System Level Neuroengineering Modeling of the Nervous System
Cancun Dec 2008
I. Steve Massaquoi
i. The Problemii. Overview of sensorimotor control systemiii. Attention to cerebellar system architectureiv. RIPID control model as example of medium high level modelv. Preliminary implications of model
II. Kazutaka Takahashi
i. Cerebrocerebellar system architecture in greater detailii. RICSS Quantitative model of internal signal generationiii. Model adequacy and modeling issuesiv. Relation of dynamic models to point process signal analysis
I. The Problem to develop increasingly comprehensive, integrated, multi-resolution engineering models of nervous system structure and function
MotivationsScientific: i. understanding the principles of human behavior and intelligence in quantitative, mechanistic terms ii. understanding the principles of neurological and psychiatric disease in quantitative, mechanistic terms
Engineering: i. design of devices to mimic and potentially supersede human behavior artificially ii. development of devices that better interact with the nervous system to study, restore or extend human function
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
I The Problem (cont’d)
Key Challenges (constraints) for Neuroengineering models:
A. Models must respect known or highly suspected neuroantomical connections
B. Models must respect known time delays and phase lags (these are non-trivial)C. Models must utilize functions that are known to be achievable by collections
of neurons. (e.g. Quantitative multiplication ?)D. Models must be consistent with both normal human function, and
pathological dysfunction.E. Models of human function should be able to be related to those of animals in
a manner consistent with natural evolution (i.e. continuously)F. Models must ultimately account for the firing behavior of system neurons (i.e. must be multi-resolution)
System Level Neuroengineering Modeling of Nervous System I.System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
I The Problem (cont’d)
Style of approach: Ideal features of Neuroengineering models:
A. Models should seek to explain the nervous system in terms of established engineering principles and abstractions
(e.g. filters, estimators, feedback/feedforward controllers, registers, switches
not simply to formulate a computational model)
B. Models should point to potentially fruitful areas of engineering research where current engineering methods are lacking.
• Models are more profitably driven by science, and guided by engineering principles. . . Rather than vice versa
(editorial remark!)
System Level Neuroengineering Modeling of Nervous System I.System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
I The Problem (cont’d)
Neuroengineering strategy Modeling and analysis Behavior & Intelligence: Bottom up Primitive complex mimicking evolution editorial comment #2 Intelligence will emerge
System Level Neuroengineering Modeling of Nervous System I.System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
I The Problem (cont’d)
Neuroengineering strategy Modeling and analysis Behavior & Intelligence: Bottom up Primitive complex mimicking evolution editorial comment #2 Intelligence will emerge Modeling and analysis of Physiology: Simultaneous multi-resolution neuron circuits architecture
Massaquoi
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
I The Problem (cont’d)
Neuroengineering strategy Modeling and analysis Behavior & Intelligence: Bottom up Primitive complex mimicking evolution editorial comment #2 Intelligence will emerge Modeling and analysis of Physiology: Simultaneous multi-resolution neuron circuits architecture
Takahashi
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
I The Problem (cont’d)
Neuroengineering strategy Modeling and analysis Behavior & Intelligence: Bottom up Primitive complex mimicking evolution editorial comment #2 Intelligence will emerge Modeling and analysis of Physiology: Simultaneous multi-resolution neuron circuits architecture
Emphasis for rest of session
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
I The Problem (cont’d)
Neuroengineering strategy Modeling and analysis Behavior & Intelligence: Bottom up Primitive complex mimicking evolution editorial comment #2 Intelligence will emerge Modeling and analysis of Physiology: Simultaneous multi-resolution neuron circuits architecture
Investigate both health and disease
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
• The problem
• Overview of the sensorimotor control system -- Multi-loop architecture, feedback and delays -- Internal dynamic models?
• RIPID Model -- Physiology -- Stability analysis -- Cerebellar linear gainscheduling?
• Application to bipedal balance and locomotion
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Neural signals
executive
sensory
Mtr CtxBrainstem orSpinal Cord
Segment
Im AntCblL Ant
Cbl
Putamen& GP
Caudate& GP
Frontal & ParietalAssoc Ctx
BodyForce/Motion
Muscle & tendon,Joints, skin
“highest level”PLANS (strategy)
“middle level” PROGRAMS (tactics)
“lower level”ACTION
(force, velocity)
“Motor Servo”
Vestib
Visual
M. Cbl Flocc Cbl
• Human motor control principal information flow (adapted from V. Brooks, 1986)
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Brainstem orSpinal Cord
Segment
Im AntCblL Ant
Cbl
Putamen& GP
Caudate& GP
Frontal & ParietalAssoc Ctx
BodyForce/Motion
Muscle & tendon,Joints, skin
“highest level”PLANS (strategy)
“middle level” PROGRAMS (tactics)
“lower level”ACTION
(force, velocity)“Proprioceptive Motor Servo ?”
