Me I am a neuroscientist with a background in Mathematics. I have experience in both computational and experimental neuroscience.

Short bio

I did my PhD at the Instituto de Neurociencias de Alicante, under the supervision of Dr. Miguel Maravall and Dr. Luis Martínez. During this period, I built a connectivity model based on data recorded from all three stages of the cat’s primary visual cortex (retina, thalamus and V1) (Martínez et al. 2014, Benjumeda et al. 2014). I also worked on the analysis of electrophysiological data obtained on the rat and mice somatosensory cortex (Alenda et al. 2010, Pitas et al. 2016), and collaborated with Prof. Oscar Marín’s laboratory (currently the director of the MRC Centre for Developmental Neurobiology at King’s College London) developing a computational model that described the distribution of the Cajal Retzius neurons during the first developmental stages of the mouse brain (Villar-Cerviño et al. 2013). Furthermore, I acquired experience in both obtaining and analyzing calcium-imaging data, mostly thanks to a 7-month stay in Prof. Jason Kerr’s laboratory in the Max Planck Institute in Tübingen, where I learnt to perform two-photon microscopy experiments in anaesthetized rats and to process and analyze the data I produced.

During my first postdoctoral position, in the laboratory of Prof. Rodrigo Quian-Quiroga at the Centre for Systems Neuroscience in Leicester (UK), I investigated the neural mechanisms involved in the encoding of concepts in the hippocampus of the mouse. To this end, I built a virtual reality set-up and developed a protocol to train mice in a two-alternative forced choice task.

In January 2015, I started my second postdoctoral position in Dr. Stefano Panzeri’s laboratory at the Istituto Italiano di Tecnologia in Rovereto. During this period, I first participated in a European project, VISUALIZE, analyzing stimulus-evoked activity from the retinal ganglion cells of the Axolotl salamander.

Marie Curie fellowship

In 2016 I was awarded with a Marie Curie fellowship. During my fellowship I have participated in three main projects.

First, I developed a method, the Information Jitter Derivative (IJD) method, that allows decomposing the mutual information encoded in the temporal structure of a spike train into the unique information contained in its different temporal scale components. I applied the IJD method to two datasets obtained from the retinal ganglion cells in the salamander retina and the trigeminal ganglion cells in the rat somatosensory system, and uncover the different temporal strategies these neurons use to encode different stimulus features (Molano-Mazon et al., SFN2017, Molano-Mazon et al., NIPS2016).

Second, I have tightly collaborated with Prof. Tommaso Fellin’s laboratory (at the Istituto Italiano di Tecnologia in Genoa) in the development of advanced methodologies for the processing and analysis of two-photon, calcium imaging data. In particular, I (together with Dr. Marco Brondi from Fellin’s lab) have developed a novel procedure, the Smart-line Scan method, to optimize the tradeoff between sampling rate and Signal to Noise Ratio achieved when imaging a population of neurons in layer 4 of the somatosensory cortex.

Finally, during the fellowship I have developed an interest for machine-learning methods and in particular the deep-learning technique Generative Adversarial Networks (GANs). Over the last year I have led a project in which I applied this methodology to develop the Spike-GAN software, that allows generating realistic spike train patterns as well as to investigating which are the most relevant features characterizing the probability distribution underlying a neural dataset. This work was accepted at the International Conference on Learning Representations (ICLR2018) (Molano-Mazon et al. 2018).

At the same time I have been able to maintain international collaborations which have also been fruitful (Martini et al. 2017). Furthermore, the fellowship has also allowed me to participate in several conferences (SFN2017, NIPS2016, Bernstein Conference 2016), workshops (ICCV2017 Generative Adversarial Workshop) and specialized courses (RegML 2018).

In summary, the Marie Curie fellowship has enabled me to reach a unique, highly topical and highly desirable profile as an expert in machine learning techniques applied to neuroscience.

References

  • M. Molano-Mazón, A. Onken, E. Piasini and S. Panzeri (2018) “Synthesizing realistic neural population activity patterns using Generative Adversarial Networks”. ICLR 2018.
  • M. Molano-Mazón, A. Onken, J. K. Liu, T. Gollisch, Diamond. M, H. Safaai and S. Panzeri (2018) Information Jitter Derivative Method: A Novel Approach to the Analysis of Multiplexed Neural Codes. SFN2017.
  • F. Martini, M. Molano-Mazón and M. Maravall (2017) “Interspersed Distribution of Selectivity to Kinematic Stimulus Features in Supragranular Layers of Mouse Barrel Cortex“.Cerebral Cortex.
  • M. Molano-Mazón, A. Onken, J. K. Liu, T. Gollisch, H. Safaai and S. Panzeri (2016) Information Jitter Derivative Method: A Novel Approach to the Analysis of Multiplexed Neural Codes. NIPS2016, Bits and Brain Workshop.
  • A. Pitas*, A. Albarracín*, M. Molano-Mazón* and M. Maravall. “Neuronal encoding of instantaneous stimulus properties in the barrel cortex “. Cerebral Cortex 2016. (*equal contribution).
  • L. M. Martinez*, M. Molano-Mazón*, X. Wang F. T. Sommer and J. A. Hirsch (2014) ”Statistical wiring of thalamic receptive fields optimizes spatial sampling of the retinal image”. Neuron. (*equal contribution).
  • I. Benjumeda*, M. Molano-Mazón* and L. M Martinez (2014) “Flowers and weeds: cell-type specific pruning in the developing visual thalamus.” BMC Biology. (*equal contribution).
  • V. Villar-Cerviño, M. Molano-Mazón, T. Catchpole, M. Valdeolmillos, M. Henkemeyer, L. M. Martínez, V. Borrell and O. Marín (2013) “Cellular tiling in the cerebral cortex through contact repulsion”. Neuron.
  • A. Alenda, M. Molano-Mazón, S. Panzeri, and M. Maravall (2010) “Sensory input drives multiple intracellular information streams in somatosensory cortex”. J. Neurosci.