Context:
sights
 into
 how
 our
 brain
s
 process
 information
 but
 also
 pa
ves
 the
 way
 to
ward
 creat
ing
 autonom
ous
 devices
 that
 interact

 effective
ly
 with
 
their
 environment
,
 and
 ne
ural
 pros
these
s
 that
 can
 
restore
 brain
 function
.
 We
 address
 
these
 questions

 in
 the
 visual
 system
 of
 
mice
.
 Mic
e
 are
 well
 suite
d
 for
 study
ing
 the
 ne
ural
 circuit
s
 of
 vision
.
 
They
 are
 small
 and
 smart

;
 
their
 visual
 system
 re
semble
s
 in
 many
 
ways
 that
 of
 human
s
;
 and
 the
y
 can
 be
 manipulat
ed
 
genetic
ally
 to
 identif
y
 and

 study
 specific
 groups
 of
 nerve
 
cells
.
 Us
ing
 fast
 and
 
sensitive
 laser
-
scanning
 
microscop
es
,
 micro
pip
ette
 or
 
advanced

 micro
probe
s
 
recording
s
,
 we
 measure
 the
 activity
 of
 large
 groups
 of
 nerve
 
cells
 
while
 the
 animal
 explore
s
 
a
 
controlled

 visual
 environment
.
 By
 relat
ing
 the
 measure
d
 ne
ural
 response
s
 to
 what
 the
 
mice
 see
,
 we
 infer
 the
 visual
 comput
ations

 perform
ed
 by
 the
 network
 and
 build
 
mathematic
al
 models
 of
 them
.
 We
 
further
 test
 
these
 models
 by
 electric
ally
 or
 opt

ically
 stimul
ating
 target
ed
 groups
 of
 neuron
s
 and
 observ
ing
 the
 activity
 of
 neighbor
ing
 or
 distant
 
cells
.
 This
 approach

 lets
 
us
 assess
 how
 different
 components
 of
 the
 circuit
 can
 under
lie
 specific
 visual
 comput
ations
,
 and
 how
 
these
 comput

ations
 are
 tai
lored
 to
 the
 animal
s
 visual
 environment
 and
 behavior
al
 
goals
.
 Ultimate
ly
,
 this
 research
 will
 provide
 in

sights
 into
 how
 we
 see
,
 and
 into
 how
 b
iological
 ne
ural
 networks
 are
 construct
ed
 to
 perform
 
useful
 comput
ations
.
 20

/11/2018
 
-
 In
 
a
 new
 study
,
 
scientist
s
 from
 
NER
F
 (
N
euro
-
Electronic
s
 Research
 F
landers
)
 un
cover
 that
 the
 processing
 of

 visual
 information
 in
 the
 brain
 is
 inde
ed
 modul
ated
 by
 our
 own
 behavior
.
 16
/07/2018
 
-
 A
 research
 
collaboration
 
between

 the
 University
 of
 Le
th
bridge
 and
 
NER
F
,
 V
IB
-
KU
 Le
uven
-
ime
c
,
 
provided
 new
 
insight
 into
 how
 the
 brain
 learn
s
 about
 the

 environment
 and
 
why
 the
 
hippo
campus
,
 
a
 key
 part
 of
 the
 brain
,
 is
 so
 important
 in
 this
 process
.
 16
/08/2017
 
-
 Research
ers

 at
 
NER
F
 (
V
IB
-
ime
c
-
KU
 Le
uven
)
 have
 now
 un
covered
 strik
ing
 ne
ural
 activity
 
patterns
 in
 
a
 brain
 area
 
called
 the
 retro
s
pl

enial
 corte
x
 that
 may
 assist
 with
 
spatial
 memory
 and
 navigation
.

Input:
 excellent
 transport
 links
),
 the
 position
 is
 ideal
ly
 suite
d
 to
 international
 candidat
es
.
 Interest
ed
 students
 should
 send

 
a
 CV
,
 
a
 copy
 of
 
their
 university
 
transcript
s
,
 names
 of
 3
 refere
es
,
 and
 
a
 cover
 letter
 stat
ing
 career
 
goals
,
 research

 interest
s
,
 and
 how
 
these
 
relate
 to
 our
 research
.
 The
 Bon
in
 lab
 studies
 the
 network
 mechanism
s
 that
 under
lie
 sensor
y

 processing
 in
 the
 mamma
lian
 brain
.
 Us
ing
 fast
 and
 
sensitive
 laser
-
scanning
 
microscop
es
 and
 
advanced
 micro
probe
s
 

recording
s
,
 we
 measure
 the
 activity
 of
 large
 groups
 of
 nerve
 
cells
 
while
 the
 animal
 explore
s
 
a
 
controlled
 virtual

 environment
.
 By
 relat
ing
 the
 measure
d
 ne
ural
 response
s
 to
 the
 sensor
y
 
stimuli
 experience
d
 by
 the
 animal
,
 we
 infer
 the

 sensor
y
 comput
ations
 perform
ed
 by
 the
 network
.
 We
 are
 particular
ly
 inter
sted
 in
 in
 how
 specific
 components
 of
 the

