Catalyzing Next-Generation Artificial Intelligence through NeuroAI
By Anthony Zador*, Sean Escola*, Blake Richards, Bence Ölveczky, Yoshua Bengio, Kwabena Boahen,
Matthew Botvinick,  Dmitri Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya
Ganguli, Jeff Hawkins, Konrad Körding, Alexei Koulakov, Yann LeCun, Timothy Lillicrap, Adam
Marblestone, Bruno Olshausen, Alexandre Pouget, Cristina Savin, Terrence Sejnowski, Eero Simoncelli,
Sara Solla, David Sussillo, Andreas S. Tolias, Doris Tsao
* Equal contributions
Abstract
:
Neuroscience
has
long
been
an
essential
driver
of
progress
in
artificial
intelligence
(AI).
We
propose
that
to
accelerate
progress
in
AI,
we
must
invest
in
fundamental
research
in
NeuroAI.
A
core
component
of
this
is
the
embodied
Turing
test,
which
challenges
AI
animal
models
to
interact
with
the
sensorimotor
world
at
skill
levels
akin
to
their
living
counterparts.
The
embodied
Turing
test
shifts
the
focus
from
those
capabilities
like
game
playing
and
language
that
are
especially
well-developed
or
uniquely
human
to
those
capabilities
–
inherited
from
over
500
million
years
of
evolution
–
that
are
shared
with
all
animals.
Building
models
that
can
pass
the
embodied
Turing
test
will
provide
a
roadmap
for the next generation of AI.
Over
the
coming
decades,
Artificial
Intelligence
(AI)
will
transform
society
and
the
world
economy
in
ways
that
are
as
profound
as
the
computer
revolution
of
the
last
half
century
and
likely
at
an
even
faster
pace.
This
AI
revolution
presents
tremendous
opportunities
to
unleash
human
creativity
and
catalyze
economic
growth,
relieving
workers
from
performing
the
most
dangerous
and
menial
jobs.
However,
to
reach
this
potential,
we
still
require
advances
that
will
make
AI
more
human-like
in
its
capabilities.
Historically,
neuroscience
has
been
a
critical
driver
and
source
of
inspiration
for
improvements
in
AI,
particularly
those
that
made
AI
more
proficient
in
areas
that
humans
and
other
animals
excel
at,
such
as
vision,
reward-based
learning,
interacting
with
the
physical
world,
and
language
1,2
.
It
can
still
play
this
role.
To
accelerate
progress
in
AI
and
realize
its
vast
potential,
we
must
invest
i
n
fundamental
research
in
“NeuroAI.”
The
seeds
of
the
current
AI
revolution
were
planted
decades
ago,
mainly
by
researchers
attempting
to
understand
how
brains
compute
3
.
Indeed,
the
earliest
efforts
to
build
an
“artificial
brain”
led
to
the
invention
of
the
modern
“von
Neumann
computer
architecture,”
for
which
John
von
Neumann
explicitly
drew
upon
the
very
limited
knowledge
of
the
brain
available
to
him
in
the
1940s
4,5
.
Later,
the
Nobel-prize
winning
work
of
David
Hubel
and
Torsten
Wiesel
on
visual
processing
circuits
in
the
cat
neocortex
inspired
the
deep
convolutional
networks
that
have
catalyzed
the
recent
revolution
in
modern
AI
6–8
.
Similarly,
the
development
of
reinforcement
learning
was
directly
inspired
by
insights
into
animal
behavior
and
neural
activity
during
learning
9–15
.
Now,
decades
later,
applications
of
ANNs
and
RL
are
coming
so
quickly
that
many
observers
assume
that
the
long-elusive
goal
of
human-level
intelligence
–
sometimes
referred
to
as
“artificial
general
intelligence”
–
is
within
our
grasp.
However,
in
contrast
to
the
optimism
of
those
outside
the
field,
many
front-line
AI
researchers
believe
that
major
breakthroughs
are

