2 Laminar Structure of Cortex. 4 Area Structure of Cortex

Transcription

1 Networks!! Lamnar Structure of Cortex. Bology: The cortex. Exctaton: Undrectonal (transformatons) Local vs. dstrbuted representatons Bdrectonal (pattern completon, amplfcaton). Inhbton: Controllng bdrectonal exctaton.. Constrant Satsfacton: Puttng t all together. Lamnar Structure of Cortex Area Structure of Cortex (Layers,) (Layer ) Output (Layers,) Sensaton (Thalamus) Motor/BG

2 Area Structure of Cortex Exctaton (Undrectonal): Transformatons (Layers,) (Layer ) Output (Layers,) (Layers,) (Layers,) (Layer ) Output (Layers,) Output (Layers,) Detectors work n parallel to transform nput actvty pattern to hdden actvty pattern. Sensaton (Thalamus) BG Thalamus Thalamus Motor/BG Emphaszes some dstnctons, collapses across others. Functon of what the detectors detect (and what they gnore). Transformatons Emphasze dstnctons: Dfferent dgts non-overlappng. Collapse dstnctons: Nosy dgts categorzed as same. Dstnctons: Cluster Plot a) NosyDgts Pattern: b) _Acts Pattern:...

3 Detectors are Dedcated, Content-Specfc a) Letters Pattern: b) A M Q W H D B..... V N IT Z L K R E U J S FP O CG _Acts Pattern: Z W V U T R Q P O N M L K J I H G F E D C B A S... Dstrbuted vs Localst Representatons Localst = unt actve at a tme (e.g., dgts). Dstrbuted = many unts actve, for multple nputs (the bran!). Dgts Wth Dstrbuted Representatons Advantages of Dstrbuted Representatons Effcency: Fewer total unts requred. Smlarty: As a functon of overlap. Generalzaton: Can use novel combnatons. Robustness: Redundancy. Accuracy: By coarse-codng. Learnng: Bootstrappng of small changes.

4 energy Bdrectonal Exctaton... Attractor Dynamcs attractor basn. Top-down processng ( magery ).. Pattern completon.... state y state x attractor state. Amplfcaton/bootstrappng. Bdrectonal exctaton caused network to settle nto a partcular stable state over tme: the attractor.. Attractor dynamcs. Networks Need Inhbton!. Bology: The cortex. Exctaton: Controls actvty (bdrectonal exctaton). Competton -> selecton (Darwn!). Undrectonal (transformatons) Local vs. dstrbuted representatons Bdrectonal (pattern completon, amplfcaton). Inhbton: Controllng bdrectonal exctaton.. Constrant Satsfacton: Puttng t all together.. Bology: Feedforward and feedback nhbton.. Crtcal Parameters.. Smplfcaton.

5 Types of Inhbton a) b) Inhb Antcpates exctaton Feed Forward Crtcal Parameters Feedback Inhb Reacts to exctaton Inhb conductance nto hdden unts (g_bar_.hdden) Smplfcaton: KWTA Approxmaton Very computatonally expensve to smulate all nhbtory nterneurons. Approxmate nhbton by allowng maxmum of actve at any tme (the most actve unts). unts to be Approxmates set pont behavor of negatve feedback systems Implemented by computng for entre layer, such that get actve, but rest wll be too nhbted. unts can Strength of feedforward weghts to nhb (scale.ff) Strength of feedback weghts to nhb (scale.fb) KWTA Approxmaton: Smple a) b) c) g g g k k+ n k k+ n k k+ n KWTA Approxmaton: Average-Based a) b) c) < > k < >k g < >n k < > k g < > n k g Actual Actual < >n k actvty actvty k k+ n k k+ n k k+ n () () () () () ()

6 energy Constrant Satsfacton The Energy Functon attractor basn. Energy Functon.. Nose.. Inhbton. state y state x attractor state Fallng thngs are mnmzng ther potental energy: nature always seeks to mnmze the energy of a system. If we can wrte an expresson for the energy of a network, wll the nature of the network mnmze t? es! Energy, Harmony The Role of Nose Best when actvatons consstent w/weghts. Just updatng actvatons locally ncreases global constrant satsfacton! Local Mnmum Global Mnmum