Vestib
Visual
M. Cbl Flocc Cbl
• Motor Servo Concept?
Actuator ?
Mtr Ctx
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Mtr CtxBrainstem orSpinal Cord
Segment
Im AntCblL Ant
Cbl
Putamen& GP
Caudate& GP
Frontal & ParietalAssoc Ctx
BodyForce/Motion
Muscle & tendon,Joints, skin
“highest level”PLANS (strategy)
“middle level” PROGRAMS (tactics)
“lower level”ACTION
(force, velocity)
(programmed) Stimulus- Response Loops?
Vestib
Visual
M. Cbl Flocc Cbl
• Programmed Triggered Response Loops?
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Mtr Ctx
Im AntCbl
L AntCbl
Putamen &GP, SN
M. Cbl Flocc Cbl
“high level”PROGRAMS
(discrete control) (tactics: trajectories
cues)
Frontal & ParietalAssoc Ctx
“highest level”PLANS,
ALGORITHMS (free assoc, strategy)
“intermediate level”CONTROL
(continuous control)(stability, tracking, stiffness,
scaling, movement time)
• Possible refinement of upper portion
L Post Crerebellum
Caudate & GP
Primary & Peri-Sensorimotor Ctxs
“Proprioceptive Motor Servo ?”
(programmed) Stimulus- Response Loops?
Cognitive Programming?
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Brainstem orSpinal Cord
Segment
Im AntCblL Ant
Cbl
Frontal & ParietalAssoc Ctx
BodyForce/Motion
Muscle & tendon,Joints, skin
• Important sensorimotor control issues: 1) (often) low plant stiffness
2) significant time delays/phase lags~35-40 ms round trip to elbow+ low pass filtering of neural signal at muscle
if not compensated, cause significant instability
Mtr Ctx
T
~10 ms
~8 ms
T
2/(s+)2
Primary and Peri-Sensorimotor Ctxs
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
• As a result, considerable thinking views cerebellum as an adaptive compensator that incorporates internal models of inverse and/or forward dynamics. Eg:
–
+ G PT
T2–+
T
ref
Miall et al, 1993
Smith Predictor?
ˆ P
P– +
++G T
T
ref
Kawato & Gomi, 1992
Feedback error learning ofinverse dynamics?
ˆ P 1
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
• The problem
• Overview of the sensorimotor control system -- Multi-loop architecture, feedback and delays -- Internal dynamic models?
• RIPID Model -- Structure and performance -- Stability analysis -- Cerebellar gainscheduling?
• Application to bipedal balance and locomotion
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Simpler control approaches may also fit better with what is known about cerebellar architecture
Primary Processing Elements:Purkinje Cells (PC), 1.5x107
Branch of Major Input Channels:Parallel Fibers (PF) ~1011, 1012
3-6 mm in humans (very long)& thin, very slowly conducting 0.5 m/sec
medio-lateral
Cortex of folium
• Specifically, control must be implemented by simple lattice-like modular architecture of cerebellum
(phylogenetically strongly conserved)
Deep white matter
PCPF
Operation?Activated “beam” of parallel fibers(known) ….
u(t)y(t)
control signal input (?) (Mossy Fiber to PF and DCN)
–MF
PFs
DCN
direct excitation
Inhibition by sidepath
+
y(t)= 1u(t)2u(tt)
y(t) =(12)u(t) + 2(u(t) u(tt))
Y(s)( gbs + gk)U(s)
• In light of its connectivity, we can consider that the lateral cerebellum may compute proportional and derivative signals
2 represents adaptable weight
s represents Laplace complex frequency variable
PD
u(t)1
2t
y(t)
CbCtx
Dn
y(t) (3 4 )u()d0
tY(s) ((3 4 )
1
s)U(s)
• …. and that the intermediate cerebellum may compute integral signals
I
u(t)
Ipy(t)
z(t)
CbCtx
RNmc LRN
MF
Ip
• …. Together, cerebellar regions may implement “PID” (control) circuitry
IPD
u(t)1
2t
y(t)
CbCtx
Dn
u(t)
Ipy(t)
z(t)
CbCtx
RNmc LRN
MF
Ip
spinomusculoskeletal plant with low-pass muscle activation dynamics
• … so a simple linear control system structure may be considered.