needed
before
we
can
build
artificial
systems
capable
of
doing
all
that
a
human,
or
even
a
much
simpler
animal like a mouse, can do.
Although
AI
systems
can
easily
defeat
any
human
opponent
in
games
such
as
chess
16
and
Go
17
,
they
are
not
robust
and
often
struggle
when
faced
with
novel
situations.
Moreover,
we
have
yet
to
build
effective
systems
that
can
walk
to
the
shelf,
take
down
the
chess
set,
set
up
the
pieces,
and
move
them
around
during
a
game,
although
recent
progress
is
encouraging
18
.
Similarly,
no
machine
can
build
a
nest,
forage
for
berries,
or
care
for
young.
Today’s
AI
systems
cannot
compete
with
the
sensorimotor
capabilities
of
a
four-year
old
child
or
even
simple
animals.
Many
basic
capacities
required
to
navigate
new
situations
–
capacities
that
animals
have
or
acquire
effortlessly
–
turn
out
to
be
deceptively
challenging
for
AI,
partly
because
AI
systems
lack
even
the
basic
abilities
to
interact
with
an
unpredictable
world.
A
growing
number
of
AI
researchers
doubt
that
merely
scaling
up
current
approaches
will
overcome
these
limitations.
Given
the
need
to
achieve
more
natural
intelligence
in
AI,
it
is
quite
likely
that
new
inspiration from naturally intelligent systems is needed
19
.
Historically,
many
key
AI
advances,
such
as
convolutional
ANNs
and
reinforcement
learning,
were
inspired
by
neuroscience.
Neuroscience
continues
to
provide
guidance
–
e.g.,
attention-based
neural
networks
were
loosely
inspired
by
attention
mechanisms
in
the
brain
20–23
–
but
this
is
often
based
on
findings
that
are
decades
old.
The
fact
that
such
cross-pollination
between
AI
and
neuroscience
is
far
less
common
than
in
the
past
represents
a
missed
opportunity.
Over
the
last
decades,
through
efforts
such
as
the
NIH
BRAIN
initiative
and
others,
we
have
amassed
an
enormous
amount
of
knowledge
about
the
brain.
The
emerging
field
of
NeuroAI,
at
the
intersection
of
neuroscience
and
AI,
is
based
on
the
premise
that
a
better
understanding
of
neural
computation
will
reveal
fundamental
ingredients
of
intelligence
and
catalyze
the
next
revolution
in
AI.
This
will
eventually
lead
to
artificial
agents
with
capabilities
that
match
those
of
humans.
The
NeuroAI
program
we
advocate
is
driven
by
the
recognition
that
AI
historically
owes
much
to
neuroscience
and
the
promise
that
AI
will
continue
to
learn
from
it–but
only
if
there
is
a
large
enough
community
of
researchers
fluent
in
both
domains.
We
believe
the
time
is
right
for
a
large-scale
effort
to
identify
and
understand
the
principles
of
biological
intelligence
and
abstract those for application in computer and robotic systems.
It
is
tempting
to
focus
on
the
most
characteristically
human
aspects
of
intelligent
behavior,
such
as
abstract
thought
and
reasoning.
However,
the
basic
ingredients
of
intelligence
–
adaptability,
flexibility,
and
the
ability
to
make
general
inferences
from
sparse
observations
–
are
already
present
in
some
form
in
basic
sensorimotor
circuits
which
have
been
evolving
for
hundreds
of
millions
of
years.
As
AI
pioneer
Hans
Moravec
24
put
it,
abstract
thought
“is
a
new
trick,
perhaps
less
than
100
thousand
years
old….effective
only
because
it
is
supported
by
this
much
older
and
much
more
powerful,
though
usually
unconscious,
sensorimotor
knowledge.”
This
implies
that
the
bulk
of
the
work
in
developing
general
AI
can
be
achieved
by
building
systems
that
match
the
perceptual
and
motor
abilities
of
animals
and
that
the
subsequent
step
to
human-level
intelligence
would
be
considerably
smaller.
This
is
good
news
because
progress
on
the
first
goal
can
rely
on
the
favored
subjects
of
neuroscience
research
–
rats,
mice,
and
non-human
primates
–
for
which
extensive
and
rapidly
expanding
behavioral
and
neural
datasets
can
guide
the
way.
Thus,
we
believe
that
the
NeuroAI
path
will
lead
to
necessary
advances
if
we
figure
out
the
core capabilities that all animals possess in embodied sensorimotor interaction with the world.

NeuroAI Grand Challenge: The Embodied Turing Test
In
1950,
Alan
Turing
proposed
the
“imitation
game”
as
a
test
of
a
machine’s
ability
to
exhibit
intelligent
behavior
indistinguishable
from
that
of
a
human
25
(Figure
1,
left).
In
that
game,
now
known
as
the
Turing
test,
a
human
judge
evaluates
natural
language
conversations
between
a
real
human
and
a
machine
trained
to
mimic
human
responses.
By
focusing
on
conversational
abilities,
Turing
evaded
asking
whether
a
machine
could
“think,”
a
question
he
considered
impossible
to
answer.
The
Turing
test
is
based
on
the
implicit
belief
that
language
represents
the
pinnacle
of
human
intelligence
and
that
a
machine
capable
of
conversation must surely be intelligent.
Until
recently,
no
artificial
system
could
come
close
to
passing
the
Turing
test.
However,
a
class
of
modern
AI
systems
called
“large
language
models”
can
now
engage
in
surprisingly
cogent
conversations
26
.
In
part,
their
success
reveals
how
easily
we
can
be
tricked
into
imputing
intelligence,
agency,
and
even
consciousness
to
our
interlocutor
27
.
Impressive
though
these
systems
are,
because
they
are
not
grounded
in
real-world
experiences,
they
nonetheless
continue
to
struggle
with
many
basic
aspects
of
causal
reasoning
and
physical
common
sense.
Thus,
the
Turing
test
does
not
probe
our
prodigious
perceptual
and
motor
abilities
to
interact
with
and
reason
about
the
physical
world,
abilities
shared
with
animals and honed through countless generations of natural selection.
We
therefore
propose
an
expanded
“embodied
Turing
test,”
one
that
includes
advanced
sensorimotor
abilities
(Figure
1,
right).
The
spirit
of
the
original
Turing
test
was
to
establish
a
simple
qualitative
standard
against
which
our
progress
toward
building
artificially
intelligent
machines
can
be
judged.
This
embodied
Turing
test
would
benchmark
and
compare
the
interactions
with
the
world
of
artificial
systems
versus
humans
and
other
animals.
Similar
ideas
have
been
proposed
previously
28–32
.
However,
in
light
of
recent
advances
enabling
large-scale
behavioral
and
neural
measurements,
as
well
as
large-scale
simulations
of
embodied
agents
in
silico,
we
believe
the
time
is
ripe
to
instantiate
a
major
research
effort
in
this
direction.
Because
each
animal
has
its
own
unique
set
of
abilities,
each
animal
defines
its
own
embodied
Turing
test:
An
artificial
beaver
might
be
tested
on
its
ability
to
build
a
dam,
and
an
artificial
squirrel
on
its
ability
to
jump
through
trees.
Nonetheless,
many
core
sensorimotor
capabilities
are
shared
by
almost
all
animals,
and
the
ability
of
animals
to
rapidly
evolve
the
sensorimotor
skills
needed
to
adapt
to
new
environments
suggests
that
these
core
skills
provide
a
solid
foundation.
This
implies
that
after
developing
an
AI
system
to
faithfully
reproduce
the
behavior
of
one
species,
the
adaptation
of
this
system
to
other
species
–
and
even
to
humans
–
may
be
straightforward.
Below
we
highlight
a
few
of
the
characteristics that are shared across species.

