1. Positioning rat using neuronal activity in hippocampus
Tambet Matiisen
Deep Learning for NLP seminar

2. Place cells
In year 2014 The Nobel Prize in Physiology or Medicine was given to John O’Keefe, May-Britt Moser and Edvard I. Moser for discovering “place cells” in a rat brain.

3. Dataset Rat recordings from University College London
1m x 1m arena for rat to run around.
Number of spikes for 172 neurons.
20ms time resolution, measurements.

5. Features In total 5400 datapoints with 344 features. 1s (x,y) 1s (x,y)
200ms
200ms
In total 5400 datapoints with 344 features.

6. Baseline
Algorithm
Train mean distance
Validation mean distance
Linear regression
13.27cm
20.85cm
K nearest neighbors
0cm
24.05cm
Random forest
2.47cm
16.02cm
Extremely randomized trees
15.32cm
Gradient boosted trees
1.45cm
15.30cm
Linear SVM
13.04cm
21.09cm
Polynomial SVM
14.25cm
18.77cm
Radial basis SVM
0.16cm
15.66cm
Used 4000/1400 train-test split and grid search for hyperparameter optimization.

8. Recurrent Neural Networks
RNN
Bi-directional RNN

9. Long Short-Term Memory

10. Reference Network Architecture
1 LSTM layer (+ linear fully connected layer),
1024 hidden nodes,
dropout 0.5,
sequence length 100,
batch size 1,
RMSProp,
early stopping.
10.39cm validation error (5-fold crossvalidation).

11. Number of layers Hidden nodes Experiment Train mean distance
Validation mean distance
1 layer
5.05cm
10.39cm
2 layers
5.63cm
10.61cm
3 layers
12.13cm
16.99cm
Hidden nodes
Experiment
Train mean distance
Validation mean distance
Hidden 512
4.56cm
10.60cm
Hidden 1024
5.05cm
10.39cm
Hidden 2048
6.27cm
10.97cm

12. Dropout Experiment Train mean distance Validation mean distance
5.08cm
10.30cm
Dropout 0.2
5.43cm
10.62cm
Dropout 0.5
5.05cm
10.39cm

13. Sequence length Batch size Experiment Train mean distance
Validation mean distance
Seqlen 10
7.81cm
13.65cm
Seqlen 100
5.05cm
10.39cm
Seqlen 200
5.32cm
10.92cm
Batch size
Experiment
Train mean distance
Validation mean distance
Batch size 1
5.05cm
10.39cm
Batch size 5
4.59cm
10.50cm
Batch size 10
4.94cm
10.87cm

14. Direction Optimization method Experiment Train mean distance
Validation mean distance
Reference
5.05cm
10.39cm
Backwards
33.34cm
34.79cm
Bi-directional
4.69cm
10.51cm
Stateful
6.54cm
11.81cm
Optimization method
Experiment
Train mean distance
Validation mean distance
RMSProp
5.05cm
10.39cm
Adam
7.35cm
13.30cm
Momentum ?

16. Do place cells code future or past?

18. Are errors caused by less data?

19. Are errors caused by rat speed or direction it is facing?
Errors by speed
Errors by angle

20. Are errors bigger at the start of sequence?

21. Latest results Experiment Train mean distance Validation mean distance
LSTM 9-fold CV
5.24cm
10.19cm
Bi-LSTM 9-fold CV
4.58cm
9.86cm
LSTM 54-fold CV
8.42cm
9.84cm
Bi-LSTM 54-fold CV
6.86cm
9.65cm

22. Thanks!
Ardi Tampuu
Sander Tanni
Zurab Bzhalava
Jaan Aru
Raul Vicente