i1
–
+
s gb
–
+gk
1/sf2
+
++
ref spr
i2
–P(s)
spr
ia/s mc
neural “long-loop”signal transmissiondelays
• … so a simple linear control system structure may be considered.
i1
–
+
s gb
–
+gk
1/s
ia/s
f2
+
++
ref spr
i2
–P(s)
spr
mc
peri- and primary sensorimotor cortex including integrator anddirect paths
• … so a simple linear control system structure may be considered.
i1
–
+
s gb
–
+gk
1/s
ia/s
f2
+
++
ref spr
i2
–P(s)
spr
mc
Linear cerebellar processing
• … so a simple linear control system structure may be considered.
i1
–
+
s gb
–
+gk
1/s
ia/s
f2
+
++
ref spr
i2
–P(s)
spr
mc
• … so a simple linear control system structure may be considered.
Proposal: Stabilized feedback PID control model of cerebellum
“PID”ProportionalDerivativeIntegral
i1
–
+
s gb
–
+gk
1/s
ia/s
f2
+
++
ref spr
i2
–P(s)
spr
mc
• … so a simple linear control system structure may be considered.
Proposal: Stabilized feedback PID control model of cerebellum key added feature: integrator in feedback path to add phase-lead that stabilizes against transmission delays
i1
–
+
s gb
–
+gk
1/s
ia/s
f2
+
++
ref spr
i2
–P(s)
spr
mc
• … so a simple linear control system structure may be considered.
Key Feature: Recurrent integrator that adds phase lead to PID= “RIPID” model
i1
–
+
s gb
–
+gk
1/s
ia/s
f2
+
++
ref spr
i2
–P(s)
spr
mc
• … so a simple linear control system structure may be considered.
Key Feature: Recurrent integrator that adds phase lead to PID re-represented showing zero at origin
i1
–
+
sgb
P(s,T)T
–
+
T
ref gk
1/s
ia/s
f2
+
+
s
s i2 +
f3
0 0.5 1 1.5 2 2.5 3
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Figure R1. Impulsive disturbance response of elbow as function of f1
seconds
segmental reflex gain f1 = 0segmental reflex gain f1 = 0.4segmental reflex gain f1 = 1.5
• Postural regulation: Impulse response of linear elbow joint model to 1 Nt force for different values of segmental reflex gain
radi
ans
0 0.5 1 1.5 2 2.5 3-0.5
0
0.5
1
Figure R2a. Impulsive disturbance response of elbow with long-loop control
seconds
radi
ans
(+)segmental reflex, (-)long-loop(+)long-loop, (-)segmental reflex(+)long-loop, (+)segmental reflex
0 0.5 1 1.5 2 2.5 3-0.5
0
0.5
1
Figure R2b. Impulsive disturbance response of elbow with cocontraction, long-loop and segmental reflex control
seconds
radi
ans
(+)cocontraction, (+)segmental reflex, (-)long-loop(+)cocontraction, (+)long-loop, (-)segmental reflex(+)cocontraction, (+)long-loop, (+)segmental reflex
• Postural regulation: Impulse response of plant to 1 Nt force with contribution of RIPID-stabilized long-loop responses with and without muscular coactivation.
-60 -40 -20 0 20 40 60 80 100 120
0
50
100
150
200
250Figure 4a. Force response to sinusoidal position disturbance input
joint (angular) stiffness Nm/rad
join
t (an
gula
r) v
isco
sity
Nm
-s/r
ad
Data*Simulation
• Postural regulation: Viscous and Elastic force responses to small amplitude sinusoidal position disturbance (* Rack, 1981)
i1
–
+P(s,T)T
–
+
T
ref gk
i2
ia/s
f2
+
+– +
f3
• Effect of cerebellar lesions: lateral gk, and/or intermediate recurrent integrator i2
∫
gbd/dt
X
X
0 0.5 1 1.5 2 2.5 3-0.5
0
0.5
1
Figure R5a. Elbow disturbance response with decreased cerebellar gains i2 and gk, no cocontraction
seconds
radi
ans
i2 normal, gk normal (Figure 2a)i2 normal, gk reducedi2 reduced, gk reduced
0 0.5 1 1.5 2 2.5 3-0.5
0
0.5
1
Figure R5b. Elbow disturbance with decreased cerebellar gains i2 and gk and cocontraction
seconds
radi
ans
i2 normal, gk normal, (-)cocontractioni2 normal, gk reduced, (+)cocontractioni2 reduced, gk reduced, (+)cocontraction
• Postural regulation (pathological): Effect of reducing cerebellum-related gains gk and/or i2 , without and with muscular co-activation
• The problem
• Overview of the sensorimotor control system -- Multi-loop architecture, feedback and delays -- Internal dynamic models?
