Battaglia, Demian

Douchamps, Vincent

Goutagny, Romain

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Submitted on 21 Nov 2019
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ARE THERE DISCRETE GAMMA SUB-BANDS IN
HIPPOCAMPAL NETWORKS DURING SPATIAL
LEARNING?
Vincent Douchamps, Demian Battaglia, Romain Goutagny
To cite this version:
Vincent Douchamps, Demian Battaglia, Romain Goutagny. ARE THERE DISCRETE GAMMA
SUB-BANDS IN HIPPOCAMPAL NETWORKS DURING SPATIAL LEARNING?. Society for
Neuroscience annual meeting, Oct 2019, Chicago, United States. ￿hal-02373798￿
Vincent Douchamps1, Demian Battaglia2, Romain Goutagny1
1Laboratoire de Neurosciences Cognitives et Adaptatives, CNRS UMR 7364, Université de Strasbourg;  2Institut de Neurosciences des Systèmes, INSERM UMR 1106, Aix-Marseille Université
Are there discrete gamma sub-bands
in hippocampus during spatial learning?
Institut de
Neurosciences des
Systèmes
Fonds Paul Mandel pour les neurosciences
Fédération Neuropôle
Université de Strasbourg
Power across learningIntroduction
Theta (θ) and gamma (γ) oscillations are believed to 
organise hippocampal activity, via their cross-freq coupling 
Classic view: Slow γ (~30-60 Hz), possibly related to memory 
retrieval; and Medium γ (~60-100 Hz), possibly related to 
sensory infomation and memory encoding
However, recent evidence suggests a wider repertoire of 
coupling patterns when considering individual θ cycles
Schomburg et al., 2014
AIM OF THE STUDY
Explore theta-gamma oscillatory dynamics 
in CA1 and DG across learning in an ecologic 
spatial navigation task and without a priori 
on frequencies and phases that should be observed.
The Arm-to-Arm task
Subjects: water-restricted mice (n=4)
Maze: 8-arm radial arm maze; distal 
visual cues available
 