Figure
1.
Turing
tests:
comparisons
between
the
performance
of
AI
systems
and
their
living
counterparts.
Left
:
The
original
Turing
test
as
proposed
by
Alan
Turing
25
.
If
a
human
tester
cannot
determine
whether
their
interlocutor
is
an
AI
system
or
another
human,
the
AI
passes
the
test.
Modern
large
language
models
have
made
substantial
progress
towards
passing
this
test
26
.
Right
:
The
embodied
Turing
test.
An
AI
animal
model
–
whether
robotic
or
in
simulation
–
passes
the
test
if
its
behavior
is
indistinguishable
from
that
of
its
living
counterpart.
No
AI
systems
are
close
to
passing
this
test.
Here,
an
artificial
beaver
is
tested on the species-specific behavior of dam construction.
Animals
engage
their
environments.
The
defining
feature
of
animals
is
their
ability
to
move
around
and
interact
with
their
environment
in
purposeful
ways.
Despite
recent
advances
in
optimal
control,
reinforcement
learning,
and
imitation
learning,
robotics
is
still
far
from
achieving
animal-level
abilities
in
controlling
their
bodies
and
manipulating
objects,
even
in
simulation.
Of
course,
neuroscience
can
provide
guidance
about
the
kinds
of
modular
and
hierarchical
architectures
that
could
be
adapted
to
artificial
systems
to
give
them
these
capabilities
33
.
It
can
also
provide
us
with
design
principles
like
partial
autonomy
(how
low-level
modules
in
a
hierarchy
act
semi-autonomously
in
the
absence
of
input
from
high-level
modules)
and
amortized
control
(how
movements
generated
at
first
by
a
slow
planning
process
are
eventually
transferred
to
a
fast
reflexive
system).
These
principles
could
guide
the
design
of
systems
for
perception,
action
selection,
locomotion,
and
fine-grained
control
of
limbs,
hands,
and
fingers.
Understanding
how
specific
neural
circuits
participate
in
different
tasks
could
also
inspire
solutions
for
other
forms
of
‘intelligence,’
including
in
more
cognitive
realms.
For
example,
we
speculate
that
incorporating
principles
of
circuitry
for
low-level
motor
control
could
help
provide
a
better
basis
for
higher-level motor planning in AI systems.
Animals
behave
flexibly.
Another
goal
is
to
develop
AI
systems
that
can
engage
a
large
repertoire
of
flexible
and
diverse
tasks
in
a
manner
that
echoes
the
incredible
range
of
behaviors
that
individual
animals
can
generate.
Modern
AI
can
easily
learn
to
outperform
humans
at
video
games
like
Breakout
using
nothing
more
than
pixels
on
a
screen
and
game
scores
34
.
However,
these
systems,
unlike
human
players,
are
brittle
and
highly
sensitive
to
small
perturbations:
changing
the
rules
of
the
game
slightly,
or
even
a
few
pixels
on
the
input,
can
lead
to
catastrophically
poor
performance
35
.
This
is
because
these
systems
learn
a
mapping
from
pixels
to
actions
that
need
not
involve
an
understanding
of
the
agents
and
objects
in
the
game
and
the
physics
that
governs
them.
Similarly,
a
self-driving
car
does
not
inherently
know
about
the
danger
of
a
crate
falling
off
a
truck
in
front
of
it
unless
it
has
literally
seen
examples
of

crates
falling
off
trucks
leading
to
bad
outcomes.
And
even
if
it
has
been
trained
on
the
dangers
of
falling
crates,
the
system
might
consider
an
empty
plastic
bag
being
blown
out
of
the
car
in
front
of
it
as
an
obstacle
to
avoid
at
all
costs
rather
than
an
irritant,
again,
because
it
doesn’t
actually
understand
what
a
plastic
bag
is
or
how
unthreatening
it
is
physically.
This
inability
to
handle
scenarios
that
have
not
appeared in the training data is a significant challenge to widespread reliance on AI systems.
To
be
successful
in
an
unpredictable
and
changing
world,
an
agent
must
be
flexible
and
master
novel
situations
by
using
its
general
knowledge
about
how
such
situations
are
likely
to
unfold.
This
is
arguably
what
animals
do.
Animals
are
born
with
most
of
the
skills
needed
to
thrive
or
can
rapidly
acquire
them
from
limited
experience,
thanks
to
their
strong
foundation
in
real-world
interaction,
courtesy
of
evolution
and
development
36
.
Thus,
it
is
clear
that
training
from
scratch
for
a
specific
task
is
not
how
animals
obtain
their
impressive
skills;
animals
do
not
arrive
into
the
world
tabula
rasa
and
then
rely
on
large
labeled
training
sets
to
learn.
Although
machine
learning
has
been
pursuing
approaches
for
sidestepping
this
tabula
rasa
limitation,
including
self-supervised
learning,
transfer
learning,
continual
learning,
meta
learning,
one-shot
learning
and
imitation
learning
37
,
none
of
these
approaches
comes
close
to
achieving
the
flexibility
found
in
most
animals.
Thus,
we
argue
that
understanding
the
neural
circuit-level
principles
that
provide
the
foundation
for
behavioral
flexibility
in
the
real-world,
even
in
simple
animals,
has
the
potential
to
greatly
increase
the
flexibility
and
utility
of
AI
systems.
Put
another
way,
we
can
greatly
accelerate
our
search
for
general-purpose
circuits
for
real-world
interaction
by
taking
advantage
of
the
optimization process that evolution has already engaged in
38–45
.
Animals
compute
efficiently.
One
important
challenge
for
modern
AI
–
that
our
brains
have
overcome
–
is
energy
efficiency.
Training
a
neural
network
requires
enormous
amounts
of
energy.
For
example,
training
a
large
language
model
such
as
GPT-3
requires
over
1000
megawatts-hours,
enough
to
power
a
small
town
for
a
day
46
.
Biological
systems
are,
by
contrast,
much
more
energy
efficient:
The
human
brain
uses
about
20
watts
47
.
The
difference
in
energy
requirement
between
brains
and
computers
derives
from
differences
in
information
processing.
First,
at
an
algorithmic
level,
modern
large-scale
ANNs,
such
as
large
language
models
26
,
rely
on
very
large
feedforward
architectures
with
self-attention
to
process
sequences
over
time
23
,
ignoring
the
potential
power
of
recurrence
for
processing
sequential
information.
One
reason
for
this
is
that
currently
we
do
not
have
efficient
mechanisms
for
credit
assignment
calculations
in
recurrent
networks.
In
contrast,
brains
utilize
flexible
recurrent
architectures
that
can
solve
the
temporal
credit
assignment
problem
with
great
efficiency.
Uncovering
the
mechanisms
by
which
this
happens
could
potentially
enable
us
to
increase
the
energy
efficiency
of
artificial
systems.
Alternatively,
it
has
been
proposed
that
the
synaptic
dynamics
within
adjacent
dendritic
spines
could
serve
as
a
mechanism
for
learning
sequential
structure,
a
scheme
that
could
potentially
be
efficiently
implemented
in
hardware
48
.
Second,
at
an
implementation
level,
neural
circuits
differ
from
digital
computers.
Neural
circuits
compute
effectively
despite
the
presence
of
unreliable
or
"noisy"
components.
For
example,
synaptic
release,
the
primary
means
of
communication
between
neurons,
can
be
so
unreliable
that
only
one
in
every
ten
messages
is
transmitted
49
.
Furthermore,
neurons
interact
mainly
by
transmitting
action
potentials
(spikes),
an
asynchronous
communication
protocol.
Like
the
interactions
between
conventional
digital
elements,
the
output
of
a
neuron
can
be
viewed
as
a
string
of
0s
and
1s;
but
unlike
a
digital
computer,
the
energy
cost
of
a
“1”
(i.e.
of
a
spike)
is
several
orders
of
magnitude
higher
than
that
of
a
“0”
50
.
Because
biological
circuits
operate
in
a
regime
where
spikes
are
sparse
–
even
very
active
neurons