• RIPID Model -- Structure & performance -- Stability analysis -- Cerebellar gainscheduling?
• Application to bipedal balance and locomotion
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
• Without RIPID controller, cortical integrator and bypass path Start with a simple nominal linear model of spinomuscular apparatus with long loop gain K, and delays.
CNS
udesc
+
–
0
+Tcns(s)
Tcns(s)
K
Tpr(s)
(1+0.1s)
spinomusculoskeletal plant including peripheral nervedelays
P(s,T)
Natural frequencies for long-loop control, as K≥0.
-140 -120 -100 -80 -60 -40 -20 0 20-60
-40
-20
0
20
40
60
Real Axis
Ima
g A
xis
TextEnd
Root locus of total plant, delay and sf2
CNS
–
+P(s,T)
ref
ia/s
f3
Tcns(s)
Consider behavior for various values of gain K
gbs2 gks i1s i2
K
Tcns(s)1+sf2/iaf3Tpr(s)
(1+0.1s)
• With RIPID Controller, cortical integrator and bypass path
-140 -120 -100 -80 -60 -40 -20 0 20-60
-40
-20
0
20
40
60
Real Axis
Ima
g A
xis
TextEnd
PZ map of compensator, cortical integrator, total plant, loop delay, spindle and sf2
Natural frequencies (x) of RIPID model for K=0, and zeros (o) in complex plane:
Poles relatedto E-A coupling
musculoskeletaldynamics
thalamocorticalintegrator
x x x xx
x
Recurrent Integrator
-140 -120 -100 -80 -60 -40 -20 0 20-60
-40
-20
0
20
40
60
Real Axis
Ima
g A
xis
TextEnd
PZ map of compensator, cortical integrator, total plant, loop delay, spindle and sf2
Natural frequencies (x) of RIPID model for K=0, and zeros (o) in complex plane:
primary spindleafferent
recurrent integrator
controller
cortical integrator bypass path
Natural frequencies (x) of RIPID model for K > 0, and zeros (o) in complex plane:
-140 -120 -100 -80 -60 -40 -20 0 20-60
-40
-20
0
20
40
60
Real Axis
Ima
g A
xis
TextEnd
Root locus of total plant, delay and sf2
• The problem
• Overview of the sensorimotor control system -- Multi-loop architecture, feedback and delays -- Internal dynamic models?
• RIPID Model -- Structure & performance -- Stability analysis -- Cerebellar gainscheduling?
• Application to bipedal balance and locomotion
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Operation?Activated “beam” of parallel fibers(known) …. Two classes (?, speculation)
u(t)y(t)
s(t)context-specificselector input (?)(Mossy Fiber to PF)
control signal input (?) (Mossy Fiber to PF and DCN)
–
MF
MF
PFs
DCN
direct excitation
Inhibition by sidepath
+
laterally inhibited Purkinje Cells
Operation?Activated “beam” of parallel fibersfocused by lateral inhibition (reasonably established).
u(t)y(t)
s(t)context-specificselector fiber(Mossy Fiber to PF)
input signal(Mossy Fiber to PF)
–
MF
MF
PFs
+
Operation?Activated “beam” of parallel fibersand lateral inhibition.Together,Possible selection mechanism?(net behavior not definitivelyestablished)
u(t)y(t)
s(t)context-specificselector fiber(Mossy Fiber to PF)
input signal(Mossy Fiber to PF)
–
MF
MF
PFs
common teaching input Climbing fibers (CF)
+
Adaptation of selected synaptic weight?(PF - PC synapse known to be adaptive (decreases) under coincident PF - CF activity, Ito, 1984)
u(t)y(t)
s(t)context-specificselector fiber (MF-PF)
input signal (MF-PF)+–
teaching signalresponding to behavioral error(CF)
cerebellum
cerebralcortex
• A slightly enhanced version suited for gainscheduling studies:
–+ spr
–
+
ref
i2
1/s
ia/s
f2
+
+
–
+
i3
–s gb(i) S()
gk(i) S()
I1(i) S()
+mc
spr
e
P(s)+
+
+
scheduling variables“intent”
“motor command”
cerebellum
cerebralcortex
• A slightly enhanced version suited for gainscheduling studies:
–+ spr
–
+
ref
i2
1/s
ia/s
f2
+
+
–
+
i3
–s gb(i) S()
gk(i) S()
I1(i) S()
+mc
spr
e
P(s)+
+
+
scheduling variables“intent”
“motor command”
statespr
• The problem
• Overview of the sensorimotor control system -- Multi-loop architecture, feedback and delays -- Internal dynamic models?
