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ONR Cognitive Neuroscience & Human-Robot Interaction Arlington, VA, June 9, 2010 Phil Goodman 1,2 , Fred Harris, Jr 1,2 , Sergiu Dascalu 1,2 , Florian Mormann 3 & Henry Markram 4 1 Brain Computation Laboratory, School of Medicine, UNR 2 Dept. of Computer Science & Engineering, UNR 3 Dept. of Epileptology, University of Bonn, Germany 4 Brain Mind Institute, EPFL, Lausanne, Switzerland e-Scale Biologically Realistic Models of Brain Dyna Applied to Intelligent Robotic Decision Making N00014-10-1-0014

ONR Cognitive Neuroscience & Human-Robot Interaction Arlington, VA, June 9, 2010

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Large-Scale Biologically Realistic Models of Brain Dynamics Applied to Intelligent Robotic Decision Making. ONR Cognitive Neuroscience & Human-Robot Interaction Arlington, VA, June 9, 2010. N00014-10-1-0014. - PowerPoint PPT Presentation

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Graduate StudentsBrain models & NCSLaurence JayetSridhar Reddy

RoboticsSridhar ReddyRoger Hoang

Cluster CommunicationsCorey Thibeault InvestigatorsFred Harris, Jr. Sergiu Dascalu Phil GoodmanHenry MarkramEPFLContributors

ChildBot

Florian MormannU Bonn

Mathias QuoyU de Cergy-Pontoise

2

dopamineAmygdala [fear response]: inhibited by HYp oxytocinHYpothalamus paraventricular nucleus [trust]: oxytocin neuronsPRVCDPM

IT

oxytocin

VCVisual Cortex

PFVPMACAuditory CortexACPFPrefrontal, dorsolateraland medialPRParietal Reach (LIP): reach decision makingVentral PreMotor: sustained activityVPM

Million-Cell Brain Model

Dorsal PreMotor: planning & decidingDPM

BG

BG Basal Ganglia: decision making

AM

AM

HYp

HYpHPF

HPF HippoC FormationEC

HPFEC Entorhinal CortexInferoTemporal cortex: responds to facesIT

BS

BSBrainStem DA & NE centers

NeuroscienceMesocircuit Modeling

Present Scope of Work

Robotic/Human Loops(Virtual Neurorobotics)

Parallel Hardware OptimizationNeuroscienceMesocircuit Modeling

Robotic/Human Loops(Virtual Neurorobotics)

Parallel Hardware OptimizationTo Neural Models & Software Engineering

NCS is the only system witha real-time robotic interface

(bAC)KAHP7Leaky Integrate & Fire Equations

800excitatoryneuronsGexcPconnect200inhibitoryneuronsGexcPconnectGinhPconnectGinhPconnectRecurrent Asynch Irreg Nonlinear (RAIN) networks9Simulated RAIN Activity (1600 cells, 4:1 E:I)

10Mesocircuit RAIN: Edge of ChaosOriginally coined wrt cellular automata: rules for complex processing most likely to be found at phase transitions (PTs) between order & chaotic regimes (Packard 1988; Langton 1990; but questioned by Mitchell et al. (1993)

Hypothesis here wrt Cognition, where SNN have components of SWN, SFN, and exponentially truncated power laws

PTs cause rerouting of ongoing activity (OA), resulting in measured rhythmic synchronization and coherence

The direct mechanism is not embedded synfire chains, braids, avalanches, rate-coded paths, etc.

Modulated by plastic synaptic structures

Modulated by neurohormones (incl OT)

Dynamic systems & directed graph theory > theory of computation

Edge of Chaos Concept

Unpublished data, 3/2010: Quoy, Goodman Lyapunov exponents on human unit simultaneous recordings from Hippocampus and Entorhinal Cortex

ECHIP (data provided in collab withI Fried lab, UCLA)11Biology: EC and HP in vivo

NO intracellular theta precessionAsymm ramp-like depolarizationTheta power & frequ increase in PF

EC cells stabilize PF ignitionEC suppresses # of PF cells firing while increasing firing rateECHP Model: Linear Maze Place Fields

A Circuit-Level Model of Hippocampal Place Field Dynamics Modulated by Entorhinal Grid and Suppression-Generating CellsLaurence C. Jayet1*, and Mathias Quoy2, Philip H. Goodman11 University of Nevada, Reno 2 Universit de Cergy-Pontoise, Paris

w/o Kahp channels NO intracellular theta precessionAsymm ramp-like depolarizationTheta power & frequ increase in PFExplained findings of Harvey et al. (2009) Nature 461:941