rarely
fire
at
more
than
100
spikes
per
second
and
typical
cortical
firing
rates
may
be
less
than
1
spike/second
–
they
are
much
more
energy
efficient
51
.
Spike-based
computation
has
also
been
shown
to
be
orders of magnitude faster and more energy efficient in recent hardware implementation
52
.
A roadmap for solving the embodied Turing test
How
might
artificial
systems
that
pass
the
embodied
Turing
test
be
developed?
One
natural
approach
would
be
to
do
so
incrementally,
guided
by
our
evolutionary
history.
For
example,
almost
all
animals
engage
in
goal-directed
locomotion;
they
move
toward
some
stimuli
(e.g.
food
sources)
and
away
from
others
(e.g.
threats).
Layered
on
top
of
these
foundational
abilities
are
more
sophisticated
skills,
such
the
ability
to
combine
different
streams
of
sensory
information
(e.g.
visual
and
olfactory),
to
use
this
sensory
information
to
distinguish
food
sources
and
threats,
to
navigate
to
previous
locations,
to
weigh
possible
rewards
and
threats
to
achieve
goals,
and
to
interact
with
the
world
in
precise
ways
in
service
of
these
goals.
Most
of
these
–
and
many
other
–
sophisticated
abilities
are
found
to
some
extent
in
even
very
simple
organisms,
such
as
worms.
In
more
complex
animals,
such
as
fish
and
mammals,
these
abilities
are
elaborated and combined with new strategies to enable more powerful behavioral strategies.
This
evolutionary
perspective
suggests
a
strategy
for
passing
the
embodied
Turing
test
by
breaking
it
down
into
a
series
of
incrementally
challenging
ones
that
build
on
each
other,
and
iteratively
optimizing
on
this
series
53
.
Specifically,
the
embodied
Turing
test
comprises
challenges
that
include
a
wide
range
of
organisms
used
in
neuroscience
research,
including
worms,
flies,
fish,
rodents
and
primates.
This
would
enable
us
to
deploy
the
vast
amount
of
knowledge
we
have
accumulated
about
the
behavior,
biomechanics,
and
neural
mechanisms
of
these
model
organisms
to
both
precisely
define
each
species-specific
embodied
Turing
test
and
serve
as
strong
inductive
biases
to
guide
the
development
of
robust AI controllers that can pass it.
The
performance
of
these
artificial
agents
could
be
compared
with
that
of
animals.
Rich
behavioral
datasets
representing
a
large
swath
of
a
species’
ethogram
have
now
been
collected
and
can
be
deployed
to
benchmark
performance
on
species-specific
embodied
Turing
tests.
Furthermore,
these
datasets
are
being
rapidly
expanded
given
new
tools
in
3D
videography
54–57
.
Additionally,
detailed
biomechanical
measurements
support
highly
realistic
animal
body
models,
complete
with
skeletal
constraints,
muscles,
tendons,
and
paw
features
58
.
Combined
with
the
open-sourcing
of
powerful,
fast
physics
simulators
and
virtual
environments
59,60
,
these
models
will
afford
the
opportunity
for
embodied
Turing
test
research
to
be
performed
in
silico
at
scale
33
.
Finally,
existing
extensive
neural
datasets
with
simultaneous
neural
recordings
across
multiple
brain
regions
during
behavior,
combined
with
increasingly
detailed
neural
anatomy
and
connectomics,
provide
a
powerful
roadmap
for
the
design
of
AI
systems
that
can
control
virtual
animals
to
recapitulate
the
behaviors
of
their
in
vivo
counterparts
and
thus
pass
the
embodied
Turing test.
Importantly,
the
specifics
of
the
embodied
Turing
test
for
each
species
can
be
tuned
to
the
needs
of
different
groups
of
researchers.
We
can
test
the
capacity
of
AI
systems
in
terms
of
sensorimotor
control,
self-supervised
and
continual
learning,
generalization,
memory-guided
behavior
on
both
short
and

