Synthesizing realistic neural population activity patterns using Generative Adversarial Networks Manuel Molano-Mazon1, Arno Onken1,2, Eugenio Piasini1,3 and Stefano Panzeri1 1. Laboratory of Neural Computation, Istituto Italiano di Tecnologia Rovereto, 38068 Rovereto, Italy. 2. University of Edinburgh, Edinburgh EH8 9AB, UK. 3. University of Pennsylvania, Philadelphia, PA 19104 • We use GANs to simulate the concerted activity of a population of neurons. • Spike-GAN generates spike trains that match the first- and second-order statistics of datasets of tens of neurons. • We apply Spike-GAN to a real dataset recorded from salamander retina and showed that it performs as well as state-of-the-art approaches. • We exploit a trained Spike-GAN to construct importance maps to detect the most relevant statistical structures present in a spike train. Summary Architecture The samples Fitting neural data from salamander retina Fitting the whole probability distribution Comparison with a multi-layer perceptron Generated Sample Example Samples correspond to the spiking activity of a population of N neurons represented as binary vectors of length T (N=8 and T=12ms in the example below). The architecture of Spike-GAN is adapted from the one proposed by Gulrajani et al. (WGAN-GP). Samples are transposed so as to input the neurons’ activities into different channels. The convolutional filters (red box) span thus all neurons but share weights across the time dimension. The architecture of the generator is the same as that of the critic, used in the opposite direction and with sigmoid units in the last layer. Furthermore, we applied the procedure described by Odena et al. to avoid the checkerboard effect. We simulated activity patterns for a small ‘population’ of 2 neurons during 12 ms and evaluated how well Spike-GAN fits the whole probability density function from which the patterns are drawn. Novel samples 55% | 56% Not in Underlying distribution 3.8% | 3.2% Underlying distribution (numerical probabilities) Training dataset Surrogate dataset Spike-GAN distribution Blue tones Red tones Nearest neighbor analysis We checked for signs of overfitting by plotting randomly selected generated samples together with their closest sample (in terms of L1 distance) in the training dataset. Spike-GAN samples Real samples Importance maps: We infer the most relevant features characterizing a given neural activity pattern by enquiring a trained critic: 1. Compute the output produced by the critic for that particular pattern. 2. Shuffle across time the spikes emitted by a neuron during a specific period of time and compute again the output of the critic. 3. The absolute difference between the two outputs gives the importance of shuffled spikes. 4. Multiply the masked original sample by the importance. 5. Sum up all resulting maps for all neurons and time periods to get the importance map. Hypothetical experiment: N repetitions of a behavioral task, where a mouse has to discriminate two different stimuli (vertical/horizontal stripes). By means of two-photon calcium imaging the activity of a population of V1 neurons in the visual cortex of the mouse is recorded in response to the two stimuli. 1. Goodfellow et al. NIPS 2014. 2. Gulrajani et al. NIPS 2017. 3. Odena et al. Distill 2016. 4. Marre et al. IST Austria 2014. 5. Tkacik et al. Plos Comp. Biol. 2014. 6. Lyamzin et al. Front. Comp. Neurosci. 2010. 7. Luczak et al. Nat. Rev. Neurosci. 2015. References critic critic - shuffle Importance of shuffled spikes x Finding relevant patterns of neural activity Acknowledgements This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 699829. We compared Spike-GAN with a generative adversarial network in which both the generator and the critic are a 4-layer multi-layer perceptron (MLP). (Goodfellow et al.; Gulrajani et al.) Packets: We tested the importance maps procedur