Calling song recognition in female crickets: temporal tuning of identified brain neurons matches behavior


Kostarakos, K.; Hedwig, B.

Journal of Neuroscience 32(28): 9601-9612

2012


Phonotactic orientation of female crickets is tuned to the temporal pattern of the male calling song. We analyzed the phonotactic selectivity of female crickets to varying temporal features of calling song patterns and compared it with the auditory response properties of the ascending interneuron AN1 (herein referred to as TH1-AC1) and four newly identified local brain neurons. The neurites of all brain neurons formed a ring-like branching pattern in the anterior protocerebrum that overlapped with the axonal arborizations of TH1-AC1. All brain neurons responded phasically to the sound pulses of a species-specific chirp. The spike activity of TH1-AC1 and the local interneuron, B-LI2, copied different auditory patterns regardless of their temporal structure. Two other neurons, B-LI3 and B-LC3, matched the temporal selectivity of the phonotactic responses but also responded to some nonattractive patterns. Neuron B-LC3 linked the bilateral auditory areas in the protocerebrum. One local brain neuron, B-LI4, received inhibitory as well as excitatory synaptic inputs. Inhibition was particularly pronounced for nonattractive pulse patterns, reducing its spike activity. When tested with different temporal patterns, B-LI4 exhibited bandpass response properties; its different auditory response functions significantly matched the tuning of phonotaxis. Temporal selectivity was established already for the second of two sound pulses separated by one species-specific pulse interval. Temporal pattern recognition in the cricket brain occurs within the anterior protocerebrum at the first stage of auditory processing. It is crucially linked to a change in auditory responsiveness during pulse intervals and based on fast interactions of inhibition and excitation.

The
Journal
of
Neuroscience,
July
11,
2012
32(28):9601-9612
9601
Behavioral/Systems/Cognitive
Calling
Song
Recognition
in
Female
Crickets:
Temporal
Tuning
of
Identified
Brain
Neurons
Matches
Behavior
Konstantinos
Kostarakos
and
Berthold
Hedwig
Department
of
Zoology,
University
of
Cambridge,
Cambridge
CB2
3EJ,
United
Kingdom
Phonotactic
orientation
of
female
crickets
is
tuned
to
the
temporal
pattern
of
the
male
calling
song.
We
analyzed
the
phonotactic
selectivity
of
female
crickets
to
varying
temporal
features
of
calling
song
patterns
and
compared
it
with
the
auditory
response
properties
of
the
ascending
interneuron
AN1
(herein
referred
to
as
TH1-AC1)
and
four
newly
identified
local
brain
neurons.
The
neurites
of
all
brain
neurons
formed
a
ring-like
branching
pattern
in
the
anterior
protocerebrum
that
overlapped
with
the
axonal
arborizations
of
TH1-AC1.
All
brain
neurons
responded
phasically
to
the
sound
pulses
of
a
species-specific
chirp.
The
spike
activity
of
TH1-AC1
and
the
local
intemeuron,
B-LI2,
copied different
auditory
patterns
regardless
of
their
temporal
structure.
Two
other
neurons,
B-LI3
and
B-LC3,
matched
the
temporal
selectivity
of
the
phonotactic
responses
but
also
responded
to
some
nonattractive
patterns.
Neuron
B-LC3
linked
the
bilateral
auditory
areas
in
the
protocerebrum.
One
local
brain
neuron,
B-LI4,
received
inhibitory
as
well
as
excitatory
synaptic
inputs.
Inhibition
was
particularly
pronounced
for
nonattractive
pulse
patterns,
reducing
its
spike
activity.
When
tested
with
different
temporal
patterns,
B-LI4
exhibited
bandpass
response
properties;
its
different
auditory
response
functions
significantly
matched
the
tuning
of
phonotaxis.
Temporal
selectivity
was
established
already
for
the
second
of
two
sound
pulses
separated
by
one
species-specific
pulse
interval.
Temporal
pattern
recognition
in
the
cricket
brain
occurs
within
the
anterior
protocerebrum
at
the
first
stage
of
auditory
processing.
It
is
crucially
linked
to
a
change
in
auditory
responsiveness
during
pulse
intervals
and
based
on
fast
interactions
of
inhibition
and
excitation.
Introduction
From
cetaceans
to
insects,
animals
use
acoustic
signals
with
species-specific
patterns
for
intraspecific
acoustic
communica-
tion.
Processing
and
recognition
of
these
signals
is
crucial
to
their
lifestyle
as
it
is
linked
to
mating
success
and
rivalry
behavior
(Bradbury
and
Vehrenkamp,
1998).
Elaborate
acoustic
patterns
with
variations
in
sound
frequency,
amplitude,
and
duration
are
used
by
mammals,
birds,
and
some
amphibians
(Fay,
1992;
Yu
and
Margoliash,
1996).
Communication
signals
are
shaped
by
sexual
selection
(Endler
and
Basolo,
1998;
Ryan,
1998).
As
a
re-
sult,
in
some
groups
of
lower
vertebrates
(e.g.,
fish
and
frogs),
as
well
as
in
different
species
of
insects,
these
signals
evolved
based
on
variation
in
their
temporal
structure
(e.g.,
pulse
duration,
pulse
interval,
or
the
combination
of
pulses
within
a
call
pattern)
(Bradbury
and
Vehrenkamp,
1998;
Gerhardt
and
Huber,
2002).
Insects
tend
to
use
rather
simple
sequences
of
sound
pulses,
in
which
the
feature
for
species
recognition
is
the
temporal
pattern
of
these
pulses
(Pollack
and
Hoy,
1979;
Pollack,
2000;
Gerhardt
Received
Mardi
9,
2012;
revised
May
11,
2012;
accepted
May
17,
2012.
Author
contributions:
B.H.
designed
research;
K.IC
performed
research;
ICK.
analyzed
data;
K.K.
and
B.H.
wrote
the
paper.
This
work
was
supported
by
a
Newton
International
Fellowship
from
the
Royal
Society;
additional
support
was
given
by
the
Newton
Trust
Cambridge
and
the
Department
of
Zoology.
Behavioural
studies
were
supported
by
the
Biotechnology
and
Biological
Sciences
Research
Council;
we
thank
L
Goldrick
for
excellent
technical
assistance
and
H.
ter
Hofstede
and
T.
Bayley
for
critically
reading
the
manuscript.
Correspondence
should
be
addressed
to
Dr.
Berthold
Hedwig,
Department
of
Zoology,
University
of
Cambridge,
Downing
Street,
Cambridge
CB2
3EJ,
UK.
E-mail:
bh202@cam.ac.uk.
D01:10.1523/1NEUROSCI.1170-12.2012
Copyright
2012
the
authors
0270-6474/12/329601-12$15.00/0
and
Huber,
2002).
Consequently,
on
the
receiver
side,
intraspe-
cific
communication
requires
neural
networks
tuned
to
the
species-specific
temporal
pattern
of
the
sound
signals.
Considerable
progress
has
been
made
in
analyzing
the
neural
mechanisms
underlying
temporal
processing
in
frogs
and
fish.
Pulse
rate
selectivity
of
neurons
in
the
anuran
inferior
colliculus
results
from
precisely
timed
interactions
between
inhibition
and
pulse
rate-dependent
excitation
(Edwards
et
al.,
2007;
Rose
et
al.,
2011).
In
the
fish
Pollimyrus,
pattern-selective
neurons
within
the
torus
semicircularis
exhibit
long-lasting
inhibition
and
postin-
hibitory
rebound
properties
(Crawford,
1997;
Large
and
Craw-
ford,
2002).
Further
to
these
advances
in
lower
vertebrates,
the
neural
net-
works
of
insects
provide
the
advantage
to
analyze
and
reveal
prin-
ciples
of
auditory
processing
at
the
level
of
identified
neurons
(Wohlers
and
Huber,
1982;
Schildberger,
1984;
Brodfuehrer
and
Hoy,
1990;
Zorovia
and
Hedwig,
2011).
Phonotactic
behavior
of
female
crickets
is
tuned
to
the
temporal
structure
of
the
male
calling
song.
Several
concepts
have
been
proposed
to
explain
the
neural
basis
of
this
selectivity.
Hennig
(2003)
suggested
a
cross-
correlation
analysis
between
the
perceived
signal
and
an
innate
template
based
on
pulse
coincidence.
Hoy
(1978)
speculated
that
such
an
internal
template
could
be
derived
from
the
same
neural
network
that
drives
the
central
pattern
generator
for
singing
in
males.
Data
from
Bush
and
Schul
(2006)
indicate
that
temporal
pattern
recognition
may
be
achieved
by
oscillatory
responses
of
individual
neurons.
Based
on
intracellular
recordings
of
auditory
brain
neurons,
Schildberger
(1984)
suggested
that
temporal
fil-
tering
is
established
by
sequential
processing
in
low-pass
and
9602
J.
Neurosci.,
July
11,
2012
32(28):9601-9612
Kostarakos
and
Hedwig
Pattern
Recognition
in
the
(ticket
Brain
high-pass
filter
neurons
that
finally
shape
the
bandpass
response
properties
of
brain
neurons
that
match
the
temporal
tuning
of
female
phonotaxis.
Although
these
concepts
provide
hypotheses
for
pattern
rec-
ognition,
the
actual
neural
mechanisms
for
temporal
selectivity
in
the
cricket
brain
are
still
not
dearly
resolved.
Here,
we
characterize
the
temporal
filtering
of
auditory
brain
neurons
and
compare
it
with
the
phonotactic
responses
of
females.
We
demonstrate
that
the
be-
havioral
selectivity
to
temporal
features
is
mirrored
in
the
response
properties
of
these
brain
neurons,
and
we
provide
evidence
for
un-
derlying
processing
mechanisms.
Materials
and
Methods
Last
instar
female
crickets
(Gryllus
bimaculatus
de
Geer)
were
collected
from
the
colony
at
the
Department
of
Zoology
of
University
of
Cam-
bridge
(Cambridge,
UK)
and
maintained
on
a
12
h
light/dark
cycle.
They
were
kept
isolated
from
singing
males
and
were
used
for
experiments
1
week
after
their
final
moult.
All
experiments
were
performed
at
21-23°C.
Behavior
Females
were
tethered
in
natural
walking
posture
on
top
of
a
trackball.
An
L-shaped
insect
pin
(0.2
mm
diameter)
was
waxed
to
the
metatho-
racic
tergite
and
its
end
damped
into
a
needle
holder.
While
the
females
moved
during
phonotactic
walking,
an
optical
sensor
(ADNS-2051;
2D
optical
mouse
sensor;
Agilent)
monitored
the
rotations
of
the
trackball
in
the
forward-backward
and
left-right
directions.
Due
to
the
tethered
body
position,
the
sound
conditions
at
the
spiracles
remained
constant
during
walking
(for
details,
see
Hedwig
and
Poulet,
2004,
2005).
In
be-
havioral
experiments,
each
acoustic
paradigm
was
tested
three
times
in
at
least
10
females;
different
animals
were
used
for
the
neurophysiological
experiments.
Acoustic
stimulation
The
calling
song
of
male
G.
bimaculatus
consists
of
sound
pulses
with
a
duration
of
15-23
ms
and
pulse
intervals
(time
from
the
end
of
one
pulse
to
the
start
of
the
next)
of
16-24
ms.
Three
to
four
pulses
are
grouped
into
chirps,
which
are
repeated
at
intervals
of
190-250
ms
with
an
overall
chirp
rate
of
2-3
Hz
(Doherty,
1985b;
Ferreira
and
Ferguson,
2002;
Ver-
burgt
et
al.,
2010).
We
designed
three
acoustic
paradigms
to
analyze
the
temporal
selec-
tivity
of
phonotactic
behavior
and
of
auditory
neurons.
Pulse
duration.
Using
a
constant
pulse
period
of
40
ms,
four
sound
pulses
were
grouped
into
chirps.
Pulse
duration
(PD)
values
of
4,
6,
8,10,
15,
20, 25,
30, 35,
and
40
ms
were
used.
With
increasing
PD,
the
pulse
interval
decreased
correspondingly,
so
a
PD
of
10
ms
was
followed
by
a
pulse
interval
of
30
ms
and
a
PD
of
40
ms
resulted
in
a
constant
tone
of
160
ms.
This
paradigm
also
comes
with
a
variation
of
the
duty
cycle
as
the
pulse
period
was
kept
constant
at
40
ms
for
all
chirps.
It
corresponds
to
the
duty
cycle
paradigm
used
by
Hennig
(2009)
and
Verburgt
et
al.
(2008).
Pulse
interval.
Four
sound
pulses
with
a
constant
PD
of
20
ms
were
grouped
into
a
chirp.