life-long
timescales,
and
social
interactions.
Despite
these
potentially
different
areas
of
interest,
the
challenges
that
compose
the
embodied
Turing
test
can
be
standardized
to
permit
the
quantification
of
progress
and
comparison
between
research
efforts.
Standardization
can
be
fostered
by
stakeholders
including
government
and
private
funders,
large
research
organizations
such
as
the
Allen
Institute,
and
major
collaborations
like
the
International
Brain
Lab,
with
an
eye
toward
the
development
of
common
APIs
and
support
for
competitions
as
has
been
an
important
impetus
for
much
progress
in
machine
learning
and
robotics
61,62
.
Ultimately,
virtual
organisms
that
demonstrate
successful
recapitulation
of
behaviors
of
interest
can
be
adapted
to
the
physical
world
with
additional
efforts
in
robotics
and
deployed
to solve real-world problems.
What we need
Achieving
these
goals
will
require
significant
resources
deployed
in
three
main
areas.
First,
we
must
train
a
new
generation
of
AI
researchers
who
are
equally
at
home
in
engineering/computational
science
and
neuroscience
.
These
researchers
will
chart
fundamentally
new
directions
in
AI
research
by
drawing
on
decades
of
progress
in
neuroscience.
The
greatest
challenge
will
be
in
determining
how
to
exploit
the
synergies
and
overlaps
in
neuroscience,
computational
science,
and
other
relevant
fields
to
advance
our
quest:
identifying
what
details
of
the
brain’s
circuitry,
biophysics,
and
chemistry
are
important
and
what
can
be
disregarded
in
the
application
to
AI.
There
is
thus
a
critical
need
for
researchers
with
dual
training
in
AI
and
neuroscience
to
apply
insights
from
neuroscience
to
advance
AI
and
to
help
design
experiments
that
generate
new
insights
relevant
to
AI.
Although
there
is
already
some
research
of
this
type,
it
exists
largely
at
the
margins
of
mainstream
neuroscience;
training
in
neuroscience
has
thus
far
been
motivated
and
funded
mainly
by
the
goal
of
improving
human
health
and
of
understanding
the
brain
as
such.
This
lack
of
alignment
between
fields
might
explain,
e.g.
,
the
multi-decade
gap
between
Hubel
and
Wiesel’s
discovery
of
the
structure
of
the
visual
system
6
and
the
development
and
application
of
convolutional
neural
networks
in
modern
machine
learning
8
.
Thus,
the
success
of
a
NeuroAI
research
program
depends
on
the
formation
of
a
community
of
researchers
for
whom
the
raison
d'être
of
their
training
is
to
exploit
synergies
between
neuroscience
and
AI.
Explicit
design
of
new
training
programs
can
ensure
that
the
NeuroAI
research
community
reflects
the
demographics
of
society
as
a
whole
and
is
equipped
with
the
ethical tools needed to ensure that the development of AI benefits society
63
.
Second,
we
must
create
a
shared
platform
capable
of
developing
and
testing
these
virtual
agents.
One
of
the
greatest
technical
challenges
that
we
will
face
in
creating
an
iterative,
embodied
Turing
test
and
evolving
artificial
organisms
to
pass
it
is
the
amount
of
computational
power
required.
Currently,
training
just
one
large
neural
network
model
on
a
single
embodied
task
(e.g.
control
of
a
body
in
3-dimensional
space)
can
take
days
on
specialized
distributed
hardware
64
.
For
multiple
research
groups
to
iteratively
work
together
to
optimize
and
evaluate
a
large
number
of
agents
over
multiple
generations
on
increasingly
complex
embodied
Turing
tests,
a
large
investment
in
a
shared
computational
platform
will
be
required.
Much
like
a
particle
accelerator
in
physics
or
large
telescope
in
astronomy,
this
sort
of
large-scale
shared
resource
will
be
essential
for
moving
the
brain-inspired
AI
research
agenda
forward.
It
will
require
a
major
organizational
effort,
with
government
and
ideally
also
industry
support,
that
has
as
its central goal scientific progress on animal and human-like intelligence.

Third,
we
must
support
fundamental
theoretical
and
experimental
research
on
neural
computation
.
We
have
learned
a
tremendous
amount
about
the
brain
over
the
last
decades,
through
the
efforts
of
the
NIH,
in
no
small
measure
due
to
the
BRAIN
Initiative,
and
other
major
funders
,
and
we
are
now
reaching
an
understanding
of
the
vast
diversity
of
the
brain’s
individual
cellular
elements,
neurons,
and
how
they
function
as
parts
of
simple
circuits.
With
these
building
blocks
in
place,
we
are
poised
to
shift
our
focus
toward
understanding
how
the
brain
functions
as
an
integrated
intelligent
system.
This
will
require
insight
into
how
a
hundred
billion
neurons
of
a
thousand
different
types,
each
one
communicating
with
thousands
of
other
neurons,
with
variable,
adaptable
connections,
are
wired
together,
and
the
computational
capabilities
–
the
intelligence
–
that
emerges.
We
must
reverse
engineer
the
brain
to
abstract
the
underlying
principles.
Taking
advantage
of
the
powerful
synergies
between
neuroscience
and
AI
will
require
program
and
infrastructure
support
to
organize
and
enable
research
across
the
disciplines
at
a
large
scale.
Fortunately,
there
is
now
broad
political
agreement
that
investments
in
AI
research
are
essential
to
humanity’s
technological
future.
Indeed,
IARPA
(Intelligence
Advanced
Research
Projects
Activity)
was
a
pioneer
in
this
field,
launching
the
Machine
Intelligence
from
Cortical
Networks
(MICrONS)
project.
This
project
spearheaded
the
collection
of
an
unprecedented
data
set
consisting
of
a
portion
of
a
mouse
connectome
and
associated
functional
responses
with
the
specific
goal
of
catalyzing
the
development
of
next-generation
AI
algorithms
65
.
Nonetheless,
community-wide
efforts
to
bridge
the
fields
of
neuroscience
and
AI
will
require
robust
investments
from
government
resources,
as
well
as
oversight
of
project
milestones,
commercialization
support,
ethics,
and
big
bets
on
innovative
ideas.
In
the
U.S.,
there
are
currently
some
lines
of
federal
resourcing,
such
as
the
NSF’s
National
Artificial
Intelligence
Research
Institutes,
explicitly
dedicated
to
driving
innovation
and
discovery
in
AI
from
neuroscience
research,
but
these
are
largely
designed
to
support
a
traditional
academic
model
with
different
groups
investigating
different
questions,
rather
than
the
creation
of
a
centralized
effort
that
could
create
something
like
the
embodied
Turing
test.
Likewise,
AI
support
grants
in
the
U.S.
are
predominantly
ancillary
programs
through
the
NIH,
NSF,
DoD,
and
even
the
EPA
–
each
of
which
have
their
own
directives
and
goals
–
and
this
pattern
is
shared
by
funding
agencies
globally.
This
leaves
a
significant
funding
gap
for
technology
development
as
an
end
in
itself.
The
creation
o
f
overarching
directives
either
through
existing
entities,
or
as
a
stand-alone
agency,
to
support
NeuroAI
and
AI
research
would
drive
this
mission
and
consequently
unlock the potential for AI to benefit humanity.
Conclusions
Despite
the
long
history
of
neuroscience
driving
advances
in
AI
and
the
tremendous
potential
for
future
advances,
most
engineers
and
computational
scientists
in
the
field
are
unaware
of
the
history
and
opportunities.
The
influence
of
neuroscience
on
shaping
the
thinking
of
von
Neumann,
Turing
and
other
giants
of
computational
theory
are
rarely
mentioned
in
a
typical
computer
science
curriculum.
Leading
AI
conferences
such
as
NeurIPS,
which
once
served
to
showcase
the
latest
advances
in
both
computational
neuroscience
and
machine
learning,
now
focus
almost
exclusively
on
the
latter.
Even
some
researchers
aware
of
the
historical
importance
of
neuroscience
in
shaping
the
field
often
argue
that
it
has
lost
its