 circuit
 implement
 specific
 sensor
y
 comput
ations
,
 and
 how
 
these
 comput
ations
 
relate
 to
 the
 animal
s
 b
ehaviour
.
 Neuro
-

Electronic
s
 Research
 F
landers
 (
NER
F
,
 www
.
ner
f
.
be
)
 is
 
a
 young
 not
-
for
-
profit
 
academic
 research
 
initiative
 with
 the
 

ultimate
 goal
 of
 
forming
 
a
 th
orough
 
understanding
 of
 brain
 function
 at
 multiple
 
levels
 of
 detail
 rang
ing
 from
 single
 

cells
 and
 circuit
s
 to
 b
ehaviour
.
 New
 in
sights
 into
 the
 operation
 of
 brain
 circuit
s
 are
 em
powered
 by
 the
 development
 of

 novel
 
technologies
 that
 
integrate
 neuro
biology
 and
 nano
-
scale
 engineering
.
 We
 aim
 to
 develop
 and
 use
 novel
 electronic
,
 

chemical
 and
 
o
ptical
 tools
 to
 monitor
 and
 manipulat
e
 brain
 circuit
s
.
 In
 the
 long
 term
 the
 
basic
 research
 at
 
NER
F
 is
 

expected
 to
 inspire
 
scientist
s
 to
 simulat
e
 brain
 networks
,
 as
 well
 as
 la
y
 
a
 scientific
 
framework
 for
 the
 development
 of

 novel
 medical
 applications
,
 in
 particular
 the
 the
 diagnos
is
 and
 treatment
 of
 neurologi
cal
 
disorders
.
 Found
ed
 by
 Ime
c
,
 V

IB
,
 and
 KU
 Le
uven
,
 
NER
F
 is
 house
d
 on
 the
 ime
c
 campus
 in
 Le
uven
,
 Belgium
,
 
where
 research
ers
 work
 in
 cross
-
disciplinar
y
 

teams
,
 benefit
ting
 from
 ime
c
s
 state
-
of
-
the
-
art
 clean
 room

Targets:
  Idx: 259, Δloglikelihood: -0.209
infrastructure Idx: 70048, Δloglikelihood: 0.220
 and Idx: 305, Δloglikelihood: 0.125
 set Idx: 2718, Δloglikelihood: 0.201
 of Idx: 304, Δloglikelihood: 0.772
 neuro Idx: 33630, Δloglikelihood: -2.137
science Idx: 43745, Δloglikelihood: -0.273
  Idx: 259, Δloglikelihood: -0.378
labs Idx: 15345, Δloglikelihood: -1.520
. Idx: 260, Δloglikelihood: -0.066
  Idx: 259, Δloglikelihood: 0.046
NER Idx: 50247, Δloglikelihood: 0.302
F Idx: 545, Δloglikelihood: 0.000
 is Idx: 339, Δloglikelihood: -0.030
 made Idx: 3785, Δloglikelihood: -0.078
 up Idx: 1150, Δloglikelihood: 0.129
 of Idx: 304, Δloglikelihood: -0.015
 6 Idx: 570, Δloglikelihood: -1.473
  Idx: 259, Δloglikelihood: 0.164
teams Idx: 64701, Δloglikelihood: -0.933
 do Idx: 342, Δloglikelihood: 0.589
ing Idx: 347, Δloglikelihood: 0.002
 world Idx: 4836, Δloglikelihood: 0.758
- Idx: 264, Δloglikelihood: 0.012
class Idx: 6233, Δloglikelihood: 0.373
  Idx: 259, Δloglikelihood: -0.115
basic Idx: 25295, Δloglikelihood: -0.725
 research Idx: 8348, Δloglikelihood: 0.200
 in Idx: 281, Δloglikelihood: -0.662
 systems Idx: 19149, Δloglikelihood: -0.146
 and Idx: 305, Δloglikelihood: -0.656

 circuit Idx: 20440, Δloglikelihood: 0.829
s Idx: 263, Δloglikelihood: -0.018
 neuro Idx: 33630, Δloglikelihood: -0.013
science Idx: 43745, Δloglikelihood: 0.027
 and Idx: 305, Δloglikelihood: 0.066
 has Idx: 1070, Δloglikelihood: -1.058
 recruit Idx: 109605, Δloglikelihood: -1.105
ed Idx: 345, Δloglikelihood: 0.001
 2 Idx: 356, Δloglikelihood: 0.318
  Idx: 259, Δloglikelihood: 0.009
additional Idx: 14896, Δloglikelihood: -0.254
 groups Idx: 36058, Δloglikelihood: -0.144
. Idx: 260, Δloglikelihood: -1.593
 C Idx: 371, Δloglikelihood: -0.019
ontinuous Idx: 93317, Δloglikelihood: -1.136
  Idx: 259, Δloglikelihood: -0.089
funding Idx: 80088, Δloglikelihood: -1.097
 is Idx: 339, Δloglikelihood: 0.209
  Idx: 259, Δloglikelihood: 0.675
provided Idx: 15644, Δloglikelihood: -0.164
 by Idx: 455, Δloglikelihood: 0.010
 the Idx: 287, Δloglikelihood: 0.336
 3 Idx: 381, Δloglikelihood: -0.457
 found Idx: 5897, Δloglikelihood: 0.523
ers Idx: 1207, Δloglikelihood: -1.800
 and Idx: 305, Δloglikelihood: -0.728
 the Idx: 287, Δloglikelihood: -0.041

 Government Idx: 21090, Δloglikelihood: 1.356
 of Idx: 304, Δloglikelihood: -0.047
 F Idx: 515, Δloglikelihood: 1.948
landers Idx: 159925, Δloglikelihood: -0.000
. Idx: 260, Δloglikelihood: -0.304
  Idx: 259, Δloglikelihood: -0.107