The
pulse
interval
(PI)
was
varied
from
0,
5,
10, 15,
20, 25,
30, 35,
40,
50,
60,
80,
to
100
ms.
As
the
sound
pulses
had
a
fixed
duration
of
20
ms,
a
PI
of
0
ms
resulted
in
a
constant
tone
of
80
ms
and
chirps
with
a
PI
of
100
ms
had
a
duration
of
380
ms.
Pulse
period.
Corresponding
to
the
paradigm
of
Thorson
et
al.
(1982),
the
pulse
period
(PP)
depends
on
the
variation
of
two
parameters;
PD
and
PI
were
both
increased
from
5
to
49
ms
in
4
ms
increments.
The
resulting
PP
of
10,
18,
26,
34,
42,
50, 58,
66,
74,
82,
90,
and
98
ms
all
had
equal
PD
and
PI
length
(duty
cycle
of
50%).
With
increasing
PP,
the
number
of
sound
pulses
was
adjusted
to
keep
the
chirp
duration
close
to
240
ms
(Thorson
et
al.,
1982).
Accordingly,
at
a
PP
of
10
ms,
chirps
consisted
of
25
sound
pulses
and
consisted
of
14,
10,
8,
6,
5, 5,
4,
4,
3, 3,
and
3
sound
pulses
at
the
other
PP,
respectively.
Chirp
durations
ranged
from
230
to
260
ms,
and
therefore
the
paradigms
did
not
all
emit
the
same
sound
energy.
Additionally,
in
neurophysiological
experiments,
we
used
two
test
par-
adigms
with
a
consecutive
variation
of
the
pulse
intervals.
In
the
first
paradigm,
six
sound
pulses
with
a
PD
of
21
ms
were
presented
with
a
PI
of
21
ms
followed
by
a
PI
of
31,
41,
71,
and
again
21
ms.
The
sequence
had
a
duration
of
305
ms
and
was
repeated
after
an
interval
of
230
ms.
In
the
second
paradigm,
two
sound
pulses
with
a
PD
of
20
ms
were
presented
sequentially
while
their
PI
was
systematically
varied
from
0,
5,
10, 15,
20,
25,
30, 35,
40,
50,
60,
80,
to
100
ms.
Pairs
of
pulses
were
separated
by
intervals
of
230
ms.
All
acoustic
stimuli
had
a
carrier
frequency
of
4.8
kHz
and
a
sound
intensity
of
75
dB
SPL
relative
to
20
µPa.
The
rising
and
falling
ramps
for
each
sound
pulse
were
2
ms.
For
behavioral
and
neurophysiological
ex-
periments,
acoustic
paradigms
were
presented
in
a
different
way.
In
be-
havior,
chirps
of
one
paradigm
were
presented
for
30
s
from
the
left-hand
and
then
30
s
from
the
right-hand
side
to
test
the
phonotactic
response.
The
chirp
period
was
constant
for
all
chirps
of
a
paradigm.
In
neurophys-
iological
experiments,
the
different
chirps
of
all
three
paradigms
were
combined
to
a
sequence
and
presented
once
per
loop;
chirps
were
sepa-
rated
by
a
constant
chirp
interval
of
230
ms.
The
sequenced
paradigm
was
necessary
for
a
comprehensive
neural
analysis
and
was
appropriate
to
reveal
neural
response
functions.
We
analyzed
the
number
of
action
potentials
(APs)
generated
per
chirp,
which
provided
a
robust
measure
for
the
degree
of
neural
activation
and
tuning.
Sound
stimuli
were
generated
with
Cool
Edit
Pro
2000
software
(now
Adobe
Audition
software;
Adobe
Systems).
Signals
from
the
PC
audio
boards
were
amplified
by
a
custom-made
amplifier
and
presented
by
speakers
(Sinus
Live
NEO
13
S;
Conrad
Electronic),
which
were
placed
at
an
angular
position
of
45°
to
the
left
and
right
side
of
the
front
of
the
females
at
a
distance
of
30
cm
from
the
front
legs.
Sound
pressure
levels
were
calibrated
at
the
position
of
the
ears
to
an
accuracy
of
1
dB
using
a
Bruel
and
Kjaer
measuring
amplifier
(type
2610)
and
a
1/2
inch
free
field
microphone
(type
4191).
Intracellular
recordings
Crickets
were
placed
ventral
surface
down
on
a
Plasticine
block
with
their
legs
restrained
by
metal
clamps.
The
head
was
slightly
tilted
backwards
and
fixed
tightly
with
beeswax
into
a
modified
Eppendorf
tube
attached
to
a
metal
rod.
The
antennae
were
removed,
and
the
frontal
part
of
the
head
capsule
was
opened
to
expose
the
ventral
side
of
the
brain
(for
details,
see
Zorovie
and
Hedwig,
2011).
The
brain
was
covered
with
insect
saline
[ionic
composition
(in
mmol/L):
140
NaCl,
10
KCl,
4
CaCl
2
,
4
NaHCO
3
,
6
NaH
2
PO
4
].
After
exposing
the
brain,
the
animals
were
teth-
ered
on
a
trackball
in
natural
posture.
The
brain
was
stabilized
between
a
small
metal
platform
at
its
dorsal
side
and
a
metal
ring
at
its
ventral
side.
The
platform
served
as
a
reference
electrode
for
intracellular
recordings.
In
some
crickets,
we
used
protease
(Sigma-Aldrich)
to
soften
the
brain
perineurium.
A
DMZ-Universal
micropipette
puller
(Zeitz
Instruments)
was
used
to
produce
microelectrodes
(Harvard
Apparatus;
1
mm
OD,
0.58
mm
ID).
The
microelectrodes
were
filled
with
5%
Lucifer
yellow
CH
(Sigma-
Aldrich)
dissolved
in
aqua
destillata,
or
with
0.8%
Alexa
568
hydrazid
(Invitrogen)
dissolved
in
0.2
an
lithium
chloride
(LiC1).
The
shaft
of
the
capillaries
was
backfilled
with
0.5
an
LiCl
providing
electrodes
of
100
-140
or
70-100
Mfl,
respectively.
For
intracellular
recordings,
microelectrodes
were
positioned
with
a
Leitz
micromanipulator
(model
M;
Leica
Microsystems).
Electrode
depth
was
controlled
with
a
Mitutoyo
absolute
digimatic
indicator
(ID-C125MB;
Mitutoyo
Corporation).
Auditory
brain
neurons
were
en-
countered
at
a
depth
of
20
-300
µm
in
the
lateral
and
medial
anterior
protocerebrum
where
the
axonal
projections
of
the
ascending
neuron
TH1-AC1
terminate.
Test
pulses
of
50
ms
duration
and
carrier
frequen-
cies
of
4.8,
10,
and
20
kHz
were
used
to
evoke
field
potentials
and
activate
auditory
neurons.
Intracellular
recordings
lasted
between
30
s
and
20
min
and
were
amplified
by
a
SEC-05LX
amplifier
(npi
electronic)
oper-
ating
in
bridge
mode.
Fluorescent
dyes
were
iontophoretically
injected
into
the
neurons
for
5-20
min
by
hyperpolarizing
current
injection
(0.5-5
nA).
For
histolog-
ical
processing,
the
brain
was
dissected
and
fixed
in
4%
paraformalde-
hyde,
dehydrated
in
a
series
of
ethanol
at
70,
90, 95,
and
100%,
and
finally
cleared
in
methyl-salicylate.
A
Zeiss
Axiophot
epifluorescence
micro-
scope
(Carl
Zeiss)
was
used
to
visualize
the
morphology
of
the
neurons
Kostarakos
and
Hedwig
Pattern
Recognition
in
the
Cricket
Brain
J.
Neurosci.,
July
11,
2012
32(28):9601-9612
9603
A
optical
nerve
TH
1-AC1
circumesophageal
connective
C
D
E
using
Zeiss
filter
sets
63
HE
for
Alexa
and
73
HE
for
Lucifer
yellow-
labeled
neurons.
Images
were
taken
with
a
digital
SLR
camera
(Canon
EOS
350D;
Canon)
attached
to
the
microscope.
Neurons
were
recon-
structed
manually
from
image
stacks
using
ImageG
(National
Institutes
of
Health,
Bethesda,
MD)
and
Photoshop
CS4
(Adobe
Systems)
soft-
ware.
Neurons
were
identified
according
to
their
morphology
and
re-
sponse
patterns.
Data
recordings
and
analysis
All
recording
channels
were
digitized
at
17.8
kHz
and
16
bit
amplitude
resolution
with
0.153
mV
per
increment
using
a
CED
1401
plus
data
acquisition
interface.
Data
were
recorded
to
the
hard
disc
of
a
PC
using
Spike
2
software
(Cambridge
Electronic
Design).
Neural
recordings
were
also
displayed
on
an
oscilloscope
(5103N;
Tektronix)
and
monitored
using
headphones.
Behavioral
data
and
neural
recordings
were
analyzed
off-line
using
customized
Neurolab
software
(Hedwig
and
Knepper,
1992;
Knepper
and
Hedwig,
1997).
Phonotactic
steering
was
calculated
from
the
lateral
deviation
toward
the
left
or
right
sound
source
(for
details,
see
Hedwig
and
Poulet,
2004,
2005).
To
compare
the
behavioral
and
neurophysiological
responses,
we
calculated
for
identical
acoustic
paradigms
the
relative
values
of
the
phonotactic
response
and
also
of
the
spike
activity.
For
each
paradigm,
the
maximal
response
was
set
to
100%,
and
all
other
responses
were
expressed
relative
to
this.
The
relative
re-
sponses
were
then
averaged
over
all
tested
animals.
Neural
responses
were
also
analyzed
using
peristimulus
time
(PST)
histograms
and
aver-
ages
of
instantaneous
spike
frequency.
To
further
relate
the
tuning
curves
of
neural
responses
to
the
tuning
of
the
phonotactic
behavior,
we
calcu-
lated
the
correlation
coefficient
(r)
between
both
sets
of
data.
For
plotting
pulse
duration/pulse
interval
response
profiles
(see
Fig.
6),
we
used
Sig-
maPlot
(Systat
Software).
Terminology
Brain
neurons
were
labeled
with
a
sequence
of
letters
and
numbers
(Hed-
wig,
1986;
Zorovit
and
Hedwig,
2011).
The
first
letter
indicates
the
gan-
glion
that
contains
the
cell
body
(B
for
brain),
the
second
letter
describes
axonal
projections
to
other
ganglia
(e.g.,
L
for
local,
D
for
descending),
and
the
third
letter
indicates
if
the
axon
runs
ipsilateral
or
contralateral
to
the
cell
body.
The
ascending
interneuron,
AN1
(Wohlers
and
Huber,
1982;
Schildberger
and
Hoerner,
1988),
is
described
here
as
TH1-AC1.
In
the
following
text,
we
will
briefly
refer
to,
for
example,
a
pulse
interval
of
20
ms
or
a
pulse
period
of
34
ms
as
PI-20
ms
and
PP-34
ms,
respectively.
Results
Structure
of
identified
auditory
brain
neurons
In
addition
to
the
ascending
interneuron
TH1-AC1,
we
identified
four
local
audi-
tory
brain
neurons
in
the
protocerebrum
(Fig.
1).
The
cell
body
of
TH1-AC1
was
located
in
the
prothoracic
ganglion
from
where
the
axon
ascended
to
the
brain
and
projected
to
the
ventral
protocerebrum
lateral
to
the
a-lobe
(Wohlers
and
Huber,
1982).
Here,
the
axon
formed
a
ring-like
projection
around
a
so-far-unidentified
neuropil
structure;
one
prominent
axonal
branch
projected
medially
in
an
anterior
loop,
while
the
other
followed
a
posterior
path.
The
branches
met
slightly
lateral
to
the
ocellar
nerve
forming
dense
arboriza-
tions
(Fig.
1A).
This
ring-like
branching
pattern
was
a
common
feature
of
the
local
auditory
brain
neurons
identified
here,
overlapping
with
the
axonal
projection
pattern
of
TH1-AC1.
All
four
local
interneurons
had
a
cell
body
in
the
lateral
protocerebrum,
next
to
the
optical
nerve
with
a
curved
primary
neurite
projecting
medi-
ally
toward
the
auditory
neuropil.
The
primary
neurite
then
split
into
two
main
branches
looping
around
a
central
structure
as
described
above.
Medially,
where
both
neurites
met,
the
neurons
formed
characteristic
but
similar
dense
arborizations,
which
may
be
regarded
as
an
auditory
neuropil
in
the
anterior
protocer-
ebrum.
The
arborizations
of
interneuron
B-LI2
were
rather
con-
fined
and
exhibited
no
clear
separation
of
axonal
or
dendritic
neurites
(Fig.