relevance.
“Engineers
don’t
study
birds
to
build
better
planes”
is
the
usual
refrain.
But
the
analogy
fails,
in
part
because
pioneers
of
aviation
did
indeed
study
birds
66,67
,
and
some
still
do
68,69
.
Moreover,
the
analogy
fails
also
at
a
more
fundamental
level:
The
goal
of
modern
aeronautical
engineering
is
not
to
achieve
“bird-level”
flight,
whereas
a
major
goal
of
AI
is
indeed
to
achieve
(or
exceed)
“human-level”
intelligence.
Just
as
computers
exceed
humans
in
many
respects,
such
as
the
ability
to
compute
prime
numbers,
so
too
do
planes
exceed
birds
in
characteristics
such
as
speed,
range
and
cargo
capacity.
But
if
the
goal
of
aeronautical
engineers
were
indeed
to
build
a
machine
with
the
“bird-level”
ability
to
fly
through
dense
forest
foliage
and
alight
gently
on
a
branch,
they
would
be
well-advised
to
pay
very
close
attention
to
how
birds
do
it.
Similarly,
if
AI
aims
to
achieve
animal-level
common-sense
sensorimotor
intelligence,
researchers
would
be
well-advised
to
learn
from
animals
and
the
solutions
they
evolved
to
behave in an unpredictable world.
Acknowledgements
This
Perspective
grew
out
of
discussions
following
the
first
conference
From
Neuroscience
to
Artificially
Intelligent
Systems
(NAISys),
held
in
CSHL
in
2020,
and
includes
many
of
the
invited
speakers
from
that
meeting.
A.Z.
would
like
to
thank
Cat
Donaldson
for
convincing
him
that
this
manuscript
would
be
worth
writing,
and
for
contributing
to
early
versions.
B.Ö.
would
like
to
acknowledge
valuable
contributions
on
the
manuscript
from
Diego
Aldorando.
The
authors
would
like
to
acknowledge
the
following
funding
sources
–
A.Z.:
Schmidt
Foundation,
Eleanor
Schwartz
Foundation,
Robert
Lourie
Foundation;
S.E.:
NIH
5U19NS104649,
5R01NS105349;
R.B.:
CIFAR,
NSERC
RGPIN-2020-05105,
RGPAS-2020-00031;
B.Ö.:
NIH
R01NS099323,
R01GM136972;
Y.B.:
CIFAR;
K.B.:
Stanford
Institute
for
Human-Centered
Artificial
Intelligence,
NSF
2223827;
A.C.:
NIH
U19NS123716;
J.D.:
Semiconductor
Research
Corporation,
DARPA,
Office
of
Naval
Research
MURI-114407,
MURI-N00014-21-1-2801,
N00014-20-1-2589,
NSF
CCF-1231216,
NSF
2124136,
Simons
SCGB
542965;
S.G.:
Simons
Foundation,
James
S.
McDonnell
Foundation,
NSF
CAREER
Award;
A.M.:
Federation
of
American
Scientists;
B.O.:
NSF
IIS-1718991;
C.S.:
NIH
1R01MH125571-01,
R01NS127122,
NSF
1922658,
Google
faculty
award;
E.S.:
Simons
Foundation,
NIH
EY022428;
S.S.:
NIH
NINDS
NS053603;
D.S.:
Simons
543049;
A.T.:
IARPA
D16PC00003,
DARPA
HR0011-18-2-0025,
NIH
R01EY026927,
R01MH109556, NSF NeuroNex DBI-1707400; D.T.: HHMI.
Competing interests
The
authors
declare
their
engagements
with
the
following
relevant
for-profit
entities
–
A.Z.:
Cajal
Neuroscience;
S.E.:
Herophilus;
R.B.:
Google
DeepMind;
B.Ö.:
Blackbird
Neuroscience;
K.B.:
Femtosense
Inc.,
Radical
Semiconductor,
Neurovigil;
C.C.:
Google
DeepMind;
S.G.:
Meta;
J.H.:
Numenta;
K.K.:
Paradromics;
T.L.:
Google
DeepMind;
A.M.:
Google
DeepMind,
Kernel;
D.S.:
Meta;
A.T.: Vathes Inc., Upload AI LLC, BioAvatar LLC.
Author contributions
A.Z.
conceived
the
project
and
wrote
the
first
draft.
A.Z.,
S.E.,
B.R.,
B.Ö.
provided
extensive
editing
to
successive drafts. All other authors provided additional guidance and edits at various stages.

