Cortical Computing
For Invariant Object Recognition
How complex are the underlying mechanisms
of Biological Intelligence ?
“
An organism on which a single atom or a group of atoms could
make a perceptible impression on its senses would most
certainly not be capable of developing the kind of orderly
thought, which after passing through a long sequence of earlier
stages ultimately results in forming among other ideas, the idea
of an atom
---Erwin Schrodinger---
”
Aspects of Understanding the Mechanisms of
the Brain
• Behavioural functions of the Brain
• Anatomical Mechanisms of the Brain
• Link between behaviour and the underlying biophysical and neurophysical
mechanisms
LAMINART MODEL
---Stephen Grossberg et.al, 2008---
LAMINART – Goal
• Links explanations of behavioural functions to the
underlying anatomical mechanisms
Principles of Cortical Computing
• Laminar Computing
• Complementary Computing
• Realization of a hierarchical resolution of uncertainity
Consequences of Complementary Computing
• Uniform design of the cortical circuits through the Neocortex
• Reduction of Computational Complexity
Consequences of Laminar Computing
• Development and Learning Process
• Binding Process
• Attentional Process
The Visual Cortex
Brodmann areas of the brain responsible for vision
Visual Processing
• Widely studied
• Plenty of experimental data
• Huge number of models explaining behavioural properties
Hubel and Wiesel 1960
Simple and Complex cell receptive fields in the
Primary Visual Cortex
Garbor Filters
Fundamental Circuit
Design in the Neocortex
---Grossberg et.al 2008---
Bottom Up Activation
• Bottom up activation from LGN
• On Centre Off Surround Network
• Inhibitory Neurons
• Producing Contrast divisive
normalization
Feedback
• Feedback from Higher cortical areas
• Shared circuit design
• Folded Feedback
Perceptual Grouping
• Competition between like cell responses
• Feedback from Groupings
• Closed circuit loop
V1/V2/LGN Interface
• Repeated circuit design
• Increased receptive fields
• Feedback path directly from Higher
cortical areas into V1
LAMINART MODEL
• Shared Circuit Design
• Pre attentive – attentive interface
• Synchronization and stability of cortical networks.
Computational Properties
1. Hybrid between feedforward and feedback computing
2. Simultaneous realization of the stability of digital computing and the
sensitivity to analog properties of the inputs
3. Self stabilization of development and learning
Feedforward Computing
• Unambiguous scenes
• Extremely fast
• From 4 to 2/3 to 4 to 2/3 of subsequent layers
Feedforward and feedback computing
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Ambiguous scenes
Self normalization of cell activities due to shunting equations
Competition among the possibilities due to Inhibitory interactions
Amplitude and coherence covary with processing speed
Feedback to resolve uncertainty
• Weakly active layers into a closed feedback loop.
• Rapid Contrast and amplification of the winning group
• Lateral Inhibition of non winning groups
Stability of Digital Computing and sensitivity to
Analog properties
• Selection of winning group in 2/3 – 6 – 4 – 2/3
• Coherence of synchronous selection
• Balance of Inhibition and excitation within 4 and 2/3
Self stabilization
• Self stabilize development and learning
• Selecting cells that fire together to wire together (Perceptual Groupings
explained in a later slide)
6 – 4 connections
• Different expressions of a shared circuit design
Perceptual Grouping
• Long Range Horizontal excitatory connections
• Balance between excitatory and Inhibitory
interneurons
• Modulatory net effect
• Realization of perceptual groupings in adults
Attention Modelling
• Matching between Top down and
bottom up signals
• Modulation in the attentional on centre
• Interaction between attention and
perceptual Grouping
• Object Representations
Attention Modelling
• Sharing the same circuit
• Attention modulates the groupings
Ability of attention to selectively light
up entire object representations
has obviously, an important survival
value in adults and it plays a key role in
infant development and adult perceptual
learning
Stability Vs Plasticity Dilemma !
Adaptive Resonance Theory
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ART’s solution to the stability vs Plasticity Dilemma
Flexibility of vigilance Criterion
Abstract vs Concrete information
Mismatch mediated search