1C).
In
contrast,
neurons
B-LI3
and
B-LI4
had
a
slightly
wider
arborization
pattern
and
additionally
had
projec-
tions
to
the
midline
of
the
brain
with
a
"beaded"
axonal
appear-
ance
(Fig.
1
D,E).
Due
to
their
high
structural
similarity,
these
neurons
could
not
be
distinguished
unequivocally
on
structural
criteria
alone.
Dendrites
of
B-LC3
also
overlaid
the
ring-like
ar-
borization
pattern.
Different
from
previous
local
interneurons,
B-LC3
had
an
axon
projecting
to
the
contralateral
auditory
neu-
ropil,
where
it
again
followed
the
ring-like
projection
pattern
(Fig.
1B).
Temporal
selectivity
of
phonotaxis
and
of
the
ascending
interneuron
TH1-AC1
Phonotactic
behavior
showed
a
clear
temporal
selectivity
at
dif-
ferent
PD,
PI,
and
PP
as
it
revealed
a
bandpass
tuning
with
max-
imal
responses
to
specific
temporal
patterns.
The
maximal
responses
occurred
at
PD-15
ms,
PI-15
ms,
and
PP-34
and
42
ms
(Fig.
2).
We
collected
neurophysiological
data
from
different
fe-
males
than
the
ones
used
in
behavioral
tests.
We
first
compared
the
behavioral
phonotactic
responses
with
the
spike
activity
of
the
ascending
interneuron
TH1-AC1,
which
is
the
only
ascending
interneuron
forwarding
calling-song-
specific
information
from
the
prothoracic
auditory
neuropil
to-
ward
the
brain
(Wohlers
and
Huber,
1982;
Schildberger,
1984).
The
activity
of
this
neuron
has
been
described
but
never
compre-
hensively
compared
against
phonotactic
tuning
to
different
tem-
poral
paradigms.
Spike
activity
of
interneuron
TH1-AC1
was
recorded
from
its
main
axon
in
the
protocerebrum
while
the
different
sound
paradigms
were
presented.
TH1-AC1
reliably
re-
sponded
to
the
sound
pulses
of
all
test
patterns.
B-LC3
A
-
400
pm
B
-
250
pm
C,
D,
E
-
200
pm
B-L12
y
B-L14
B-L
13
Figure
1.
Structure
and
location
of
auditory
neurons
in
the
cricket
brain.
A,
Axonal
projections
of
ascending
neuron
TH1-AC1
in
the
anterior
protocerebrum.
B,
Morphology
of
B-LC3
with
a
cell
body
next
to
the
optic
nerve;
dendritic
arborizations
overlap
the
ring-like
arborization
patterns
of
the
ipsilateral
TH
1-AC1
and
axonal
projections
overlap
the
contralateral
one.
C-E,
Morphology
of
B-LI2,
B-LI3,
and
B-LI4
with
a
lateral
cell
body.
15
ms
20
ms
20
90
ms
f
20
40
ms
TH1-AC1
Behaviour
TH1-AC1
Behaviou
30
20
10
N=15
N=10
N=9
N=9
Pulse
Duration
[ms]
Pulse
Interval
[ms]
Pulse
Period
[ms]
A
TH1-AC1
Sound
Pulse
Duration
8
ms
k
B
Pulse
Interval
C
Pulse
Period
18
ms
5
ms
34
ms
-
50
0
50
100
50
200
0
Time
[ms]
TH1-AC1
Behaviour
N=9
N=10
0
100
200
300
Time
[ms]
40
:Cr-
30
6
9
:
2
0
10
°-
co
co
0
5
10
15
20
25
30
35
40
0
10
20
30
40
50
60
70
80
90
100
10
18
26
34
42
50
58
66
74 82
90
98
60
ms
100
200
300
Time
[ms]
JIML
-
50
0
5
00
150
200
120
cj
80
CD.
C
e)
60
CD
40
(4
20
ce
qN
40
30
20
Figure
2.
Activity
of
TH1-AC1
and
phonotactic
tuning
of
female
crickets
to
different
PD,
PI,
and
PP.A,
Top,
Response
of
TH1-AC1
at
PD-8,
20,
and
40
ms.
Bottom,
Phonotactic
(gray)
and
neural
(black)
PD-response
functions.
B,
Top,
TH1-AC1
responses
at
PI-5,
15,
and
60
ms.
Bottom,
Phonotactic
(gray)
and
neural
(black)
PI-response
functions.
(
Top,
Response
of
TH1-AC1
at
PP-18,
34,
and
90
ms.
Bottom,
Phonotactic
(gray)
and
neural
(black)
PP-response
functions.
The
left
scale
indicates
relative
neural
and
phonot-
actic
activity,
and
the
right
scale
shows
the
absolute
values
of
spike
activity.
Neural
data
are
based
on
N
=
9
females
and
n
=
54
test
sequences;
phonotactic
data
are
based
on
N
=
10
(A,
B)
and
N
=
15
(C)
animals.
Error
bars
show
SD
of
relative
values.
9604
J.
Neurosci.,
July
11,
2012
32(28):9601-9612
Kostarakos
and
Hedwig
Pattern
Recognition
in
the
(ticket
Brain
Response
to
PD
With
increasing
PD
and
simultaneously
decreasing
PI,
the
neuron
generated
an
in-
creasing
number
of
spikes
(Fig.
2A,
top).
It
responded
to
each
sound
pulse
at
PD-8
ms
with
a
burst
of
spikes,
and
a
total
of
13.7
AP/chirp
(SD
±1.4).
At
PD-20
ms,
the
response
was
25.7
±
3.2
AP/chirp,
and
at
PD-40
ms,
the
response
to
the
now-
continuous
pulse
reached
37.2
±
5.1
AP/
chirp
on
average.
At
PD-40
ms,
the
spike
frequency
decreased
continuously
from
323
to
193
Hz
from
the
beginning
to
the
end
of
the
response.
Correspondingly,
the
PD-response
function
of
the
neuron
in-
creased
over
the
range
of
PD
tested
by
74.6%
(28.0
AP),
increasing
by
38.8%
(14.5
AP)
from
PD-10
to
25
ms,
but
only
by
18.9%
(7.1
AP)
from
PD-25
to
40
ms
(Fig.
2A,
bottom).
In
contrast,
the
PD-response
function
of
phonotaxis
had
the
shape
of
a
broadly
tuned
optimum
curve
exhibiting
a
maxi-
mal
response
at
PD-15
ms
(Fig.
2A,
bot-
tom).
The
phonotactic
response
increased
by
70.4%
from
PD-4
to
15
ms
and
de-
creased
by
17.4%
from
PD-15
to
25
ms
and
further
by
50.6%
to
40
ms.
It
reached
values
>50%
in
the
PD
range
of
8-35
ms.
Comparing
the
PD-
response
function
of
TH1-AC1
with
the
behavioral
tuning
re-
vealed
that
the
responses
did
not
match.
This
was
reflected
by
a
correlation
coefficient
of
r
2
=
0.00
(p
>
0.05).
Response
to
PI
The
response
of
TH1-AC1
only
slightly
increased
when
PI
was
increased
from
0
to
100
ms,
while
PD
was
kept
constant
at
20
ms
(Fig.
2B).
With
PI-5
ms
chirps
elicited
a
continuous
burst
of
spikes
with
23.2
±
2.8
AP,
at
PI-15
ms
the
response
was
23.8
±
3.0
AP
and
it
slightly
increased
to
26.7
±
2.7
AP
at
PI-60
ms
(Fig.
2B,
top).
Over
the
range
of
PI
tested,
the
response
function
of
TH1-
AC1
revealed
a
gradual
increase
by
25.2%
(7.5
AP).
In
comparison,
the
phonotaxis
PI-response
function
(Fig.
2B,
bottom)
revealed
a
narrow
optimum
curve
with
a
maximum
characterized
by
the
scores
at
PI-15
ms
(95.2%)
and
PI-20
ms
(93.8%).
The
phonotactic
response
steeply
increased
by
74.5%
from
PI-5
to
15
ms
and
decreased
by
42.5%
from
PI-20
to
30
ms,
dropping
further
at
higher
PI.
The
PI-response
function
reached
values
>50%
within
the
range
of
10
-30
ms.
The
correlation
co-
efficient
between
both
data
sets
was
r
2
=
-0.18
(p
>
0.05)
and
demonstrated
that
the
response
functions
did
not
match.
Response
to
PP
When
we
varied
PP
between
10
and
98
ms,
the
overall
response
of
TH1-AC1
revealed
only
a
slight
variation.
At
PP-18
ms,
the
neu-
ron
generated
32.7
±
3.9
AP/chirp,
at
34
ms
it
responded
with
36.8
±
4.1
AP/chirp,
and
at
PP-90
ms
its
spike
activity
was
35.0
±
4.7
AP/chirp
(Fig.
2C).
The
PP-response
function
of
TH1-AC1
revealed
an
overall
high
level
of
activity
as
the
relative
response
was
>80%
at
all
PP
presented.
The
spike
activity
fluctuated
be-
tween
the
maximum
response
at
PP-74
ms
(38.4
±
5.0
AP/chirp)
and
the
minimum
response
at
PP-82
ms
(32.1
±
4.0
AP/chirp).
These
fluctuations
are
not
due
to
variability
in
the
neural
re-
sponse,
but
mirror
the
slight
differences
in
sound
energy
pre-
sented
for
different
pulse
periods,
as
the
number
of
sound
pulses
per
chirp
was
adapted
to
match
the
chirp
duration
(see
Materials
and
Methods).
Very
similar
fluctuations
were
also
described
for
a
local
thoracic
auditory
interneuron
by
Nabatiyan
et
al.
(2003).
In
response
to
the
PP
paradigm,
female
phonotaxis
exhibited
a
bandpass
like
tuning
as
previously
described
(Thorson
et
al.,
1982;
Doherty,
1985a;
Hedwig,
2006)
and
had
a
maximal
re-
sponse
of
93.9%
at
PP-34
and
42
ms.
From
the
maximum,
the
response
function
decreased
with
a
similar
slope
toward
lower
and
higher
PP.
When
PP
increased
from
18
to
34
ms,
phonotaxis
increased
by
73.8%
and
it
decreased
in
a
similar
way
by
71.0%
when
PP
changed
from
42
to
58
ms.
The
relative
phonotactic
response
was
>50%
within
a
PP
range
of
26
-50
ms
and
>80%
only
at
the
response
maxima
of
34
and
42
ms.
In
comparison,
the
relative
activity
of
TH1-AC1
was
>80%
for
all
PP
and
did
not
reveal
any
tuning;
the
correlation
coefficient
with
behavior
was
r
2
=
0.00
(p
>
0.05).
Tuning
of
local
brain
neurons
To
identify
brain
neurons
with
response
properties
that
match
the
selectivity
of
phonotaxis,
we
systematically
probed
the
ante-
rior
protocerebrum
and
analyzed
the
spike
activity
of
auditory
neurons.
In
the
following,
we
present
the
auditory
response
properties
of
four
local
brain
neurons,
B-LI2,
B-LC3,
B-LI3,
and
B-LI4,
and
compare
their
temporal
tuning
with
that
of
phono-
taxis.
A
statistical
treatment
of
the
neural
data
is
given
in
Table
1.
Impact
of
PD
When
tested
with
different
PD,
female
crickets
exhibited
a
broadly
tuned
phonotactic
response
with
a
maximum
at
PD-15
ms
(Fig.
2A).
When
stimulated
with
the
PD
paradigm,
interneu-
ron
B-LI2
was
rhythmically
depolarized
by
the
pattern
of
sound
pulses
and
generated
two
to
three
spikes
per
pulse
(Fig.
3A).
For
all
PD
patterns
tested,
the
response
at
the
beginning
of
a
chirp
was
slightly
stronger.
With
increasing
pulse
duration,
the
response
of
the
neuron
increased
from
8.1
±
0.9
AP/chirp
at
PD-8
ms
to
Kostarakos
and
Hedwig
Pattern
Recognition
in
the
Cricket
Brain
Table
1.
Statistical
treatment
of
neural
data
J.
Neurosci.,
July
11,
2012
32(28):9601-9612
9605
Neurons
PD
PI
PP
Below
max
Above
max
Below
max
Above
max
Below
max
Above
max
B-LC3
15-20
ms
20-35
ms
5-15
ms
15-25
ms
26-34
ms
34-42
ms
N
=
5
p
=
0.005
p
=
0.018
p
<
0.001
p
=
0.018
p
=
0.067
p
=
0.063*
B-LI3
10-20
ms
20-40
ms
5-15
ms
15-30
ms
26-34
ms
34-42
ms
N
=
5
p
=
0.008
p
=
0.100
p
=
0.001
p
=
0.01
p
=
0.008
p
=
0.013
B-LI4
10-15
ms
20
-30
ms
5-15
ms
20-25
ms
26-34
ms
34-58
ms
N
=
3
p
=
0.052
p
=
0.042"
p
=
0.022"
p
=
0.038
p
=
0.061
p
=
0.003
Statistical
treatment
for
significant
differences
bya
paired
t
test.
