Transcript Experience-Dependent Perceptual Categorization in a

Principles Underlying the
Construction of BrainBased Devices
Jeff Krichmar
The Neurosciences Institute
San Diego, California, USA
develop
theory
compare with
empirical data
test behavior in real world
create
simulation
Construction of an Intelligent
Machine Following the
Brain Based Model

Design should be constrained by these principles:





Active sensing and autonomous movement in the
environment.
Organize the signals from the environment into
categories without a priori knowledge or instruction.
Incorporate a simulated brain with detailed neural
dynamics and neuroanatomy.
Engage in a behavioral task and adaptation of
behavior when an important environmental event
occurs.
Allow comparisons with experimental data acquired
from animal systems.
Active Sensing and
Autonomous Movement in the
Environment
Darwin IV-VI
1992 - 1998
Darwin VII-VIII
1999 - 2002
Darwin IX-X
2003 - present
BrainWorks
2004 - present
Organize the Signals from the
Environment into Categories without
A Priori Knowledge or Instruction
Seth et al, Cerebral Cortex, November 2004, V 14 N 11
Fabre-Thorpe, Phil. Trans. R. Soc. Lond. B (2003) 358, 1215–1223
Incorporate A Simulated Brain
With Detailed Neural Dynamics
And Neuroanatomy
Incorporate A Simulated Brain With
Detailed Neural Dynamics And
Camera
Neuroanatomy
ODOMETRY
V1
Color
V1
Width
V2/4
Color
V2/4
Width
IT
Pr
HD
ATN
MHDG
ECinFB
DGFB
CA3FB
CA1FB
ECin
DG
CA3
CA1
Cortex
CA3FF
CA1FF
ECout
HIPPOCAMPUS
BF
S
ECoutFB
R+
S
voltage independent
R-
voltage dependent
IR
Platform
IR
Wall
MOTOR
inhibitory
plastic
value dependent
MHDG
Engage in a Behavioral Task And
Adapt Behavior When An Important
Environmental Event Occurs
Engage in a Behavioral Task And
Adapt Behavior When An Important
Environmental Event Occurs
Allow Comparisons with Experimental
Data Acquired from Animal Systems
Allow Comparisons with Experimental
Data Acquired from Animal Systems
CA1
CA3
ECout
DG
ECin
Incorporate A Simulated Brain
With Detailed Neural Dynamics
And Neuroanatomy
predictive
input
reflex response
= error signal
reflex
“Preflex”
Incorporate A Simulated Brain With
Detailed Neural Dynamics And
Neuroanatomy
Camera
excitatory
Motion Area (MT)


inhibitory
 
climbing fiber
error signal
Pre-Cerebellar Nuclei
      
Inferior
Olive
Turn
Error
signal
IR
Turn
Purkinje Cells
Turn
LTD LTD
Deep
Cerebellar Nuclei
Turn
Reflex
“Preflex”
Motor
Turn
LTP
Purkinje Cells
Velocity
Deep
Cerebellar Nuclei
Velocity
“Preflex”
Motor
Velocity
Reflex
Inferior
Olive
Velocity
Error
signal
IR
Velocity
Engage in a Behavioral Task and
Adapt Behavior when an Important
Environmental Event Occurs
Un-Trained
Trained
Allow Comparisons with Experimental
Data Acquired from Animal Systems
LTD
Pre-Cerebellar Nuclei
      
LTD
Purkinje Cells
Velocity

Weight Matrices (initially, all weights were equal)

Pre-Cerebellar-NucleiPurkinje Cells for velocity

White = maximum

Black = minimum
More widespread LTD for sharper courses results in lower velocity

Development of Intelligent Machines that
follow Neurobiological and Cognitive
Principles in their Construction
Build A Brain Team
Don
Jeff Hutson Anil
McKinstry
Seth
Jason
Fleischer
Jeff
Krichmar
Botond
Szatmary Jim
Snook
Thomas
Alisha Allen
Lawson
Brian
Cox
Donatello
Darwin X
Darwin V
BrainWorks
Segway B
Construction of an Intelligent
Machine Following the
Brain Based Model

Design should be constrained by these principles:





Active sensing and autonomous movement in the
environment.
Organizing the signals from the environment into
categories without a priori knowledge or instruction.
Incorporating a simulated brain with detailed neural
dynamics and neuroanatomy.
Engaging in a behavioral task and adaptation of
behavior when an important environmental event
occurs.
Allowing comparisons with experimental data
acquired from animal systems.