Results 2008
Results of the spike time prediction challenge 2008
The results have also been published in Biological Cybernetics
Challenge A

In
the above table we see the ranking of participants along with their
average performance (middle column) and bootstrap estimation of
standard deviation (right column). There were 3 statistically
equivalent solutions:
- Shinomoto & Kobayashi (Autoregressive exogenous model with moving threshold),
- Badel (Exponential integrate-and-fire with dynamic threshold),
- Mensi (spike response model with moving threshold)).
The winner submission is decided according to the best average performance:
2nd prize : Shigeru Shinomoto.
You can download the ‘answers’ of the test set here: V.txt
Challenge B
Split 2nd prize: Tie between Kramer (hand tuned
Traub model), Hirschi and Naud (adaptive Exponential
Integrate-and-Fire), Acker (Izhikevich’s simple model) and Druckmann
(Hodgkin and Huxley type model tuned with genetic algorithm).
This
challenge was difficult to win since the winning solution had to
achieve the best cost for each feature of the test set. To
display the results we have kept the best submission of each
participant. We can make a global ranking using the criteria of
last year’s challenge (http://icwww.epfl.ch/~gerstner//QuantNeuronMod2007/). Briefly, we take C to be the sum over i of ci*(0.5)i with ci being the cost associated with each feature in decreasing order of cost (c1 is the cost of the worse feature, c2 the second worse, etc.). With this ranking scheme, the results are:
1. C = 9.5 Hirschi - Naud (adaptive Exponential Integrate and Fire)
2. C = 11 Druckmann (Hodgkin-Huxley type model)
3. C = 38 Acker (Izhikevich’s simple model)
4. C = 112 Kramer (Traub model)
You can download the test set ‘answers’ here: ChallBTestV.txt.
Challenge C:
1.
Ryota
Kobayashi
C1 = 0.499 C2 = 0.427
2.
Shigeru Shinomoto
C1 = 0.476 C2 = 0.379
3.
Richard
Naud
C1 = 0.309 C2 = 0.168
Challenge D:
1.
Richard
Naud D1
= 0.408 D2 = 0.446
Ryota Kobayashi wins Challenge C, Richard Naud wins Challenge D.