Data
with
asterisk
were
obtained
bya
Wilcoxon
signed
rank
test
for
samples
that
were
not
normally
distributed.
The
smallest
significant
differences
of
relative
values
below
and
above
the
maximal
responses
(indicated
in
bold)
are
shown
for
each
paradigm.
Due
to
the
small
sample
sizes,
we
considered
differences
as
significant
at
values
of
p
<
0.1
in
order
to
avoid
errors
that
can
occur
at
small
sample
sizes
due
to
large
5Ds.
Data
with
two
asterisks
showed
no
significant
differences
referred
to
the
maximal
response
as
data
were
not
normally
distributed
or
had
a
high
SD.
For
these
data,
we
show
the
smallest
significant
differences
below
and
above
the
nearest
value
to
the
maximum.
Pulse
Duration
A
B-LI2
B
B-LC3
li
B-LI3
D
B-LI4
8
ms
20
ms
A,
A.
Mk
Jm,
Am,
5
1
20
40
ms
mV
/
.
v
I
5
mV
-50
0
50
100
150
200
-50
0
50
100
150
200
Time
[ms]
Time
Ems]
120
-
100
Ct
cc
0
0
5
10
15
20
25
30
35
40
5
10
15
20
25
30
35
Pulse
Duration
[ms]
Pulse
Duration
[ms]
-50
0
50
100
150
200
-50
0
50
100
150
200
Time
[ms]
Time
[ms]
14
12
10
8
6
4
2
0
0
40
5
10
15
20
25
30
35
40
5
10
15
20
25
30
35
40
Pulse
Duration
[ms]
Pulse
Duration
[ms]
5
3
0
N=5
20
5
10
N=2
N=3
Figure
3.
Activity
of
B-LI2,
B-LC3,
B-LI3,
and
B-LI4
and
phonotactic
tuning
of
females
to
different
PD.
Top,
Response
of
neurons
at
PD-8,
20,
and
40
ms.
Bottom,
Phonotactic
(gray)
and
neural
(black)
PD-response
functions.
A
,
Activity
and
response
function
of
B-LI2.
Neural
data
are
based
on
N
=
2
females
and
n=
13
test
sequences,
2
stainings.
B,
Activity
and
response
function
of
B-LC3.
Neural
data
are
based
on
N
=
5
females
and
n
=
23
test
sequences,
4
stainings.
C,
Activity
and
response
function
of
B-LI3.
Neural
data
are
based
on
N
=
5
females
and
n
=
18
test
sequences,
4
stainings.
D,
Activity
and
response
function
of
B-LI4.
Neural
data
are
based
on
N
=
3
females
and
n
=10
test
sequences,
2
stainings.
The
left
scale
indicates
relative
neural
and
phonotactic
activity,
and
the
right
scale
shows
the
absolute
values
of
spike
activity.
Error
bars
show
SD
of
relative
values.
10.9
±
4.4
AP/chirp
at
PD
-
20
ms
and
12.9
±
4.9
AP/chirp
at
PD-35
ms;
the
response
at
PD-40
ms
then
slightly
fell
to
12.1
±
5.8
AP/chirp
(Fig.
3A,
top).
Correspondingly,
the
PD-response
function
of
B-LI2
increased
by
55.6%
(7.3
AP)
from
PD-4
ms
to
PD-35
ms,
and
then
decreased
by
8.5%
(0.8
AP)
from
35
to
40
ms.
The
response
function
of
B-LI2
revealed
no
optimum.
It
did
not
match
with
the
phonotactic
response
function
(Fig.
3A,
bottom)
and
the
correlation
coefficient
was
only
r
2
=
0.10
(p
>
0.05).
The
PD-dependent
activity
of
B-LI2,
however,
was
similar
to
the
re-
sponse
of
TH1-AC1
(Fig.
2)
and
neural
PD-response
functions
of
these
two
neurons
exhibited
a
highly
significant
correlation
of
r
2
=
0.91
(p
<
0.001).
The
activity
of
the
contralaterally
projecting
neuron
B-LC3
was
more
complex
(Fig.
3B).
At
PD-8
ms,
the
neuron
responded
with
subthreshold
EPSPs
of
4-
8
mV
to
the
first
two
sound
pulses
of
the
chirps;
the
following
pulses
elicited
suprathreshold
EPSPs
and
triggered
spikes.
On
average,
B-LC3
generated
3.6
±
1.7
AP/chirp
at
PD-8
ms,
at
PD-20
ms
it
elicited
EPSPs
of
15
mV
amplitude
and
12.0
±
1.1
AP/chirp,
and
at
PD-40
ms
the
neuron
showed
a
continuous
depolarization
of
10
mV
generating
8.8
±
1.8
AP/chirp.
At
PD-20
ms,
B-LC3
always
responded
more
strongly
to
the
second
pulse
of
a
chirp
(3.4
±
0.3
AP/sound
pulse)
than
to
the
first
(1.8
±
0.5
AP/sound
pulse).
This
was
different
from
the
previously
described
responses
of
TH1-AC1
and
B-LI2.
The
PD-response
function
of
B-LC3
revealed
a
maximum
at
PD-20
ms.
From
PD-4
to
15
ms,
spike
activity
increased
by
84.0%
(10.3
AP)
but
decreased
by
only
25.6%
(3.1
AP)
from
PD-20
to
40
ms.
The
PD-response
functions
of
the
behavior
and
B-LC3
in-
creased
in
parallel
up
to
PD-15
ms
and
exhibited
a
similar
max-
imum
at
15-20
ms.
However,
when
PD
increased
from
20
to
40
ms,
phonotaxis
decreased
more
(60.0%)
than
the
neural
response
(25.6%)
and
both
response
functions
matched
only
weakly
with
r
2
=
0.40
(p
=
0.05).
Although
sound
was
continuous
for
160
ms
at
PD-40
ms,
in
60%
of
the
recorded
B-LC3,
the
response
tran-
mV
120
9606
J.
Neurosci.,
July
11,
2012
32(28):9601-9612
Kostarakos
and
Hedwig
Pattern
Recognition
in
the
(ticket
Brain
Pulse
Interval
A
B-LI2
B
B-LC3
B-L
1
3
B-L
14
5
ms
15
ms
-50
0
50
100 150
200
-50
0
50
100
„fr.r
60
ms
I
mv
0
100
200
300
Time
[ms]
150
200
-50
0
50
100
150
200
-50
0
50
100
150
200
m
10
v
0
100
200
300
Time
[ms]
I
L
0
100
200
300
Time
[ms]
ma
m
ma
0
100
200
300
Time
[ms]
N=2
N=5
N=5
N=3
15
0
2
10
4
2
0
10
20
30
40
50
60
70
80
90100
0
10
20
30
40
50
60
70
80
90100
0
10
20
30
40
50
60
70
80
90100
0
10
20
30
40
50
60
70
80
90100
Pulse
Interval
[ms]
Pulse
Interval
[ms]
Pulse
Interval
[ms]
Pulse
Interval
[ms]
Figure
4.
Activity
of
B-LI2,
B-LC3,
B-LI3,
and
B-LI4and
p
honotactic
tuning
offemales
to
different
PI.
Top,
Response
of
neurons
at
P1-5,
15,
and
60
ms.
Bottom,
Phonotactic
(gray)
and
neural
(black)
PD-response
functions.
A,
Activity
and
response
function
of
B-LI2.
Neural
data
are
based
on
N
=
2
females
and
n
=
13
test
sequences,
2
stainings.
B,
Activity
and
response
function
of
B-LC3.
Neural
data
are
based
on
N
=
5
females
and
n
=
23
test
sequences,
4
stainings.
(Activity
and
response
function
of
B-LI3.
Neural
data
are
based
on
N
=
5
females
and
n
=
18
test
sequences,
4
stainings.
D,
Activity
and
response
function
of
B-LI4.
Neural
data
are
based
on
N
=
3
females
and
n
=
10
test
sequences,
2
stainings.
The
left
scale
indicates
relative
neural
and
phonotactic
activity,
and
the
right
scale
shows
the
absolute
values
of
spike
activity.
Error
bars
show
SD
of
relative
values.
siently
appeared
to
be
a
sequence
of
rhythmically
modulated
EP-
SPs
generating
bursts
of
two
to
three
spikes
with
a
mean
cycle
period
of
29
ms.
The
response
properties
of
the
local
interneuron
B-LI3
were
similar
to
the
response
of
interneuron
B-LC3.
At
PD-8
ms,
B-LI3
(Fig.
3C)
generated
2.4
±
1.9
AP/chirp;
its
activity
increased
to
6.7
±
1.1
AP/chirp
at
PD-20
ms,
responding
with
10
mV
EPSPs
and
spikes,
and
at
PD-40
ms
the
activity
of
the
neuron
decreased
to
4.9
±
1.5
AP/chirp.
When
tested
with
PD-20
ms
pulses,
the
response
to
the
second
pulse
of
a
chirp
was
higher
(2.1
±
0.3
AP/sound
pulse)
than
the
response
to
the
first
pulse
(1
±
0.4
AP/sound
pulse).
The
PD-response
function
revealed
the
stron-
gest
response
at
PD-20
ms.
Overall
activity
increased
by
81.1%
(5.7
AP),
with
PD
increasing
from
4
to
15
ms
but
only
gradually
decreased
by
25.3%
(1.8
AP)
when
PD
further
increased
from
20
to
40
ms.
The
PD-response
function
of
B-LI3
matched
with
the
phonotactic
behavior
slightly
better
than
B-LC3
and
gave
a
cor-
relation
coefficient
of
r
2
=
0.46
(p
<
0.05).
Different
from
the
neural
activity
described
so
far,
the
re-
sponse
of
B-LI4
always
started
with
an
inhibition
(Fig.
3D).
At
PD-8
ms,
the
first
sound
pulse
triggered
an
IPSP
of
4
mV
ampli-
tude
and
45
ms
duration,
which
was
followed
by
EPSPs
and
a
mean
of
1.9
±
0.6
AP/chirp
elicited
by
the
consecutive
sound
pulses.
At
PD-20
ms,
the
chirp
triggered
an
initial
inhibition
that
lasted
for
15
ms,
which
was
followed
by
a
mixture
of
IPSPs,
EPSPs,
and
spikes
leading
to
a
mean
response
of
4.2
±
1.2
AP/
chirp.
Chirps
with
PD-40
ms
evoked
an
initial
inhibition
of
120
ms
duration,
varying
between
-2
and
-3.5
mV
and
an
excitatory
response
of
0.8
±
1.4
AP/chirp
generated
toward
the
end
of
the
stimulus.
The
PD-response
function
of
B-LI4
had
the
shape
of
an
optimum
curve
with
the
maximum
spike
activity
at
PD-15
ms
(4.7
±
0.2
AP/chirp).
With
PD
increasing
from
4
to
15
ms,
spike
activity
increased
steeply
by
93.5%
(4.6
AP)
and
different
from
interneurons
B-LC3
and
BLI3
then
also
strongly
decreased
by
84.8%
(4.1
AP)
when
PD
increased
from
15
to
35
ms.
Thus,
the
overall
shape
of
the
PD-response
function
of
the
neuron
signifi-
cantly
matched
the
phonotactic
PD-response
giving
a
correlation
coefficient
of
r
2
=
0.90
(p
<
0.001).
Impact
of
PI
When
tested
with
different
PI,
the
phonotactic
response
of
the
females
revealed
a
narrowly
tuned
optimum
curve
with
a
maxi-
mal
response
at
PI-15
ms.
We
compared
this
with
the
spike
pat-
tern
of
the
auditory
brain
neurons
(Fig.
4,
top)
and
their
PI-response
functions
(Fig.
4,
bottom).
In
neuron
B-LI2,
spike
activity
increased
with
increasing
PI.
At
PI-5
ms,
it
responded
with
a
continuous
burst
of
9.5
±
2.5
AP/chirp;
at
PI-15
ms,
the
response
was
rhythmically
coupled
to
the
sound
pulses
and
10
±
3.2
AP/chirp,
and
at
PI-60
ms
the
activity
of
the
neuron
increased
to
12.4
±
2.0
AP/chirp
(Fig.
4A).
The
PI-response
function
revealed
a
linear
increase
of
40.4%
(5.7
AP)
over
the
range
of
0
-100
ms.
