Computational Neuroscience, Vision and Acoustic Systems
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
“
Large
-
Scale Biologically Realistic Models of Brain Dynamics
Applied to Intelligent Robotic Decision Making
”
ONR
N00014
-
10
-
1
-
0014
•
Graduate Students
Brain models & NCS
Laurence Jayet
Sridhar Reddy
Robotics
Sridhar Reddy
Roger Hoang
Cluster Communications
Corey Thibeault
•
Investigators
Fred
Harris, Jr
.
Sergiu Dascalu
Phil Goodman
Henry Markram
EPFL
Contributors
ChildBot
Florian Mormann
U Bonn
Mathias Quoy
U de Cergy
-
Pontoise
Neuroscience
Mesocircuit Modeling
Present Scope of Work
Robotic/Human
Loops
(Virtual Neurorobotics)
Software/Hardware
Engineering
Neuroscience
Mesocircuit Modeling
Robotic/Human
Loops
(Virtual Neurorobotics)
Software/Hardware
Engineering
Brain
slice technology
to Physiology
Neuroscience
Mesocircuit Modeling
Robotic/Human
Loops
(Virtual Neurorobotics)
Software/Hardware
Engineering
Neural Software Engineering
NCS is the only system with
a real
-
time robotic interface
(bAC)
K
AHP
800
excitatory
neurons
G
exc
P
connect
200
inhibitory
neurons
G
exc
P
connect
G
inh
P
connect
G
inh
P
connect
“Recurrrent Asynch Irreg Nonlinear” (RAIN) networks
RAIN Activity
HUMAN Wakeful RAIN Activity
ISI distrib
(10 min)
Rate
(cellwise)
CV (std/mn)
(cellwise)
(1 minute window)
R Parietal
5s close
-
up
Mesocircuit
RAIN: “Edge of Chaos”
•
Originally 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
Lyapunov exponents on human unit simultaneous recordings
from Hippocampus and Entorhinal Cortex
Unpublished data, 3/2010: Quoy, Goodman
Neocortical
-
Hippocampal Navigation
A Circuit
-
Level Model of Hippocampal Place Field Dynamics
Modulated by Entorhinal Grid and Suppression
-
Generating Cells
Laurence C. Jayet
1*
, and Mathias Quoy
2
, Philip H. Goodman
1
1
University of Nevada, Reno
2
Université de Cergy
-
Pontoise, Paris
w/o K
ahp
channels
•
NO
intra
cellular theta precession
•
Asymm ramp
-
like depolarization
•
Theta power & frequ increase in PF
Explained findings of Harvey et al.
(2009) Nature 461:941
EC lesion
•
EC grid cells ignite PF
•
EC suppressor cells stabilize
Explained findings of Van Cauter et al.
(2008) EJNeurosci 17:1933
Harvey et al.
(2009) Nature 461:941
Neuroscience
Mesocircuit Modeling
Robotic/Human
Loops
(Virtual Neurorobotics)
Software/Hardware
Engineering
Sunfire X4600
GPU
Beowulf
200 cpu
Neuroscience
Mesocircuit Modeling
Robotic/Human
Loops
(Virtual Neurorobotics)
Software/Hardware
Engineering
Virtual Neuro
-
Robotics
Behavioral VNR System
Human trials using intranasal OT
•
Willingness 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
Neuroanatomy
•
OT is 9
-
amino acid cyclic peptide
•
first peptide to be sequenced & synthesized! (ca. 1950)
•
means “rapid birth”: promotes uterine contraction
•
promotes milk ejection for lactation
•
reflects release from pituitary into the blood stream
“neurohypophyseal OT system”
•
rodents
: maternal & paternal bonding
•
voles
: social recognition of cohabitating partner vs stranger
•
ungulates
: selective olfactory bonding (memory) for own lamb
•
seems to modulate the saliency & encoding of sensory signals
“direct CNS OT system”
(OT & OTR KOs & pharmacology)
•
Inputs from neocortex, limbic system, and brainstem
•
Outputs:
Local dendritic release of OT into CNS fluid
Axonal inhib synapses in amygdala & NAcc
•
SON: magnocellular to pituitary
•
PVN: parvocellular to amygdala
& brainstem
axon to CNS
to PITUITARY
Magno
Parvo
fluid to CNS
“Trust & Affiliation” paradigm
Willingness to exchange token for food
Time spent facing
A
mygdala [fear response]:
inhibited by
HYp
oxytocin
HY
pothalamus
p
araventricular
nucleus [trust]: oxytocin neurons
Phase I: Trust the Intent (
TTI
)
Phase II: Emotional Reward Learning (
ERL
)
PR
VC
DPM
IT
oxytocin
VC
V
isual
C
ortex
PFdl
VPM
AC
A
uditory
C
ortex
AC
PFdl
P
refrontal,
D
orsolateral:
sustained suppression
PR
P
arietal
R
each
(LIP):
reach
decision making
V
entral
P
re
M
otor:
sustained activity
VPM
“Trust & Learn” Robotic Brain Project
D
orsal
P
re
M
otor:
planning
& deciding
DPM
BG
BG
B
asal
G
anglia:
decision
making
AM
AM
HYp
HYp
HPF
HPF
H
ippo
C
F
ormation
EC
HPF
EC
E
ntorhinal
C
ortex
I
nfero
T
emporal cortex:
responds to faces
IT
Phase I: Trust the Intent (
TTI
)
1.
Robot brain initiates
arbitrary sequence
of motions
2.
human moves object in
either
a similar
(“match”), or different
(“mismatch”)
pattern
Robot
Initiates Action
Human
Responds
LEARNING
Match:
robot
learns to trust
Mismatch:
don’t trust
3.
human
slowly
reaches for
an
object on the table
4.
Robot
either
“
trusts
”,
(assists/offers
the
object),
or
“
distrusts
”,
(retract
the
object).
Human
Acts
Robot
Reacts
CHALLENGE (at any time)
trusted
distrusted
Gabor V1
-
3 emulation
Phase II:
Emotional Reward Learning (
ERL
)
1.
human
initiates
arbitrary sequence
of object motions
Human Initiates Action
LEARNING
GOAL (after several + rewards)
Matches
consistently
2.
robot moves object in
either
a similar
(“
match”),
or different (“mismatch”)
pattern
Robot Responds
Match:
voiced +reward
Mismatch:
voiced
–
reward
Early ITI Results
Concordant > Trust
Discordant > Distrust
mean synaptic strength
Neuroscience
Mesocircuit Modeling
Robotic/Human
Loops
(Virtual Neurorobotics)
Scope of Work in the Coming Year
Software/Hardware
Engineering
Sunfire X4600
GPU
EC
CA
The Quad
at UNR
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