VIEW-TUNED UNITS VIEW ANGLE

  11. The Cortex: Neurons Tuned to Object Views Logothetis, Pauls, Poggio 1995

  12. A View Tuned Cell Logothetis, Pauls, Poggio 1995

  13. VIEW-INVARIANT, OBJECT-SPECIFIC UNIT  View Angle Model’s predictions : View-invariant, Object-specific Neurons

  14. The Cortex: View-invariant, Object-specific Neurons Logothetis, Pauls, Poggio,1995

  15. Recognition of Wire Objects

  16. Generalization Field

  17. View-dependent Response of an IT Neuron

  18. Sparse Representations in IT In the recording area in AMTS -- a specialized region for paperclips (!) -- we estimate that there are, after training,(within an order of magnitude or two) … • About 400 view tuned cells per object • Perhaps 20 view-invariant cells per object Logothetis, Pauls, Poggio, 1997

  19. Previous glimpses: cells tuned to face identity and view Perrett, 1989

  20. 2. View-tuned IT neurons View-tuned cells in IT Cortex: how do they work? How do they achieve selectivity and invariance? Max Riesenhuber and T. Poggio, Nature Neuroscience, just published

  21. max Some of our funding is from Honda...

  22. Model’s View-tuned Neurons VIEW-TUNED UNITS VIEW ANGLE

  23. Scale Invariant Responses of an IT Neuron Scale-Invariant Responses of an IT Neuron (training on one size only!) Logothetis, Pauls and Poggio, 1995

  24. Spike Rate (Target Response)/ (Mean of Best Distractors) Invariances: Overview • Invariance around training view • Invariance while maintaining specificity Logothetis, Pauls and Poggio, 1995

  25. Our quantitative model builds upon previous hierarchical models • Hubel & Wiesel (1962): • Simple to complex to ``higher order hypercomplex cells’’ • Fukushima (1980): • Alternation of “S” and “C” layers to build up feature specificity and translation invariance, resp. • Perrett & Oram (1993): • Pooling as general mechanism to achieve invariance

  26. Model of view tuned cells MAX Riesenhuber and Tommy Poggio, 1999

  27. “IT” w . . . “V4” “V1” ... ... . . . Model Diagram View-specific learning: synaptic plasticity

  28. Max (or “softmax”) • key mechanism in the model • computationally equivalent to selection (and scanning in our object detection system)

  29. V1: Simple Features, Small Receptive Fields • Simple cells respond to bars • “Complex Cells”: translation invariance; pool over simple cells of the same orientation (Hubel&Wiesel) Hubel & Wiesel, 1959

  30. Two possible Pooling Mechanisms Nn nn thanks to Pawan Sinha

  31. An Example: Simple to Complex Cells “simple”cells ? “complex” cell

  32. “simple”cells ? “complex” cell Simple to Complex: Invariance to Position and Feature Selectivity

  33. 3. Some predictions of the model • Scale and translation invariance of view-tuned AIT neurons • Response to pseudomirror views • Effect of scrambling • Multiple objects • Robustness to clutter • Consistent with K. Tanaka’s simplification procedure • More and more complex features from V1 to AIT

  34. Testing Selectivity and Invariance of Model Neurons • Test specificity AND transformation tolerance of view-tuned model neurons • Same objects as in Logothetis’ experiment • 60 distractors

  35. Invariances of IT (view-tuned) Model Neuron

  36. Invariances: Experiment vs. Model (view-tuned cells) *

  37. MAX vs. Summation

  38. Response toPseudo-Mirror Views As in experiment, some model neurons show tuning to pseudo-mirror image

  39. Robustness to scrambling: model and IT neurons Experiments: Vogels, 1999

  40. Recognition in Context: Two Objects

  41. Recognition in Context: some experimental support • Sato: Response of IT cells • to two stimuli in RF Sato, 1989

  42. Recognition in Clutter:data How does response of IT neurons change if background is introduced? Missal et al., 1997

  43. Recognition in Clutter: model • average model neuron response • recognition rates

  44. Further Support: Keiji just mentioned his simplification paradigm... Wang et al., 1998

  45. Consistent behaviour of the model

  46. Higher complexity and invariances in Higher Areas Kobatake & Tanaka, 1994

More Related