Correspondingly,
the
PI-
response
of
B-LI2
did
not
match
the
tuned
phonotactic
response
function
as
the
correlation
coefficient
was
r
2
=
-0.25
(p
>
0.05).
The
response
function
was,
however,
very
similar
to
the
PI-
response
function
of
TH1-AC1,
as
indicated
by
a
correlation
co-
efficient
of
r
2
=
0.96
with
p
<
0.001.
In
interneuron
B-LC3,
chirps
with
PI-5
ms
elicited
EPSPs
of
10
-13
mV
amplitude,
which
were
modulated
by
the
pulse
pat-
tern
and
generated
6.9
±
1.5
AP/chirp
(Fig.
4B).
At
PI-15
ms,
B-LC3
responded
with
EPSPs
of
15
mV
amplitude
and
bursts
of
spikes
to
each
pulse,
generating
10.1
±
1.6
AP/chirp.
Again,
the
response
of
the
neuron
to
the
second
pulse
was
stronger
than
to
the
first
pulse
(compare
with
Figs.
4B,
3B).
At
PI-60
ms,
EPSPs
120
100
CO
o
80
CL
60
(1.)
40
CO
20
a)
ct
0
2-
3
1
E
0_
O
CO
0_
a)
ct
Kostarakos
and
Hedwig
Pattern
Recognition
in
the
Cricket
Brain
J.
Neurosci.,
July
11,
2012
32(28):9601-9612
9607
Pulse
Period
A
B-LI2
B
B-LC3
C
B-LI3
B-LI4
18
ms
P.ABARAlkilaliaLl•
IN,AAMAA•AAAMAA
5
90
ms
I
mv
2
I
m
o
v
tmV
120
100
0
100
200
300
Time
[ms]
0
100
200
Time
[ms]
U)
15
80
a_
Cll
60
10
0)
40
20
T
N=2
N=5
0
0
J=i
RFS
300
0
100
200
300
0
100
200
300
Time
[ms]
Time
[ms]
5
10
6
4
2
N=5
N=3
0
0
0
10
18
26
34
42
50
58
66
74 82
90
98
10
18
26
34
42
50
58
66
74
82
90
98
0
18
26
34
42
50
58
66
74 82
90
98
0
18
26
34
42
50
58
66
74 82
90
98
Pulse
Period
[ms]
Pulse
Period
[ms]
Pulse
Period
[ms]
Pulse
Period
[ms]
12
10
4
3<
a)
2
,W
0_
a)
Figure
5.
Activity
of
B-LI2,
B-LC3,
B-LI3,
and
B-LI4
and
phonotactic
tuning
of
females
to
different
PP.
Top,
Response
of
neurons
at
PP-18,
34,
and
90
ms.
Bottom,
Phonotactic
(gray)
and
neural
(black)
PD-response
functions.
A,
Activity
and
response
function
of
B-LI2.
Neural
data
are
based
on
N
=
2
females
and
n
=
13
test
sequences,
2
stainings.
B,
Activity
and
response
function
of
B-LC3.
Neural
data
are
based
on
N
=
5
females
and
n
=
23
test
sequences,
4
stainings.
C,
Activity
and
response
function
of
B-LI3.
Neural
data
are
based
on
N
=
5
females
and
n
=
18
test
sequences,
4
stainings.
D,
Activity
and
response
function
of
B-LI4.
Neural
data
are
based
on
N
=
3
females
and
n
=10
test
sequences,
2
stainings.
The
left
scale
indicates
relative
neural
and
phonotactic
activity,
and
the
right
scale
shows
the
absolute
values
of
spike
activity.
Error
bars
show
SD
of
relative
values.
were
-10
mV
but
generated
only
1-2
APs
per
pulse
and
the
spike
activity
of
the
neuron
decreased
to
5.7
±
1.8
AP/chirp.
The
PI-
response
function
of
B-LC3
revealed
an
optimum
curve
with
maximum
activity
at
PI-15
ms.
With
PI
increasing
from
5
to
15
ms,
spike
activity
increased
by
30.7%
(3.2
AP).
From
20-30
ms,
the
activity
of
the
neuron
decreased
by
26.9%
(2.9
AP)
and
then
stayed
at
a
relatively
high
level
of
55-
60%
at
longer
PI.
As
the
tuning
of
phonotaxis
and
the
PI-response
function
of
the
neuron
had
a
similar
maximum
and
shape,
there
was
a
highly
significant
relationship
between
the
two
with
a
correlation
coefficient
of
r
2
=
0.77
(p
<
0.001).
B-LI3
generated
4.5
±
0.9
AP
to
chirps
with
PI-5
ms
(Fig.
4C)
but
did
not
respond
to
the
first
pulses
of
a
chirp.
At
PI-15
ms,
the
response
increased
to
5.9
±
0.9
AP/chirp
with
the
response
to
the
first
pulse
of
the
chirp
being
weaker
than
the
following
ones,
as
with
the
PD
paradigm
(Fig.
3C).
Activity
then
decreased
to
3.6
±
0.9
AP/chirp
at
PI-60
ms.
The
PI-response
function
of
B-LI3
showed
a
maximum
spike
activity
at
PI-15
ms.
With
PI
increasing
from
5
to
15
ms,
activity
increased
by
23%
(1.4
AP),
and
between
20
and
30
ms,
it
decreased
by
26.6%
(1.7
AP)
and
stayed
at
-60%
at
longer
PI.
Similar
to
B-LC3,
the
PI-response
function
of
B-LI3
matched
the
phonotactic
response
with
r
2
=
0.75
(p
<
0.001)
and
high
significance.
The
best
agreement
between
neural
and
phonotactic
tuning
was
obtained
for
interneuron
B-LI4
(Fig.
4D).
At
PI-5
ms,
the
response
of
the
neuron
started
with
IPSPs
of
-3.6
mV
and
only
toward
the
end
of
the
chirp
did
it
generate
EPSPs
with
0.2
±
0.3
AP/chirp.
At
PI-15
ms,
the
response
of
the
neuron
began
with
an
inhibition
of
-4
mV
amplitude
and
18
ms
duration,
but
the
consecutive
spike
activity
increased
to
2.9
±
1.8
AP/chirp.
When
PI
was
increased
to
60
ms,
sound
pulses
generally
elicited
IPSPs
of
-2.5
mV
amplitude
and
19
-35
ms
duration
followed
by
a
weak
spike
activity
of
0.2
±
0.4
AP/chirp.
The
PI-response
function
of
B-LI4
revealed
a
narrowly
tuned
optimum
curve
with
a
maximal
response
of
3
±
1.5
AP/chirp
at
PI-20
ms.
Spike
activity
increased
by
76.6%
(2.8
AP)
with
PI
increasing
from
5
to
15
ms
and
it
decreased
by
57.2%
(2.3
AP)
when
PI
increased
from
20
to
30
ms.
Toward
longer
PI,
the
relative
activity
of
the
neuron
dropped
further
and
was
<10%.
Thus,
the
PI-response
function
of
B-LI4
was
very
similar
to
the
tuning
of
phonotactic
behavior,
and
both
matched
with
a
high
correlation
coefficient
of
r
2
=
0.93
and
significance
of
p
<
0.001.
Impact
of
PP
The
PP
paradigm
combines
changes
in
PI
and
PD.
The
phono-
tactic
response
of
females
was
strongest
between
PP-34
ms
and
PP-42
ms.
The
response
pattern
of
local
brain
neurons
to
chirps
with
different
PP
are
compared
with
the
behavior
in
Figure
5.
B-LI2
responded
to
PP-18
ms
with
a
continuous
burst
of
spikes
and
generated
14.9
±
1.6
AP/chirp;
PP-34
ms
evoked
rhythmic
activity
coupled
to
the
sound
pulses
with
15.3
±
4.9
AP/chirp,
and
at
PP-90
ms,
each
pulse
elicited
an
EPSP
with
a
burst
of
spikes
and
the
response
was
13.6
±
4.4
AP/chirp
(Fig.
5A,
top).
The
PP-response
function
of
B-LI2
showed
that
its
relative
spike
activity
remained
always
high
and
>70%
(Fig.
5A,
bottom).
Minor
fluctuations
in
spike
activity
at
different
PP
mirrored
the
differences
in
sound
energy
over
the
paradigm
in
a
similar
way
as
for
TH1-AC1.
The
PP-response
function
of
B-LI2
exhibited
no
optimum
and
did
not
correspond
to
the
behavior
as
indicated
by
a
correlation
coefficient
of
r
2
=
0.07
(p
>
0.05).
The
PP-response
function
of
B-LI2,
however,
matched
the
PP-response
function
of
TH1-AC1
(r
2
=
0.34;p
<
0.05).
In
the
contralaterally projecting
neuron
B-LC3,
a
PP-18
ms
elicited
a
gradual
slow
depolarization
with
EPSPs
coupled
to
the
r
2
=0.00
(p
>
0.05)
r
2
=
0.10
(p
>
0.05)
r
2
=0.40
(p
=
0.05)
r
2
=0.46
(p
<
0.05)
'
2
=0.90
(p
<
0.001)
r
2
=
-0.18
(p
>
0.05)
r
2
=
-0.25
(p
>
0.05)
r
2
=
0.77
(p
<
0.001)
r
2
=
0.75
(p
<
0.001)
r
2
=
0.93
(p
<
0.001)
r
2
=
0.00
(p
>
0.05)
r
2
=
0.07
(p
>
0.05)
r
2
=
0.77
(
p
<
0.001)
r
2
=0.92
(p
<0.001)
r
2
=
0.94
(p
<
0.001)
TH1-AC1
B-LI2
B-LC3
B-LI3
B-LI4
Behaviour
TH1-AC1
B-LI2
*
B-LC3
B-L
13
B-LI4
10
20
30
40
10
20
30
40
10
20
30
40
Pulse
Duration
[ms]
Pulse
Duration
[ms]
Pulse
Duration
[ms]
Colour
code
for
relative
responses
of
behaviour
and
neurons
0%
20%
I
40%
0
60%
Q
80%
100%
Figure
6.
Relative
responses
of
phonotaxis
and
brain
neurons
plotted
against
pulse
duration
(abscissa)
and
pulse
interval
(ordinate)
based
on
all
data
points
of
the
PD,
PI,
and
PP
test
paradigms
(e.g.,
data
along
the
diagonal
from
the
bottom
left
to
the
top
right
represent
responses
to
the
PP
paradigm).
In
each
plot,
the
x-axis
starts
at
a
pulse
duration
of
4
ms.
Responses
are
color
coded
with
red
showing
the
area
of
maximum
response,
indicated
by
asterisks.
100
7
80
E
(73
60
E
40
a)
co
a
20
100
77
)
80
E
(73
60
a)
40
a)
EL
20
0
9608
J.
Neurosci.,
July
11,
2012
32(28):9601-9612
Kostarakos
and
Hedwig
Pattern
Recognition
in
the
Cricket
Brain
pattern
of
pulses
and
a
mean
response
of
5.0
±
1.4
AP/chirp
(Fig.
5B).
At
PP-34
ms,
the
mean
spike
activity
increased
to
15.3
±
2.5
AP/chirp.
The
neuron
responded
only
weakly
to
the
first
sound
pulse
of
a
chirp,
strongest
to
pulses
2-4
with
3
AP
and
subse-
quently
its
activity
decreased.
Sound
pulses
at
PP-90
ms
elicited
pronounced
EPSPs
of
10
mV
amplitude
with
a
spike
activity
of
6.1
±
1.5
AP/chirp.
The
PP-response
function
of
B-LC3
revealed
an
optimum
curve
with
a
maximal
response
of
15.3
AP/chirp
at
PP-34
ms.
Spike
activity
increased
by
66.3%
when
PP
increased
from
18
to
34
ms
(10.4
AP/chirp)
and
de-
creased
by
43.8%
(6.9
AP)
when
PP
in-
creased
further
from
34
to
50
ms.
Thus
the
PP-response
of
B-LC3
was
similar
to
phonotactic
tuning
at
short
PP
but
re-
mained
at
higher
amplitudes
at
long
PP.
Neural
and
phonotactic
tuning
matched
highly
significantly
with
r
2
=
0.77
(p
<
0.001).
At
PP-18
ms,
neuron
B-LI3
(Fig.
5C)
did
not
respond
until
the
fifth
sound
pulse
and
overall
with
3.3
±
1.9
AP/chirp.
Its
activity
increased
at
PP-34
ms
to
10.2
±
2.5
AP/chirp,
and,
like
B-LC3,
it
generated
its
strongest
response
to
pulses
2-4
of
the
chirp.
Activity
decreased
at
PP-90
ms
to
2.9
±
0.6
AP/chirp;
sound
pulses
elicited
EPSPs
with
a
spike
response
of
1-2
AP.
