Backprop, 25 Years Later: Biologically Plausible Backprop
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Transcript of Backprop, 25 Years Later: Biologically Plausible Backprop
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Backprop, 25 Years Later: Biologically Plausible Backprop
Randall C. O’Reilly
University of Colorado Boulder
eCortex, Inc.
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Outline
Backpropagation via activation differences: Generalized Recirculation (GeneRec)
Bottom-up derivation of activation differences from STDP
Bidirectional activation dynamics vs. feedforward networks
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Recirculation (early RBM)
tk
T = 0
T = 1
hj*
T = 3
T = 2
ko
hj
Target Pattern
ReconstructedPattern
Recirculation (Hinton & McClelland, 1988)
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Generalized Recirculation (GeneRec)(O’Reilly, 1996 – see also Xie & Seung, 2003)
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Contrastive Hebbian Learning (CHL)(Movellan, 1990; Hinton 1989 DBM)
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CHL, DBM:
GeneRec:
Avg Sender:
^ Symmetry = CHL
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Biology of Learning
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STDP: Spike Timing Dependent Plasticity
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Error-driven Learning from STDP(computational biological bridge)
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Urakubo et al, 2008
Captures ~80% of variance in model LTP/LTD
(Linearized BCM)
Real spiketrains in..
Fits to STDP data for pairs, triplets, quads
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Extended Spike Trains =Emergent Simplicity
S = 100Hz S = 20HzS = 50Hz
r=.894dW = f(send * recv) = (spike rate * duration)
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Bienenstock Cooper & Munro (1982)
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Floating threshold =Homeostatic regulation
More robust form of Hebbian learning
Kirkwood et al (1996):
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Fast Threshold Adaptation:Outcome vs. Expectation
dW ≈ <xy>s - <xy>m
outcome – expectation
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XCAL = temporally eXtended Contrastive Attractor Learning
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Where Does Error Come From?
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Biological Modeling Frameworkhttp://ccnbook.colorado.edu
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Same framework accounts for wide range of cognitive neuroscience phenomena: perception, attention, motor control and action selection, learning & memory, language, executive function…
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ICArUS-MINDS (IARPA)Integrated Cognitive Architecture for Understanding Sensemaking
Mirroring Intelligence in a Neural Description of Sensemaking
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Team: HRL (R. Bhattacharyya), CU Boulder (R. O’Reilly), CMU (C. Lebiere), UTH (H. Wang), PARC (P. Pirolli), UCI (J. Krichmar)
Goal: Build biologically-based cognitive architecture to model intelligence analyst.
Brain areas:•Posterior Cortex (IT, Parietal)•PFC/BG/DA•Hippocampus•BNS: LC, ACh
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Emer Virtual Robot:Perceptual Motor Control & Robust Object Recognition
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Invariant Object Recognition
Hierarchy of increasing: Feature complexity
Spatial invariance
Strong match to RF’s in corresponding brain areas
(Fukushima, 1980; Poggio, Riesenhuber, et al…)
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From Google SketchUp Warehouse
100 categories
8+ objects per categ
2 objects left out for testing
+/- 20° horiz depth rotation + 180° flip
0-30° vertical depth rotation
14° 2D planar rotations
25% scaling
30% planar translations
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3D Object Recognition Test
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Object Recognition Generalization Results
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Thanks ToCCN Lab
Tom Hazy Seth Herd Tren Huang Dave Jilk (eCortex) Nick Ketz Trent Kriete Kai Krueger Brian Mingus Jessica Mollick Wolfgang Pauli Sergio Verduzco-Flores Dean Wyatte
Funding ONR – McKenna & Bello iARPA – Minnery NSF SLC - TDLC DARPA - BICA AFOSR NIMH P50-MH079485
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Extras
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