• RIPID Model -- Structure & performance -- Stability analysis -- Cerebellar gainscheduling?
• Application to bipedal balance and locomotion
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Linear combinations of joint state signals
Gainscheduled RIPID model (with torque feedback)
applied to upright balance control
From Jo & MassaquoiBiol Cybern ***
Torque feedback
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Gainscheduling by region in state space: 1(t)×3 (t)×d1/dt n = [ n1, n2, n3 ] arbitrary state space direction vectorn = [ -1, n1, n2, n3] augmented direction vectorq(t) = [ 0, 1(t), 3(t), d/dt] bias and state signal vector
Switching plane 0 = n11(t) + n23(t) + n3d1/dt 0 = n q(t)
1(t)
3(t)d3/dt
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Switching plane 0 = n11(t) + n23(t) + n3d1/dt 0 = n q(t)
1(t)
3(t)d3/dt
Balancing region
Gainscheduling by region in state space: 1(t)×3 (t)×d1/dt n = [ n1, n2, n3 ] arbitrary state space direction vectorn = [ -1, n1, n2, n3] augmented direction vectorq(t) = [ 0, 1(t), 3(t), d/dt] bias and state signal vector
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Switching plane 0 = n11(t) + n23(t) + n3d1/dt 0 = n q(t)
1(t)
3(t)
Gainset Switching criterion(criteria for activity on given PF): -n q(t) ≥ 0 {Gk
(1), I1(1)}
n q(t) ≥ 0 {Gk(2), I1
(2)}
d3/dt
Balancing region
Gainscheduling by region in state space: 1(t)×3 (t)×d1/dt n = [ n1, n2, n3 ] arbitrary state space direction vectorn = [ -1, n1, n2, n3] augmented direction vectorq(t) = [ 0, 1(t), 3(t), d/dt] bias and state signal vector
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
1(t)
3(t)
Balance coordination trajectories in response to sudden backward platform translations (projected onto 1(t) × 3(t))
RIPID Gainscheduled model Experimental data from ***
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
RIPID-based models applied to bipedal locomotion
Simulation 1: Model of steady state walking (Jo & Massaquoi, Biol Cybern **)
Model contains
i. Two anti-synchronized 5-state on-off command signals that specify gait cadence and phase for each leg
ii. RIPID based gainscheduled feedback control of trunk pitch.iii. Fixed, linear combinations of muscle activations (synergies)iv. Piecewise linear control onlyv. No internal dynamic models
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
RIPID-based models applied to bipedal locomotion
Simulation 2: Integrated model of balance and walking (Massaquoi) Model contains
i. Two loosely coupled 5-state state machines with state transition logic that specify gait control phase for each leg
ii. No explicit specification of cadenceiii. RIPID based gainscheduled feedback control of upright posture
and COM position.iv. Fixed, linear combinations of muscle activations (synergies) based
on frog studiesv. Cerebellar control of leg movement dynamicsvi. Piecewise linear control onlyvii. No internal dynamic models
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
How does the RIPID-based model of balance and walking fare?
Key Challenges (constraints) for Neuroengineering models:
A. Models must respect known or highly suspected neuroantomical connections
B. Models must respect known time delays and phase lags (these are non-trivial)C. Models must utilize functions that are known to be achievable by collections
of neurons. (e.g. Quantitative multiplication ?)D. Models must be consistent with both normal human function, and
pathological dysfunction.E. Models of human function should be able to be related to those of animals in
a manner consistent with natural evolution (i.e. continuously)F. Models must ultimately account for the firing behavior of system neurons (i.e. must be multi-resolution)
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
How does the RIPID-based model of balance and walking fare?