EC lesionEC grid cells ignite PFEC suppressor cells stabilizeExplained findings of Van Cauter et al. (2008) EJNeurosci 17:1933

Harvey et al. (2009) Nature 461:941

Full Circuit Model: Short-Term Sequence Memory

CAECDGSUBVisualinputPrefrontalPremotorVisual-ParietalSomato-sensoryinput

R

RRR

RRR

PFCSTMHIPPLACECELLSSUBICULUMSSS

EEERRRField Potential

5010152025Completing the loop: Neocortical-Hippocampal Sequence Learning

SSSTrial 1: no rewardTrial 2: rewardTrial 3

KEY

S=START POSITION E=END POSITIONR=REWARD (green if earned) =enhanced inhibitory oscillation(resets prefrontal activity if not enhanced by prior reward)NeuroscienceMesocircuit Modeling

Robotic/Human Loops(Virtual Neurorobotics)

Parallel Hardware Optimization

Human trials using intranasal OTWillingness to trust, accept social risk (Kosfeld 2005)Trust despite prior betrayal (Baumgartner 2008)Improved ability to infer emotional state of others (Domes 2007)Improved accuracy of classifying facial expressions (Di Simplicio 2009)Improved accuracy of recognizing angry faces (Champaign 2007)Improved memory for familiar faces (Savaskan 2008)Improved memory for faces, not other stimuli (Rummele 2009)Amygdala less active & less coupled to BS and neocortex w/ fear or pain stimuli (Kirsch 2005, Domes 2007, Singer 2008)

Oxytocin Physiology

NeuroanatomyOT is 9-amino acid cyclic peptidefirst peptide to be sequenced & synthesized! (ca. 1950)means rapid birth: OT bursts promote uterine contractionOT bursts cause milk ejection during lactationneurohypophyseal OT system (from pituitary to bloodstream)rodents: maternal & paternal bondingvoles: social recognition of cohabitating partner vs strangerungulates: selective olfactory bonding (memory) for own lambseems to modulate the saliency & encoding of sensory signalsdirect CNS OT system (OT & OTR KOs & pharmacology)Inputs from neocortex, limbic system, and brainstemOutputs:Local dendritic release of OT into CNS fluid Axonal inhib synapses in amygdala & NAcc

SON: magnocellular to pituitary to blood PVN: parvocellular to amygdala, HIP, BG & brainstem

axon to CNSto PITUITARYMagnoParvofluid to CNSTrust & Affiliation paradigm

Willingness to exchange token for foodPhase I: Trust the Intent (TTI)

Robot brain initiates arbitrary sequence of motionshuman moves object in either a similar (match), or different (mismatch) pattern

Robot Initiates ActionHuman RespondsLEARNING

Match: robot learns to trustMismatch: dont trusthuman slowly reaches for an object on the table

Robot either trusts, (assists/offers the object), or distrusts, (retract the object).

Human ActsRobot ReactsCHALLENGE (at any time)trusteddistrusted

Gabor V1,2,4 emulation

Early ITI Results

Concordant > TrustDiscordant > Distrust

mean synaptic strength

Phase II: Emotional Reward Learning (ERL)

human initiates arbitrary sequence of object motionsHuman Initiates ActionLEARNINGGOAL (after several + rewards)

Matches consistentlyrobot moves object in either a similar (match), or different (mismatch) patternRobot Responds

Match: voiced +rewardMismatch: voiced reward

Amygdala [fear response]: inhibited by HYp oxytocinHYpothalamus paraventricular nucleus [trust]: oxytocin neurons

PRVCDPM

IT

oxytocin

VCVisual Cortex

VPMACAuditory CortexACPFPrefrontal, dorsolateraland medialPRParietal Reach (LIP): reach decision makingVentral PreMotor: sustained activityVPM

Million-Cell Brain Model

Dorsal PreMotor: planning & decidingDPM

BG

BG Basal Ganglia: decision making

AM

AM

HYp

HYpHPF

HPF HippoC FormationEC

HPFEC Entorhinal CortexInferoTemporal cortex: responds to facesIT

BS

BSBrainStem DA & NE centersdopamineMultiModalMirror NPF++S

The Quad at UNR