References
1.
Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick,
M. Neuroscience-Inspired
Artificial Intelligence.
Neuron
95
, 245–258 (2017).
2.
Macpherson, T.
et al.
Natural and Artificial Intelligence:
A brief introduction to the
interplay between AI and neuroscience research.
Neural
Networks
144
, 603–613
(2021).
3.
McCulloch, W. S. & Pitts, W. A logical calculus
of the ideas immanent in nervous
activity.
Bull. Math. Biophys.
5
, 115–133 (1943).
4.
von Neumann, J.
First draft of a report on the
EDVAC
.
http://dx.doi.org/10.5479/sil.538961.39088011475779
(1945)
doi:
10.5479/sil.538961.39088011475779
.
5.
von Neumann, J.
The computer and the brain
. (Yale
University Press, 2012).
doi:
10.12987/9780300188080
.
6.
Hubel, D. H. & Wiesel, T. N. Receptive fields,
binocular interaction and functional
architecture in the cat’s visual cortex.
The Journal
of Physiology
vol. 160 106–154
Preprint at https://doi.org/
10.1113/jphysiol.1962.sp006837
(1962).
7.
Fukushima, K. Neocognitron: a self organizing neural
network model for a
mechanism of pattern recognition unaffected by shift in position.
Biol. Cybern.
36
,
193–202 (1980).
8.
LeCun, Y. & Bengio, Y. Convolutional networks for
images, speech, and time series.

The handbook of brain theory and neural
3361
, (1995).
9.
Thorndike, E. L. Animal intelligence: An experimental
study of the associative
processes in animals. Preprint at https://doi.org/
10.1037/10780-000
(1898).
10.
Thorndike, E. L. The Law of Effect.
The American
Journal of Psychology
vol. 39
212 Preprint at https://doi.org/
10.2307/1415413
(1927).
11.
Thorndike, E. L. The fundamentals of learning.
Preprint at
https://doi.org/
10.1037/10976-000
(1932).
12.
Crow, T. J. Cortical synapses and reinforcement:
a hypothesis.
Nature
219
,
736–737 (1968).
13.
Rescorla, R. A. A theory of Pavlovian conditioning
: Variations in the effectiveness
of reinforcement and nonreinforcement.
Current research
and theory
64–99 (1972).
14.
Klopf, H. A.
Brain Function and Adaptive Systems:
A Heterostatic Theory
. (1972).
15.
Schultz, W., Dayan, P. & Montague, P. R. A neural
substrate of prediction and
reward.
Science
275
, 1593–1599 (1997).
16.
Campbell, M., Hoane, A. J. & Hsu, F.-H. Deep Blue.
Artif. Intell.
134
, 57–83 (2002).
17.
Silver, D.
et al.
Mastering the game of Go with
deep neural networks and tree
search.
Nature
529
, 484–489 (2016).
18.
Reed, S.
et al.
A Generalist Agent.
arXiv
(2022)
doi:
10.48550/arXiv.2205.06175
.
19.
Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M.
& Tolias, A. S. Engineering a Less
Artificial Intelligence.
Neuron
103
, 967–979 (2019).
20.
Itti, L., Koch, C. & Niebur, E. A model of saliency-based
visual attention for rapid
scene analysis.
IEEE Trans. Pattern Anal. Mach. Intell.
20
, 1254–1259 (1998).

21.
Larochelle, H. & Hinton, G. Learning to combine foveal glimpses with a third-order
Boltzmann machine.
Advances in neural information
processing systems
23
,
(2010).
22.
Xu, K.
et al.
Show, Attend and Tell: Neural Image
Caption Generation with Visual
Attention. in
Proceedings of the 32nd International
Conference on Machine
Learning
(eds. Bach, F. & Blei, D.) vol. 37 2048–2057
(PMLR, 2015).
23.
Vaswani, A.
et al.
Attention is all you need.
Adv. Neural Inf. Process. Syst.
30
,
(2017).
24.
Moravec, H.
Mind Children: The Future of Robot
and Human Intelligence
. (Harvard
University Press, 1988).
25.
Turing, A. M. I.—COMPUTING MACHINERY AND INTELLIGENCE.
Mind
LIX
,
433–460 (1950).
26.
Brown, T.
et al.
Language models are few-shot
learners.
Adv. Neural Inf. Process.
Syst.
33
, 1877–1901 (2020).
27.
Sejnowski, T. Large Language Models and the Reverse
Turing Test.
arXiv [cs.CL]
(2022).
28.
Brooks, R. A. Intelligence without representation.
Artificial Intelligence
vol. 47
139–159 Preprint at https://doi.org/
10.1016/0004-3702(91)90053-m
(1991).
29.
Meyer, J.-A. & Wilson, S. W.
From Animals to Animats:
Proceedings of the First
International Conference on Simulation of Adaptive Behavior
. (Bradford Books,
1991).
30.
Pfeifer, R. & Scheier, C. Understanding Intelligence.
Preprint at

https://doi.org/
10.7551/mitpress/6979.001.0001
(2001).
31.
Pfeifer, R. & Bongard, J.
How the Body Shapes
the Way We Think: A New View of
Intelligence
. (MIT Press, 2006).
32.
Ortiz, C. L. Why We Need a Physically Embodied
Turing Test and What It Might
Look Like.
AI Magazine
vol. 37 55–62 Preprint at
https://doi.org/
10.1609/aimag.v37i1.2645
(2016).
33.
Merel, J., Botvinick, M. & Wayne, G. Hierarchical
motor control in mammals and
machines.
Nat. Commun.
10
, 5489 (2019).
34.
Mnih, V.
et al.
Human-level control through deep
reinforcement learning.
Nature
518
, 529–533 (2015).
35.
Huang, S., Papernot, N., Goodfellow, I., Duan,
Y. & Abbeel, P. Adversarial Attacks
on Neural Network Policies.
arXiv [cs.LG]
(2017).
36.
Zador, A. M. A critique of pure learning and what
artificial neural networks can learn
from animal brains.
Nat. Commun.
10
, 3770 (2019).
37.
Bommasani, R.
et al.
On the Opportunities and
Risks of Foundation Models.
arXiv
[cs.LG]
(2021).
38.
Elman, J. L. Learning and development in neural
networks: the importance of
starting small.
Cognition
48
, 71–99 (1993).
39.
Lake, B. M., Ullman, T. D., Tenenbaum, J. B. &
Gershman, S. J. Building machines
that learn and think like people.
Behav. Brain Sci.
40
, e253 (2017).
40.
Doya, K. & Taniguchi, T. Toward evolutionary and
developmental intelligence.
Current Opinion in Behavioral Sciences
vol. 29 91–96
Preprint at