Key Challenges (constraints) for Neuroengineering models:
A. Models must respect known or highly suspected neuroantomical connections
B. Models must respect known time delays and phase lags (these are non-trivial) model includes full delays and lagsC. Models must utilize functions that are known to be achievable by collections
of neurons. (e.g. Quantitative multiplication ?)D. Models must be consistent with both normal human function, and
pathological dysfunction.E. Models of human function should be able to be related to those of animals in
a manner consistent with natural evolution (i.e. continuously)F. Models must ultimately account for the firing behavior of system neurons (i.e. must be multi-resolution)
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
How does the RIPID-based model of balance and walking fare?
Key Challenges (constraints) for Neuroengineering models:
A. Models must respect known or highly suspected neuroantomical connections
B. Models must respect known time delays and phase lags (these are non-trivial) model includes full delays and lagsC. Models must utilize functions that are known to be achievable by collections
of neurons. model uses -- piecewise linear functions and low order linear filters, -- no quantitative multiplication of signals with each other, -- neural parameter specification to two significant digits or less.D. Models must be consistent with both normal human function, and
pathological dysfunction.E. Models of human function should be able to be related to those of animals in
a manner consistent with natural evolution (i.e. continuously)F. Models must ultimately account for the firing behavior of system neurons (i.e. must be multi-resolution)
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
How does the RIPID-based model of balance and walking fare?
Key Challenges (constraints) for Neuroengineering models:
A. Models must respect known or highly suspected neuroantomical connections
B. Models must respect known time delays and phase lags (these are non-trivial) model includes full delays and lagsC. Models must utilize functions that are known to be achievable by collections
of neurons. model uses -- piecewise linear functions, -- no quantitative multiplication of signals with each other, -- neural parameter specification to two significant digits or less.D. Models must be consistent with both normal human function, and
pathological dysfunction. Comparing with ataxic gait in progressE. Models of human function should be able to be related to those of animals in
a manner consistent with natural evolution (i.e. continuously)
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
How does the RIPID-based model of balance and walking fare?
Key Challenges (constraints) for Neuroengineering models:
A. Models must respect known or highly suspected neuroantomical connections
B. Models must respect known time delays and phase lags (these are non-trivial) model includes full delays and lagsC. Models must utilize functions that are known to be achievable by collections
of neurons. model uses -- piecewise linear functions, -- no quantitative multiplication of signals with each other, -- neural parameter specification to two significant digits or less.D. Models must be consistent with both normal human function, and
pathological dysfunction. Comparing with ataxic gait in progressE. Models of human function should be able to be related to those of animals in
a manner consistent with natural evolution (i.e. continuously) () -- model uses leg control synergies very similar to those of frogs
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
How does the RIPID-based model of balance and walking fare?
Key Challenges (constraints) for Neuroengineering models:
A. Models must respect known or highly suspected neuroantomical connections
B. Models must respect known time delays and phase lags (these are non-trivial) model includes full delays and lagsC. Models must utilize functions that are known to be achievable by collections
of neurons. model uses -- piecewise linear functions, -- no quantitative multiplication of signals with each other, -- neural parameter specification to two significant digits or less.D. Models must be consistent with both normal human function, and
pathological dysfunction. Comparing with ataxic gait in progressE. Models of human function should be able to be related to those of animals in
a manner consistent with natural evolution (i.e. continuously) () -- model uses leg control synergies very similar to those of frogs
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
How does the RIPID-based model of balance and walking fare?
Key Challenges (constraints) for Neuroengineering models:
F. Models must ultimately account for the firing behavior of system neurons (i.e. must be multi-resolution) () Dr. Takahashi working in
this direction.
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Preliminary implications of the model for locomotor physiology and robot control
A. Position/impedance control with series elastic actuation appears to be sufficient
B. Internal dynamic models not required for at least basic locomotor behavior
C. Internal locomotor computations may be simple, slow and require only modest accuracy
D. As a result of C, low power control hardware may be sufficient
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008
Major specific remaining goals for this type of system model
A. More demanding locomotion: Running, negotiating uneven or slippery surfaces, push disturbances etc
B. Self tuning/ adaptation after certain structural parameter change (e.g. trunk or foot mass change)
C. More complex behaviors: skipping, stair climbing, one leg hopping, reaching manual manipulation
likely to need Basal Ganglia modeling
System Level Neuroengineering Modeling of Nervous System I. Massaquoi CDC Cancun 2008