The
PP-response
function
of
B-LI3
had
the
shape
of
an
optimum
curve
with
a
maximum
response
at
PP-34
ms.
Spike
activity
increased
by
64.3%
(6.9
AP)
with
PP
increasing
from
18
to
34
ms
and
then
decreased
by
56.4%
(6.1
AP)
when
PP
in-
creased
from
34
to
58
ms;
it
reached
a
minimal
value
of
28
3%
at
PP-98
ms
The
PP-response
function
of
B-LI3
matched
the
phonotactic
tuning
better
than
B-LC3
with
a
correlation
coefficient
of
r
2
=
0.92
(p
<
0.001).
The
auditory
response
of
B-LI4
was
again
characterized
by
inhibition
and
excitation
and
showed
the
best
match
to
phonotactic
behavior
(Fig.
5D).
At
PP-18
ms,
the
first
sound
pulses
of
the
chirps
elicited
an
inhibition
of
-4.3
mV,
which
was
followed
by
a
weak
EPSP,
generating
0.3
±
0.6
AP/
chirp.
At
PP-34
ms,
the
neuron
generated
4.1
±
2.8
AP/chirp.
Its
response
started
with
an
inhibition
of
-3.4
mV
lasting
for
-40
ms.
The
consecutive
sound
pulses
then
elicited
spikes
that
were
followed
by
a
mix
of
EPSPs
and
IPSPs
toward
the
end
of
the
response.
Inhibition
dominated
the
response
to
PP-90
ms.
Sound
pulses
triggered
an
inhibition
of
-3
to
-5
mV
amplitude
and
20
-50
ms
duration,
and
overall
the
neuron
generated
only
0.4
±
0.7
AP/chirp.
The
PP-response
function
of
B-LI4
had
an
opti-
mum
curve
with
a
maximum
response
at
PP-34
ms.
Spike
activity
increased
by
95.2%
(3.8
AP)
with
PP
increasing
from
18
to
34
ms
and
subsequently
decreased
by
85.5%
(3.6
AP)
when
PP
in-
creased
further
from
34
to
58
ms.
Moreover,
and
different
from
B-LC3
and
B-LI3,
at
very
short
or
long
PP,
the
response
function
had
values
<5%
and
thereby
had
the
highest
match
with
phono-
taxis,
with
a
correlation
coefficient
of
r
2
=
0.94
(p
<
0.001).
In
comparison,
there
was
no
selectivity
for
species-specific
pulse
patterns
at
the
level
of
the
ascending
interneuron
TH1-AC1
and
the
brain
neuron
B-LI2.
Selectivity
for
temporal
patterns
increased
from
B-LC3
and
B-LI3
to
B-LI4
(Table
2).
B-LC3
and
Table
2.
Correlation
between
phonotaxis
and
neural
responses
Neurons
PD
PI
PP
Correlation
coefficient
and
significance
between
behavioral
response
data
and
neural
response
data
given
for
all
test
patterns.
Values
in
bold
show
a
statistically
significant
match.
B-LI3
revealed
a
significant
match
with
behavior
for
different
PI
and
PP,
but
only
B-LI4
exhibited
a
highly
significant
match
with
the
behavior
over
all
test
paradigms.
The
relative
response
of
behavior
and
brain
neurons
was
plotted
in
pulse
duration/pulse
interval
profiles
(Fig.
6).
In
the
color
coded
diagrams,
the
areas
of
highest
responses
are
indicated
in
red
and
marked
by
an
asterisk.
The
diagrams
demonstrate
a
close
match
between
the
tuning
of
the
behavior
and
the
response
profile
of
B-LI4.
TH1-AC1
and
B-LI2
show
a
broad
unspecific
response
profile,
whereas
B-LI3
and
B-LC3
exhibit
some
degree
of
temporal
tuning
but
are
also
activated
over
the
whole
range
of
pulse
intervals
tested.
Opposite
to
the
improvement
of
pattern
selectivity,
the
maxi-
mum
spike
activity
in
response
to
acoustic
stimulation
decreased
by
-90%
from
the
ascending
interneuron
TH1-AC1
through
to
B-LI4
(Table
3).
The
average
of
the
maximal
responses
over
all
test
paradigms
was
34.9
AP/chirp
for
TH1-AC1,
14.3
AP/chirp
for
B-LI2,
12.5
AP/chirp
for
B-LC3,
7.6
AP/chirp
for
B-LI3,
and
3.9
AP/chirp
for
B-LI4.
Whereas
the
overall
spike
activity
de-
creased,
the
average
spike
latencies,
as
calculated
for
all
sound
pulses
of
chirps,
increased
from
20.7
ms
(TH1-AC1)
to
24.6
ms
(B-LI2)
to
31.6
ms
(B-LC3)
to
35.5
ms
(B-LI3)
and
finally
to
37.1
ms
(B-LI4),
indicating
a
sequenced
flow
of
auditory
activity
in
the
pathway.
In
all
five
neurons
tested,
we
found
no
evidence
for
Kostarakos
and
Hedwig
Pattern
Recognition
in
the
Cricket
Brain
Table
3.
Average
maximal
spike
activity
of
neurons
Neurons
PD
PI
PP
Average
over
all
maxima
(AP\chirp)
TH1-AC1
40
ms
100
ms
74
ms
37.3
±
5.1
29.0
±
4.5
38.4
±
5.0
34.9
±
5.1
B-LI2
35
ms
100
ms
58
ms
12.9
±
4.9
14.4
±
1.3
15.6
±
4.4
14.3
±
1.3
B-LC3
20
ms
15
ms
34
ms
12.0
±
1.1
10.1
±
1.6
15.3
±
2.5
12.5
±
2.6
B-LI3
20
ms
15
ms
34
ms
6.7
±
1.1
5.9
±
0.9
10.2
±
2.5
7.6
±
2.3
B-LI4
15
ms
20
ms
34
ms
4.7
±
0.2
3
±
1.5
4.1
±
2.8
3.9
±
0.9
Spike
activity
of
the
neurons
(ARchirp
±
SD)
at
their
maximal
response
given
for
all
test
paradigms.
Average
activity
(ARchirp)
over
all
response
maxima
of
all
test
paradigms
is
given
in
the
right
column.
ongoing
maintained
oscillations
of
the
membrane
potential
after
stimulation
with
a
calling-song-like
sound
pattern.
The
response
of
selective
neurons
was
higher
to
PD-20
ms
compared
with
PI-20
ms,
although
the
corresponding
chirps
were
identical.
In
B-LI4,
a
coincidental
difference
of
only
one
spike
led
to
a
relative
difference
of
30%.
Habituation
in
the
audi-
tory
pathway
may
have
contributed
to
this
difference.
In
the
pulse
duration
paradigm,
the
chirps
preceding
the
test
with
20
ms
pulse
duration
had
an
overall
lower
sound
energy
as
the
chirps
preced-
ing
the
corresponding
test
in
the
pulse
interval
paradigm.
Corre-
spondingly,
the
PI
response
function
showed
left
to
its
maximum
a
higher
spike
activity
than
the
PD
response
function.
Temporal
dynamic
underlying
pattern
selectivity
Temporal
pattern
recognition
requires
at
least
two
consecutive
sound
pulses
to
allow
an
analysis
of
the
pulse
rate
(Poulet
and
Hedwig,
2005).
How
many
pulses
and
intervals
does
the
system
require
for
a
selective
response
to
specific
PI?
By
using
a
test
paradigm
with
a
successive
variation
of
the
pulse
interval,
we
tested
whether
temporal
filtering
is
established
on
a
pulse-to-
pulse
basis
after
a
specific
pulse
interval,
or
whether
pattern
rec-
ognition
requires
integrating
over
several
sound
pulses
and
intervals.
We
tested
the
response
of
TH1-AC1
and
B-LC3
by
systematically
altering
the
pulse
intervals
in
a
sequence
of
six
pulses.
The
PI
between
the
first
and
second
pulse
had
a
duration
of
21
ms
based
on
the
species-specific
pattern.
It
was
followed
by
pulses
with
a
preceding
PI
of
31,
41,
71,
and
again
21
ms.
This
sequence
was
repeated
after
230
ms.
The
resulting
PST
histo-
grams
and
the
average
of
the
instantaneous
spike
frequency
were
calculated
over
40
stimuli
for
TH1-AC1
(Fig.
7A)
and
over
10
stimuli
for
B-LC3
in
a
female
(Fig.
7B),
which
showed
an
excep-
tional
selectivity
for
specific
PI.
In
the
following,
we
provide
the
mean
data
for
TH1-AC1
and
B-LC3
obtained
in
three
females,
respectively;
each
neuron
was
tested
with
10
sequences.
TH1-AC1
responded
to
each
sound
pulse
of
the
paradigm
with
a
mean
of
7.1
±
0.5
spikes
(Fig.
7A).
Over
the
whole
se-
quence,
its
response
to
the
consecutive
pulses
gradually
dropped
from
7.9
±
1.4
to
6.6
±
0.7
AP.
The
mean
spike
frequency
of
TH1-AC1
was
in
the
range
of
346-384
Hz
for
all
pulses
(Fig.
7A,
bottom).
As
with
the
previous
paradigms,
interneuron
TH1-AC1
exhibited
no
selectivity
for
any
PI
(Fig.
7B);
for
example,
the
activity
of
TH1-AC1
even
dropped
from
7.9
±
1.4
AP
after
a
PI
of
230
ms
to
7.3
±
0.9
AP
in
response
to
the
sound
pulse
after
a
PI
of
21
ms.
In
contrast,
the
activity
of
B-LC3
did
depend
on
the
duration
of
the
interval
preceding
a
sound
pulse
(Fig.
7B).
The
first
sound
pulse
of
the
sequence
occurred
after
a
PI
of
230
ms
and
elicited
1.9
±
0.4
AP
and
a
mean
spike
rate
of
84.79
Hz.
The
second
pulse,
J.
Neurosci.,
July
11,
2012
-
32(28):9601-9612
9609
occurring
after
an
interval
of
21
ms,
elicited
a
considerably
stron-
ger
response
with
3
±
0.5
AP/sound
pulse
and
a
spike
rate
of
202.6
Hz.
The
response
to
the
third
pulse
after
an
interval
of
31
ms
was
only
2.1
±
0.8
AP
with
a
spike
rate
127.2
Hz.
After
the
following
even
longer
intervals
of
41
and
71
ms,
the
neuron
re-
sponded
only
with
1.2
±
0.8
AP/sound
pulse
(47.3
Hz
spike
rate)
and
1.5
±
0.8
AP/sound
pulse
(78.4
Hz
spike
rate),
respectively.
However,
to
the
last
pulse
of
the
sequence,
which
again
was
pre-
sented
after
an
interval
of
21
ms,
the
neuron
generated
a
strong
response
with
2.4
±
0.6
AP
with
a
spike
rate
of
178.9
Hz.
In
the
PST
histogram
and
the
average
of
the
instantaneous
spike
rate
of
a
selected
female
(Fig.
7B,
bottom),
the
enhanced
responses
of
B-LC3
to
sound
pulses
occurring
after
an
interval
of
21
ms
clearly
stand
out.
This
test
paradigm
therefore
indicates
that
temporal
filtering
as
reflected
by
the
B-LC3
neuron
is
based
on
a
pulse-to-
pulse
processing
and
that
the
response
to
a
pulse
crucially
de-
pends
on
the
duration
of
the
preceding
pulse
interval.
The
response
was
particular
strong,
when
the
preceding
interval
matched
the
species-specific
pulse
interval.
For
interneuron
B-LI4,
time
interval-dependent
changes
in
synaptic
and
spike
activity
could
be
tested
more
systematically
with pairs
of
pulses
for
which
PI
was
varied
in
5-10
ms
incre-
ments
(Fig.
7C).
A
single
sound
pulse
elicited
an
IPSP
with
a
duration
of
-17
ms
that
was
followed
by
a
subthreshold
EPSP
after
which
the
response
returned
to
the
resting
potential.
After
that,
there
was
no
indication
of
an
oscillatory
response
of
the
B-LI4
membrane.
At
a
PI
of
5
ms,
the
EPSP
was
followed
by
a
pronounced
inhibition
lasting
for
45
ms
(Fig.
7,
asterisk).
At
PI-10
ms,
however,
the
response
to
the
second
pulse
started
with
an
inhibition,
but
then
elicited
a
small
EPSP
lasting
15
ms
(Fig.
7,
arrowhead).
In
the
following
tests
with
PI
increasing
from
15
to
40
ms,
the
excitatory
response
to
the
second
pulse
considerably
increased
as
B-LI4
generated
suprathreshold
EPSPs
with
1-2
AP.