Task: finding the rewarded arm starting 
from another (semi-random) arm. 
Fixed reward arm across the 10-day 
training, 4 daily trials
Reward: 0.05 ml of water at last arm end
Day 1 - Trial 1 Day 4 - Trial 4
0
50
100
Ti
m
e 
to
 re
ac
h 
ta
rg
et
 (s
)
Learning day
1 1098765432
F(9,63) = 7.956
p < 0.0001
target-90 opposite0
0.2
0.4
0.6
R
at
io
 o
f v
is
its
 in
 a
rm
+90
* *
*
*
F(7,49) = 12.38
p < 0.0001
Identification of θ cycles: preservation of θ 
asymmetry (1-25 Hz filtering); phase estimation with 
piece-wise linear interpolation
γ spectral content of each θ cycle: coincident seg-
ment of the spectrogram of γ-composite signal 
(complex Morlet wavelets; 1-200 Hz; 1 Hz steps)
Mean θ-γ motifs from repre-
sentative channels and trials do 
correspond to the “classical 
model” 
However, the possibility of multi-
ple γ bouts per θ cycle is not 
“classical”, as well as the pres-
ence of both medium and slow 
γ episodes in both dendritic 
layers 
The large variability of individ-
ual cycles and the broad count 
distributions suggest that the 
landscape of possible θ-γ cou-
plings is better described as a 
structured continuum
270
90
0 180
90
0 180
90
0 180
At all stages of learning and all anatomical 
layers, cycles arehighly variable, but phase, freq 
and power are not uniformly distributed
The “swarms” of cycles morph along 
learning (e.g. migration of hi power SLM cycles 
toward higher frequencies)
Yet, despite complexity, non-trivial 
coding of navigation speed or location?
We trained classifiers (Random forests, via RUSBoost 
method) to predict speed and maze location as a 
function of individual cycle properties
Decoding is possible, revealing that different 
inputs convey different information at different 
times (but not simply “recall” or “encoding”!)
Raw LFP (hippocampal fissure)
Unsupervised EEMD decomposition
Unsupervised empirical ensemble mode decomposition (EEMD for cycle-by-cycle analy-
sis [Wu & Huang, 2009]; CEEMD for power spectrum [Torres et al., 2011])
Power spectra of EEMD-derived IMFs is stable along training
Need to correct for theta harmonics!
SLM spectra
n.s.
n.s.
n.s.
θ cycle-by-cycle analyses
Peak-to-peak segmentation in θ cycles (ref. in LFP from hippocampal fissure)
θ-γ coupling: means vs counts 
20
40
60
80
100
120
140
160
180
200
0
-pi 0 pi
Fr
eq
ue
nc
y 
(H
z) LFP pow
er
max
min
SP
20
40
60
80
100
120
140
160
180
200
0
Fr
eq
ue
nc
y 
(H
z)
20
40
60
80
100
120
140
160
180
200
0
Fr
eq
ue
nc
y 
(H
z)
Theta phase (°)
Hippocampal fissure
C
SD
 am
plitude
min
max
SR
SLM
A
rb
itr
ar
y 
un
its
Mean
Count
Frequency (Hz)
Mean
Count
Mean
Count
0 100 15050
-π 0 π
Theta phase (°)
Hippocampal fissure
15
85
160
Fr
eq
ue
nc
y 
(H
z)
-π 0 π
Theta phase (°)
Hippocampal fissure
-π 0 π
Theta phase (°)
Hippocampal fissure
15
85
160
Fr
eq
ue
nc
y 
(H
z)
15
85
160
Fr
eq
ue
nc
y 
(H
z)
INDIVIDUAL CYCLESMEAN θ CYCLES Mean vs Count
Daunting variability and deviations from “prototypical” case (usually, only ~2% of cycles are similar to mean template) 
40 80 120
SLM
Frequency (Hz)
SP
C
yc
le
 c
ou
nt
s 
(a
.u
.)
SR
CYCLE COUNTS (all trials and channels)
LO and HI power
together
LO power only
HI power only
A structured continuum of cycles... 
180
-90
0
90
40
60
80
100
120
40
60
80
100
120
40
60
80
100
40
60
80
100
120
0
40
60
80
100
120
40
60
80
100
120
40
60
80
100
120
40
60
80
100
120
40
60
80
100
40
60
80
100
120
40
60
80
100
120
40
60
80
100
120
SO
Early Late
SP
Early Late
SR
Early Late
SLM
Early Late
DG mol
Early Late
DG gran
Early Late
CENTER:
low freq cycles
PERIPHERY:
hi freq cycles
One dot = 
one θ/γ event
Cycle peak power
5% quantile 95% quantile
Phase
30 Hz
120 Hz
Frequency
e.g., in SLM, 
cycles occurring during
high-speed epochs
tend to cluster
in patches in this 
dimensionally-
reduced projection of
all SLM cycles 
... which conveys decodable information
Trial group Early Late Trial group Early Late
SPEED PREDICTION TARGET ARMPREDICTION
C
or
re
la
tio
n 
be
tw
ee
n 
pr
ed
ic
te
d 
an
d 
re
al
 s
pe
ed
0.5
0.3
0.62
0.70
0.78
A
re
a 
un
de
r R
O
C
 c
ur
ve
 (A
U
C
)
SLM
SR
SR confusion matrix
(% of classifications)
Other
Target
arm
Reward
O
th
er
Ta
rg
et
ar
m
R
ew
ar
d
0% 100%
50%
60%
82%
REPRESENTATIONAL
SIMILARITY
Tr
ia
l g
ro
up
 
Early
Late
SLM predicts speed
Tr
ia
l g
ro
up
 
Early
Late
SLM predicts target arm
Trial group 
Ea
rly
La
te
low high
Cross-classification
performance
pre
dic
ts


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ARE THERE DISCRETE GAMMA SUB-BANDS IN HIPPOCAMPAL NETWORKS DURING SPATIAL LEARNING ?

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ARE THERE DISCRETE GAMMA SUB-BANDS IN HIPPOCAMPAL NETWORKS DURING SPATIAL LEARNING ?

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