https://doi.org/
10.1016/j.cobeha.2019.04.006
(2019).
41.
Pehlevan, C. & Chklovskii, D. B. Neuroscience-Inspired
Online Unsupervised
Learning Algorithms: Artificial Neural Networks.
IEEE
Signal Process. Mag.
36
,
88–96 (2019).
42.
Stanley, K. O., Clune, J., Lehman, J. & Miikkulainen,
R. Designing neural networks
through neuroevolution.
Nature Machine Intelligence
1
, 24–35 (2019).
43.
Gupta, A., Savarese, S., Ganguli, S. & Fei-Fei,
L. Embodied intelligence via
learning and evolution.
Nat. Commun.
12
, 5721 (2021).
44.
Stöckl, C., Lang, D. & Maass, W. Structure induces
computational function in
networks with diverse types of spiking neurons.
bioRxiv
2021.05.18.444689 (2022)
doi:
10.1101/2021.05.18.444689
.
45.
Koulakov, A., Shuvaev, S., Lachi, D. & Zador,
A. Encoding innate ability through a
genomic bottleneck.
bioRxiv
2021.03.16.435261 (2022)
doi:
10.1101/2021.03.16.435261
.
46.
Patterson, D.
et al.
Carbon Emissions and Large
Neural Network Training.
arXiv
[cs.LG]
(2021).
47.
Sokoloff, L. The metabolism of the central nervous
system in vivo.
Handbook of
physiology, section I, neurophysiology
3
, 1843–1864
(1960).
48.
Boahen, K. Dendrocentric learning for synthetic
intelligence.
Nature
612
, 43–50
(2022).
49.
Dobrunz, L. E. & Stevens, C. F. Heterogeneity
of release probability, facilitation,
and depletion at central synapses.
Neuron
18
, 995–1008
(1997).

50.
Attwell, D. & Laughlin, S. B. An energy budget for signaling in the grey matter of the
brain.
J. Cereb. Blood Flow Metab.
21
, 1133–1145 (2001).
51.
Lennie, P. The cost of cortical computation.
Curr.
Biol.
13
, 493–497 (2003).
52.
Davies, M.
et al.
Advancing neuromorphic computing
with loihi: A survey of results
and outlook.
Proc. IEEE Inst. Electr. Electron. Eng.
109
, 911–934 (2021).
53.
Cisek, P. & Hayden, B. Y. Neuroscience needs evolution.
Philos. Trans. R. Soc.
Lond. B Biol. Sci.
377
, 20200518 (2022).
54.
Mathis, A.
et al.
DeepLabCut: markerless pose
estimation of user-defined body
parts with deep learning.
Nat. Neurosci.
21
, 1281–1289
(2018).
55.
Wu, A.
et al.
Deep Graph Pose: a semi-supervised
deep graphical model for
improved animal pose tracking.
Adv. Neural Inf. Process.
Syst.
33
, 6040–6052
(2020).
56.
Marshall, J. D.
et al.
Continuous Whole-Body 3D
Kinematic Recordings across the
Rodent Behavioral Repertoire.
Neuron
109
, 420–437.e8
(2021).
57.
Pereira, T. D.
et al.
Publisher Correction: SLEAP:
A deep learning system for
multi-animal pose tracking.
Nat. Methods
19
, 628 (2022).
58.
Merel, J.
et al.
Deep neuroethology of a virtual
rodent. in
International Conference
on Learning Representations
(2020).
59.
Todorov, E., Erez, T. & Tassa, Y. MuJoCo: A physics
engine for model-based
control. in
2012 IEEE/RSJ International Conference
on Intelligent Robots and
Systems
(IEEE, 2012). doi:
10.1109/iros.2012.6386109
.
60.
Brockman, G.
et al.
OpenAI Gym. (2016) doi:
10.48550/arXiv.1606.01540
.

61.
Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I. & Osawa, E. RoboCup: The Robot
World Cup Initiative. in
Proceedings of the first
international conference on
Autonomous agents
340–347 (Association for Computing
Machinery, 1997).
62.
Bell, R. M. & Koren, Y. Lessons from the Netflix
prize challenge.
ACM SIGKDD
Explorations Newsletter
vol. 9 75–79 Preprint at
https://doi.org/
10.1145/1345448.1345465
(2007).
63.
Doya, K., Ema, A., Kitano, H., Sakagami, M. &
Russell, S. Social impact and
governance of AI and neurotechnologies.
Neural Netw.
152
, 542–554 (2022).
64.
Liu, S.
et al.
From Motor Control to Team Play
in Simulated Humanoid Football.
arXiv [cs.AI]
(2021).
65.
MICrONS Consortium
et al.
Functional connectomics
spanning multiple areas of
mouse visual cortex.
bioRxiv
2021.07.28.454025 (2021)
doi:
10.1101/2021.07.28.454025
.
66.
Lilienthal, O.
Birdflight as the Basis of Aviation:
A Contribution Towards a System of
Aviation, Compiled from the Results of Numerous Experiments Made by O. and G.
Lilienthal
. (Longmans, Green, 1911).
67.
Culick, F. What the Wright Brothers did and did
not understand about flight
mechanics - In modern terms. in
37th Joint Propulsion
Conference and Exhibit
(American Institute of Aeronautics and Astronautics, 2001).
68.
Shyy, W., Lian, Y., Tang, J., Viieru, D. & Liu,
H.
Aerodynamics of low Reynolds
number flyers
. (2008).
69.
Akos, Z., Nagy, M., Leven, S. & Vicsek, T. Thermal
soaring flight of birds and

unmanned aerial vehicles.
Bioinspir. Biomim.
5
, 045003 (2010).