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The Evolution of Backpropagation: A Biologically Plausible Approach 25 Years Later

This research explores the advancements in backpropagation techniques 25 years post-inception, introducing the concept of Generalized Recirculation (GeneRec) and its biological foundations, specifically derived from spike-timing-dependent plasticity (STDP). The paper discusses error-driven learning and contrasts conventional feedforward networks with bidirectional dynamics. Emphasizing biological relevance, the findings contribute significantly to various cognitive neuroscience domains, including perception, attention, and learning. This work aims to pave the way for building a biologically plausible cognitive architecture to advance our understanding of intelligence and sensemaking.

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The Evolution of Backpropagation: A Biologically Plausible Approach 25 Years Later

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  1. Backprop, 25 Years Later: Biologically Plausible Backprop Randall C. O’Reilly University of Colorado Boulder eCortex, Inc.

  2. Outline • Backpropagation via activation differences: Generalized Recirculation (GeneRec) • Bottom-up derivation of activation differences from STDP • Bidirectional activation dynamics vs. feedforward networks

  3. Recirculation (early RBM)

  4. Generalized Recirculation (GeneRec)(O’Reilly, 1996 – see also Xie & Seung, 2003)

  5. Contrastive Hebbian Learning (CHL)(Movellan, 1990; Hinton 1989 DBM) CHL, DBM: GeneRec: Avg Sender: ^ Symmetry = CHL

  6. Biology of Learning

  7. STDP: Spike Timing Dependent Plasticity

  8. Error-driven Learning from STDP(computational  biological bridge) Urakubo et al, 2008 Real spike trains in.. Captures ~80% of variance in model LTP/LTD (Linearized BCM) Fits to STDP data for pairs, triplets, quads

  9. Extended Spike Trains =Emergent Simplicity S = 100Hz S = 50Hz S = 20Hz r=.894 dW = f(send * recv) = (spike rate * duration)

  10. Bienenstock Cooper & Munro (1982) Floating threshold = Homeostatic regulation More robust form of Hebbian learning Kirkwood et al (1996):

  11. Fast Threshold Adaptation:Outcome vs. Expectation dW ≈ <xy>s - <xy>m outcome – expectation XCAL = temporally eXtended Contrastive Attractor Learning

  12. Where Does Error Come From?

  13. Biological Modeling Frameworkhttp://ccnbook.colorado.edu Same framework accounts for wide range of cognitive neuroscience phenomena: perception, attention, motor control and action selection, learning & memory, language, executive function…

  14. ICArUS-MINDS (IARPA)Integrated Cognitive Architecture for Understanding SensemakingMirroring Intelligence in a Neural Description of Sensemaking • 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

  15. Emer Virtual Robot:Perceptual Motor Control & Robust Object Recognition

  16. 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…)

  17. 3D Object Recognition Test • 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

  18. Object Recognition Generalization Results

  19. Thanks To CCN 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

  20. Extras

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