There
was
no
indication
of
membrane
potential
oscillation
even
after
the
strongest
response
to
the
second
pulse
at
a
PI
of
25
ms
(1.2
±
0.5
AP/pulse).
At
a
PI
of
50
ms,
the
second
sound
pulse
only
caused
a
weak
EPSP.
At
PI-60
ms,
B-LI4
responded
to
the
second
sound
pulse
again
with
an
inhibition
and
a
consecutive
subthreshold
EPSP,
in
the
same
way
as
it
responded
to
the
first
pulse
of
the
stimulus
pair.
In
the
recordings,
we
obtained
a
strong
excitatory
response
to
the
second
pulse
at
PI-15
to
40
ms.
When
tested
with
different
PI,
the
neurons
PI-response
function
was
maximal
at
20
ms
(Fig.
4D).
Temporal
selectivity
as
measured
over
a
whole
chirp
may,
however,
be
sharpened
when
several
sound
pulses
with
the
according
PI
are
presented.
This
test
clearly
demonstrated
that
the
selective
response
properties
of
interneu-
ron
B-LI4
for
specific
PI
depend
on
the
time
interval
between
two
consecutive
pulses
due
to
an
interaction
of
inhibitory
and
excit-
atory
PSPs.
Discussion
Temporal
tuning
of
cricket
phonotaxis
Phonotaxis
in
female
crickets
(G.
bimaculatus,
G.
campestris)
is
tuned
to
the
temporal
features
of
the
male
calling
song
and
shows
bandpass
characteristics
when
tested
with
different
pulse
periods
(Popov
and
Shuvalov,
1977;
Thorson
et
al.,
1982;
Doherty,
1985a;
Hedwig,
2006).
The
behavior
is
additionally
affected
by
variation
in
pulse
interval,
pulse
duration,
and
also
pulse
number
and
chirp
interval
(Doherty,
1985a;
Stout
and
McGhee,
1988;
Hennig,
2009).
With
a
trackball
system
(Hedwig
and
Poulet,
2004),
we
recorded
the
characteristic
tuning
of
female
phonotaxis
to
varia-
tions
of
pulse
patterns.
Phonotaxis
was
strongest
at
15
ms
pulse
duration,
15
ms
pulse
interval,
and
34-42
ms
pulse
period.
These
A
230
ms
21
31
41
ms
ms
ms
71
ms
21
ms
60
ms
B-LI4
50
ms
TH1-AC1
120
mV
40
ms
35
ms
N=40
sequence
2
AP=45
6
400
AP/bin
0
AP/s
30
ms
200
25
ms
5
mV
1
0
20
ms
B
15
ms
B-LC3
15
mV
10
ms
5
ms
N=10
sequences
200
••••••••,.
AP=9.9
P1=5-60
ms
N=4
AP/bin
AP/s
N=16
0
100
200
400
Ti
me
[ms]
Figure
7.
Responses
of
TH1-AC1,
B-LC3,
and
B-LI4
to
different
PI
that
were
varied
within
a
sequence
of
sound
pulses
or
pairs
of
pulses;
PD
kept
constant
at
21
ms.
A,
Recording
of
TH1-AC1
(top)
and
PSTH
(4
ms/bin)
with
averaged
instantaneous
spike
frequency
(bottom;
N
=
1
female,
n
=
40
sequences).
B,
Recording
of
B-LC3
(top)
and
NTH
(4
ms/bin)
with
averaged
instanta-
neous
spike
frequency
(bottom;
N
=
1
female,
n
=
10
sequences).
C,
Averaged
synaptic
activity
of
B-LI4
to
single
pulses
(bottom)
and
pairs
of
sound
pulses
with
PI
ranging
from
5
to
60
ms
(N
=
1
female,
n
=
16
for
single
pulses,
and
n
=
4
for
pairs
of
pulses).
0
50
100
150
Time
[ms]
9610
J.
Neurosci.,
July
11,
2012
32(28):9601-9612
Kostarakos
and
Hedwig
Pattern
Recognition
in
the
(ticket
Brain
parameters
fall
well
within
the
range
of
the
male
calling
song
parameters
(Doherty,
1985b;
Ferreira
and
Ferguson,
2002;
Ver-
burgt
et
al.,
2010).
Structure
of
local
auditory
neurons
within
the
brain
The
prothoracic
ascending
neuron
TH1-
AC1
is
the
only
neuron
that
forwards
calling-song-specific
auditory
informa-
tion
to
the
brain
(Wohlers
and
Huber,
1982;
Schildberger,
1984).
In
the
anterior
protocerebrum,
its
two
main
axonal
pro-
jections
run
anteriorly
and
posteriorly
around
an
unidentified
structure
and
form
a
dense
ring-like
arborization.
We
identified
local
brain
neurons
(B-LI2,
B-LI3,
and
B-LI4)
with
a
lateral
cell
body
and
a
similar
ring-like
pattern
of
arboriza-
tions
that
overlap
the
projections
of
TH1-
AC1.
These
neurons
showed
no
clear
separation
of
dendritic
and
axonal
branches,
which
is
typical
for
local
sensory
interneurons
(Burrows,
1992).
Structural
characteristics
alone
made
distinguishing
between
B-LI3
and
B-LI4
difficult,
but
they
revealed
very
different
physiological
properties,
particularly
in
the
degree
of
inhibition
resulting
from
sound
pulses.
B-LC3
connected
both
auditory
areas
at
the
ante-
rior
protocerebrum.
Its
ipsilateral
branches
exhibited
a
smooth
dendritic
appearance,
and
its
contralateral
projections,
a
beaded
axonal
structure
(Peters
et
al.,
1986).
Bilateral
auditory
neuropils
are
also
linked
in
the
brain
of
bushcrickets
(Ostrowski,
2009)
and
locusts
(Boyan
et
al.,
1993),
a
feature
that
may
support
pattern
recognition
and
directional
processing.
Other
identified
auditory
brain
neurons
also
showed
dense
branching
patterns
overlapping
with
the
ring-like
arborization
area
(Schildberger,
1984,
1985).
Therefore,
we
consider
that
this
area
is
a
crucial
early
stage
for
auditory
processing
in
the
cricket
brain.
Temporal
selectivity:
brain
neurons
and
phonotactic
behavior
When
we
compared
the
tuning
of
phonotactic
behavior
with
that
of
auditory
neurons,
the
response
functions
of
TH1-AC1
re-
vealed
no
temporal
selectivity
(Fig.
2);
its
spike
activity
forwarded
any
pattern
of
acoustic
stimuli
to
the
brain.
Local
brain
neuron
B-LI2
also
showed
no
selectivity
(Figs.
3-5),
and
as
its
response
functions
were
very
similar
to
TH1-AC1,
its
activity
might
be
directly
driven
by
the
ascending
interneuron.
Three
auditory
brain
neurons
(B-LC3,
B-LI3,
and
B-LI4)
re-
vealed
a
clear
temporal
selectivity
that
matched
the
tuning
of
phonotaxis
to
different
degrees
(Figs.
3-5).
The
responses
of
B-LI3
and
B-LC3
were
quite
similar.
When
tested
with
a
chirp
pattern
akin
to
calling
song,
both
neurons
had
a
stronger
re-
sponse
to
the
second
pulse
of
the
chirp,
showing
that
they
re-
ceived
a
more
complex
input
than
just
a
reflection
of
the
ascending
spike
pattern.
When
>4
pulses/chirp
were
presented
to
B-LI3
and
B-LC3
(Fig.
5B,
C),
the
response
toward
the
sound
pulses
over
a
chirp
decreased
to
a
stronger
degree
than
expected
based
on
the
ascending
activity
(Fig.
2C).
Although
these
results
indicate
the
involvement
of
inhibitory
processing,
IPSPs
were
not
obvious
in
these
interneurons.
The
response
optima
and
the
shape
of
the
neural
response
functions
matched
the
behavior;
however,
away
from
the
optimum,
the
minimum
response
was
often
>50%.
Response
functions
were
quite
different
for
neuron
B-LI4,
which
demonstrated
the
highest
temporal
selectivity
and
best
match
to
phonotaxis
(Figs.
3-6).
B-LI4
generated
pronounced
IPSPs
at
the
beginning
of
each
response
to
a
chirp.
The
inhibitory
inputs
dominated
when
a
chirp
was
clearly
different
from
the
calling
song
pattern,
reducing
its
spike
activity
to
very
low
levels
(Figs.
3D-5D).
Thus,
the
maximum
responses
were
always
well
above
background
activity,
and
all
of
the
response
functions
of
B-LI4
exhibited
a
close
match
to
the
tuning
of
phonotaxis.
This
revealed
that
bandpass-like
selectivity
for
auditory
pulse
patterns
as
exhibited
by
phonotactic
behavior
is
already
established
within
the
ring-like
arborization
area
in
the
anterior
protocerebrum.
Every
brain
neuron
we
analyzed
exhibited
a
clear
phasic
response
to
individual
sound
pulses
when
exposed
to
a
calling-song-like
pattern.
In
relation
to
the
level
of
temporal
filtering,
however,
the
number
of
AP/chirp
continuously
decreased
from
TH1-AC1
to
B-LI2
to
B-LC3
to
B-LI3
to
reach
nearly
a
90%
reduction
in
B-LI4.
This
decrease
of
spike
activity
is
in
line
with
concepts
on
sparse
coding
(Olshausen
and
Field,
2004).
It
shifts
the
represen-
tation
of
stimulus
features
from
a
temporal
activity-based
code
to
a
neuron-specific
place
code
and
appears
to
be
an
energetically
efficient
way
for
simple
networks
to
ensure
a
robust
representa-
tion
of
stimulus
patterns.
Neural
mechanisms
underlying
temporal
selectivity
Different
concepts
have
been
proposed
to
explain
the
selectivity
of
cricket
phonotaxis.
Hoy
(1978),
Weber
and
Thorson
(1989),
and
Hennig
(2003)
suggested
that
temporal
filtering
might
de-
pend
on
"feature
detection"
when
the
temporal
pattern
of
the
signal
coincides
with
a
form
of
template
matching
or
cross-
correlation.
As
B-LI4
revealed
a
strong
selectivity
in
its
response,
it
may
be
described
as
a
"feature
detector"
for
the
calling
song,
considering,
however,
that
inhibitory
and
excitatory
inputs
from
the
presynaptic
auditory
network
shape
its
activity.
As
the
neuron
showed
a
tuning
to
PD,
even
when
the
pulse
period
was
kept
constant,
its
tuning
does
not
appear
to
depend
on
the
pulse
rate
but
rather
on
combinations
of
PD
and
PI.
Furthermore,
a
pulse
Kostarakos
and
Hedwig
Pattern
Recognition
in
the
Cricket
Brain
J.
Neurosci.,
July
11,2012
32(28):9601-9612
9611
coincidence
detector
was
suggested,
based
on
autocorrelation
via
a
delay
line
adjusted
to
the
species-specific
pulse
interval
(Reiss,
1964;
Weber
and
Thorson,
1989).
Anatomical
substrates
for
40
ms
delay
lines
are
unlikely
in
insect
brains;
however,
delays
may
be
achieved
by
inhibitory
processing
as
evident
in
B-LI4.
Evidence
from
Bush
and
Schul
(2006)
indicated
that
pattern
selectivity
may
result
from
intrinsic
membrane
potential
oscilla-
tions
of
auditory
neurons
resonating
with
the
frequency
of
the
species-specific
pulse
pattern.
Our
recordings
did
not
provide
evidence
for
resonance
in
the
auditory
network
as
stimulation
with
calling
song
patterns
did
not
lead
to
ongoing
oscillations
of
the
membrane
potential
of
any
of
the
neurons
recorded.
Further-
more,
neither
our
behavioral
data
nor
the
response
functions
of
our
neurons
revealed
a
secondary
peak
at
one-half
of
the
optimal
pulse
period
as
suggested
by
Bush
and
Schul
(2006).
Only
the
membrane
potential
of
B-LC3
in
some
preparations
transiently
oscillated
at
—34
Hz
when
stimulated
with
a
constant
tone
of
160
ms
(Fig.
3B,
top),
maybe
reflecting
feedback
loops
of
the
network
tuned to
the
temporal
features
of
the
calling
song.
Schildberger
(1984)
suggested
that
temporal
selectivity
un-
derlying
phonotaxis
results
from
low-pass
and
high-pass
filter
neurons
that
together
shape
a
bandpass
response
in
other
brain
neurons,
a
concept
akin
to
bandpass
responses
encountered
in
the
anuran
auditory
pathway
(Rose
and
Capranica,
1983).
Band-
pass
responses
via
low-
and
high-pass
filtering
require
at
least
two
sound
pulses
and
long
latency
processing.
Phonotactic
orienta-
tion,
however,
works
much
faster
and
can
occur
toward
indivi-
dual
sound
pulses
(Hedwig
and
Poulet,
2004,
2005).
Here,
we
show
that
selective
processing
in
brain
neurons
requires
only
one
specific
pulse
interval
to
elicit
an
enhanced
response
to
the
con-
secutive
sound
pulse
(Fig.
7);
a
response
property
that
indicates
a
change
in
auditory
responsiveness
over
time,
which
is
very
diffe-
rent
from
bandpass
filtering
by
low-pass
and
high-pass
neurons.
Thus,
pattern
recognition
seems
to
be
tightly
linked
to
specific
changes
in
neural
responsiveness
occurring
over
the
duration
of
a
single
pulse
interval.
Considering
the
synaptic
activity
of
B-LI4
(Fig.
7C),
which
exhibited
clear
bandpass
properties
(Figs.
3D-
5D),
we
propose
that
species-specific
pattern
recognition
in
the
brain
of
G.
bimaculatus
is
based
on
fast
pulse-by-pulse
interac-
tions
of
inhibitory
and
excitatory
synaptic
activity
in
local
brain
neurons.
Neuron
B-LI4
revealed
phasic
responses
to
individual
sound
pulses
and
may
only
be
a
few
synapses
after
TH1-AC1,
whereas
the
bandpass
neuron
described
by
Schildberger
(1984)
generated
a
rather
tonic
pattern
of
spikes
over
a
chirp
and
did
not
overlap
with
the
ring-like
arborizations,
indicating
that
those
bandpass
responses
resulted
from
preceding
auditory
filtering.
Concepts
for
temporal
processing
in
lower
vertebrates
high-
light
the
importance
of
inhibitory
inputs
for
interval
selectivity
and
of
delays
established
by
inhibition
lasting
longer
than
the
duration
of
single
sound
pulses.
A
model
by
Large
and
Crawford
(2002)
demonstrated
that
temporal
selectivity
in
the
fish
Pol-
limyrus
could
be
facilitated
by
the
timing
of
excitatory
inputs
that
coincide
with
an
intrinsic
postinhibitory
rebound
excitation.
The
importance
of
the
interaction
between
inhibition
and
pulse
rate-
dependent
excitation
for
interval
selectivity
has
been
shown
in
anurans
(Edwards
et
al.,
2007;
Rose
et
al.,
2011).
"Interval-
counting"
neurons
responded
with
spikes
only
after
some
pulse
intervals
with
a
specific
pulse
repetition
rate
due
to
a
gradual
decrease
of
the
inhibitory
input
and
thus
increasing
excitation.
Like
in
the
cricket,
these
responses
in
lower
vertebrates
support
a
model
based
on
integration
of
EPSPs
and
IPSPs
in
single
neurons
(Buonomano,
2000)
to
which
postinhibitory
rebound
mecha-
nisms
may
contribute.
References
Boyan
G,
Williams
L,
Meier
T
(1993)
Organization
of
the
commissural
fi-
bers
in
the
adult
brain
of
the
locust.
J
Comp
Neurol
332:358
-377.
Bradbury
JW,
Vehrenkamp
SL
(1998)
Principles
of
animal
communication.
Sunderland,
MA:
Sinauer
Associates.
Brodfuehrer
PD,
Hoy
RR
(1990)
Ultrasound
sensitive
neurons
in
the
cricket
brain.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
166:651-662.
Buonomano
DV
(2000)
Decoding
temporal
information:
a
model
based
on
short-term
synaptic
plasticity.
J
Neurosci
20:1129
-1141.
Burrows
M
(1992)
Local
circuits
for
the
control
of
leg
movements
in
an
insect.
Trends
Neurosci
15:226
-232.
Bush
SL,
Schul
J
(2006)
Pulse-rate
recognition
in
an
insect:
evidence
of
a
role
for
oscillatory
neurons.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
192:113-121.
Crawford
JD
(1997)
Feature-detecting
auditory
neurons
in
the
brain
of
a
sound-producing
fish.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
180:439
-450.
Doherty
JA
(1985a)
Trade-off
phenomena
in
calling
song
recognition
and
phonotaxis
in
the
cricket
Gryllus
bimaculatus
(Orthoptera,
Gryllidae).
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
156:787-801.
Doherty
JA
(1985b)
Temperature
coupling
and
"trade
off'
phenomena
in
the
acoustic
communication
system
of
the
cricket,
Gryllus
bimaculatus
De
Geer.
J
Exp
Biol
114:17-35.
Edwards
CJ,
Leary
CJ,
Rose
GJ
(2007)
Counting
on
inhibition
and
rate-
dependent
excitation
in
the
auditory
system.
J
Neurosci
27:13384-13392.
Endler
JA,
Basolo
AL
(1998)
Sensory
ecology,
receiver
biases
and
sexual
selection.
Tree
13:415-420.
Fay
RR
(1992)
Structure
and
function
in
sound
discrimination
among
ver-
tebrates.
In:
The
evolutionary
biology
of
hearing
(Webster
D,
Fay
RR,
Popper
AN,
eds),
pp
229
-263.
Springer,
New
York.
Ferreira
M,
Ferguson
JWH
(2002)
Geographic
variation
in
the
calling
song
of
the
field
cricket
Gryllus
bimaculatus
(Orthoptera:
Gryllidae)
and
its
relevance
to
mate
recognition
and
mate
choice.
J
Zool
Lond
257:163-170.
Gerhardt
HC,
Huber
F
(2002)
Acoustic
communication
in
insects
and
an-
urans.
Chicago:
The
University
of
Chicago.
Hedwig
B
(1986)
On
the
role
in
stridulation
of
plurisegmental
interneurons
of
the
acridid
grasshopper
Omocestus
viridulus
L.
I.
Anatomy
and
physi-
ology
of
descending
cephalothoracic
interneurons.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
158:413-427.
Hedwig
B
(2006)
Pulses,
patterns
and
paths:
neurobiology
of
acoustic
be-
havior
in
crickets.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
192:677-689.
Hedwig
B,
Knepper
M
(1992)
NEUROLAB,
a
comprehensive
program
for
the
analysis
of
neurophysiological
and
behavioral
data.
J
Neurosci
Meth-
ods
45:135-148.
Hedwig
B,
Poulet
JF
(2004)
Complex
auditory
behavior
emerges
from
sim-
ple
reactive
steering.
Nature
430:781-785.
Hedwig
B,
Poulet
JF
(2005)
Mechanisms
underlying
phonotactic
steering
in
the
cricket
Gryllus
bimaculatus
revealed
with
a
fast
trackball
system.
J
Exp
Biol
208:915-927.
Hennig
RM
(2003)
Acoustic
feature
extraction
by
cross-correlation
in
crickets?
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
189:589
-598.
Hennig
RM
(2009)
Walking
in
Fourier's
space:
algorithms
for
the
compu-
tation
of
periodicities
in
song
patterns
by
the
cricket
Gryllus
bimaculatus.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
195:971-987.
Hoy
RR
(1978)
Acoustic
communication
in
crickets:
a
model
system
for
the
study
of
feature
detection.
Fed
Proc
37:2316-2323.
Knepper
M,
Hedwig
B
(1997)
NEUROLAB,
a
PC-program
for
the
pro-
cessing
of
neurobiological
data.
Comput
Methods
Programs
Biomed
52:75-77.
Large
EW,
Crawford
JD
(2002)
Auditory
temporal
computation:
interval
selectivity
based
on
post-inhibitory
rebound.
J
Comput
Neurosci
13:125-142.
Nabatiyan
A,
Poulet
JF,
de
Polavieja
GG,
Hedwig
B
(2003)
Temporal
pat-
tern
recognition
based
on
instantaneous
spike
rate
coding
in
a
simple
auditory
system.
J
Neurophysiol
90:2484
-2493.
Olshausen
BA,
Field
DJ
(2004)
Sparse
coding
of
sensory
inputs.
Curr
Opin
Neurobiol
14:481-487.
Ostrowski
TD
(2009)
Filtering
of
species-specific
song
parameters
via
in-
9612
J.
Neurosci.,
July11,2012
32(28):9601-9612
Kostarakos
and
Hedwig
Pattern
Recognition
in
the
(ticket
Brain
terneurons
in
a
bush
cricket's
brain.
Dissertation,
Universitaet
Goettin-
gen,
Goettingen,
Germany.
Peters
BH,
Romer
H,
Marquart
V
(1986)
Spatial
segregation
of
synaptic
inputs
and
outputs
in
a
locust
auditory
interneurone.
J
Comp
Neurol
254:34-50.
Pollack
G
(2000)
Who,
what,
where?
Recognition
and
localization
of
acous-
tic
signals
by
insects.
Curr
Opin
Neurobiol
10:763-767.
Pollack
GS,
Hoy
RR
(1979)
Temporal
pattern
as
a
cue
for
species-specific
calling
song
recognition
in
crickets.
Science
204:429-432.
Popov
AV,
Shuvalov
VF
(1977)
Phonotactic
behavior
of
crickets.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
119:111-126.
Poulet
JF,
Hedwig
B
(2005)
Auditory
orientation
in
crickets:
pattern
rec-
ognition
controls
reactive
steering.
Proc
Natl
Acad
Sci
U
S
A
102:15665-15669.
Reiss
RF
(1964)
A
theory
of
resonance.
In:
Neural
theory
and
modelling
(Reiss
RF,
ed),
pp
105-137.
Stanford,
CA:
Stanford
UP.
Rose
G,
Capranica
RR
(1983)
Temporal
selectivity
in
the
central
auditory
system
of
the
Leopard
frog.
Science
219:1087-1089.
Rose
GJ,
Leary
CJ,
Edwards
CJ
(2011)
Interval-counting
neurons
in
the
an-
uran
auditory
midbrain:
factors
underlying
diversity
of
interval
tuning.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
197:97-108.
Ryan
MJ
(1998)
Sexual
selection,
receiver
biases,
and
the
evolution
of
sex
differences.
Science
281:1999-2003.
Schildberger
K
(1984)
Temporal
selectivity
of
identified
auditory
neurons
in
the
cricket
brain.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
155:171-185.
Schildberger
K
(1985)
Recognition
of
temporal
patterns
by
identified
audi-
tory
neurons
in
the
cricket
brain.
In:
Acoustic
and
vibrational
communi-
cation
in
insects
(Kalmring
K,
Eisner
N,
eds),
pp
41-49.
Hamburg,
Germany:
Paul-Parey
Verlag.
Schildberger
K,
Hoerner
M
(1988)
The
function
of
auditory
neurons
in
cricket
phonotaxis:
I.
Influence
of
hyperpolarization
of
identified
neurons
on
sound
localization.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
163:621-631.
Stout
J,
McGhee
R
(1988)
Attractiveness
of
the
male
Acheta
domesticus
call-
ing
song
to
females.
II.
The
relative
importance
of
syllable
period,
inten-
sity
and
chirp
rate.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
164:277-287.
Thorson
J,
Weber
T,
Huber
F
(1982)
Auditory
behaviour
of
the
cricket.
II.
Simplicity
of
calling-song
recognition
in
Gryllus,
and
anomalous
phono-
taxis
at
abnormal
carrier
frequencies.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
146:361-378.
Verburgt
L,
Ferguson
JW,
Weber
T
(2008)
Phonotactic
response
of
female
crickets
on
the
Kramer
treadmill:
methodology,
sensory
and
behavioural
implications.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
194:79-96.
Verburgt
L,
Ferreira
M,
Ferguson
JWH
(2010)
Male
field
cricket
song
re-
flects
age,
allowing
females
to
prefer
young
males.
Anim
Behav
81:19-29.
Weber
T,
Thorson
J
(1989)
Phonotactic
behavior
of
walking
crickets.
In:
Cricket
behaviour
and
neurobiology
(Huber
F,
Moore
TE,
Loher
W,
eds),
pp
310-339.
Ithaca,
NY:
Cornell
UP.
Wohlers
DW,
Huber
F
(1982)
Processing
of
sound
signals
by
six
types
of
neurons
in
the
prothoracic
ganglion
of
the
cricket,
Gryllus
campestris
L.
J
Comp
Physiol
A
Neuroethol
Sens
Neural
Behav
Physiol
146:161-173.
Yu
AC,
Margoliash
D
(1996)
Temporal
hierarchical
control
of
singing
in
birds.
Science
273:1871-1875.
Zorovie
M,
Hedwig
B
(2011)
Processing
of
species-specific
auditory
pat-
terns
in
the
cricket
brain
by
ascending,
local
and
descending
neurons
during
standing
and
walking.
J
Neurophysiol
105:2181-2194.