I'm currently a post-doc at the Neuromorphic Machine Intelligence Lab (NMI) lab, working with prof. Emre Neftci on Neuromorphic Computing, Machine Learning and Computational Neuroscience.
Currently, he is a member of Neuromorphic Intelligence lab (NMI) as a post-doctoral researcher working with professor Emre Neftci. He is involved in the development of a neuromorphic device capable of embedded and on-line learning. He is one of the core developers of the software simulator for the under-development device. He investigates how natural mechanisms of biological brains can lead to more efficient and biological plausible machine learning algorithms suitable for neuromorphic chips. Furthermore, he is interested in implementing high-performance neural (rate) simulations. And last but not least, he continues collaborating with professor Antoine Chaillet on control theory applications in neuroscience.
He has worked on Parkinson's disease (PD) with Antoine Chaillet at Supélec (École supérieure d'électricité) in Gif-sur-Yvette, France as post-doctorate researcher. The topic of the post-doc was: Modeling and identification of neuronal firing rates of Basal Ganglia using optogenetics and it was a part of the French ANR project SynchNeuro. He developed a model of neural fields of the basal ganglia in order to better understand the genesis of PD pathological oscillations and ultimately to propose a suitable closed-loop deep brain stimulation mechanism, which will be able to eliminate pathological oscillations. During that post-doc he collaborated with Stefan Palfi and Suhan Senova of H. Mondor de Créteil hospital and Christophe Pouzat of University Paris Descartes (MAP5).
A second field of research in which he was involved is that of cortical plasticity and self-organization. He used as mathematical framework the theory neural fields. One of the perspectives of his research was to understand how the brain cortex can self-organize itself and how it can recover from a serious injury, like a stroke, due to cortical reorganization. The better understanding of the underlying mechanisms, both computational and neurophysiological, have been also taken into consideration.
He was involved in projects concerning rhythmical motor control and human tremor. The aim of those projects were the processing and the analysis of neurophysiological signals like EEG and EMG, as well as the interpretation of those signals in so as to determine the role of the human tremor and figure out where its source lies within the brain or the spinal cord. Other projects that he was involved in related to Central Pattern Generators (CPGs). The purpose of those projects were the mathematical/computational modelization of locomotion patterns and the developing of algorithms with applications to humanoid robots.
|cRBM||cRMB, is a C implementation of a restricted Bolzmann machine (RBM) supporting non-negative matrix factorization (NMF) as well.|
|pyAIS||pyAIS, is a Python script that implements as a class the Annealed Importance Sampling algorithm for Boltzmann machines based on the work of Ruslan Salakhutdinov, (original Matlab code can be found here: http://www.utstat.toronto.edu/~rsalakhu/rbm_ais.html).|
|CorrSpikeTrains||CorrSpikeTrains, is a Python class implementing the Cox processes and Mixture methods for correlated spike trains generation. It is based on the paper: Romain Brette, "Generation of Correlated Spike Trains", Neural Computation 21, 188-215, 2009.|
|SPySort||SPySort, is a spike sorting software developed by Christophe Pouzat and Geogios Is. Detorakis. The repository contains a Python package providing all the classes for performing a spike sorting.|
|NeuralFieldDBSModel||This is a collection of Python scripts that implemt a computational model of Basal Gaglia. The purpose of the underlying model is to describe the attenuation of pathological oscillations during Parkinson's disease due to optogenetics Deeb Brain Stimulation.|
|DelayedNFStability||This is a collection of Python scripts and C code, which numerically computes the solution of a two-dimensional delayed neural field of two-populations. The source code generates the figures provided in A Global Stability Analysis for Delayed Neural Fields, BCCN, Göttingen, 2014.|
|SI-RF-Structure||Here is provided the Python source code of the peer-reviewed article Structure of Receptive Fields in a Computational Model of Area 3b of Primary Sensory Cortex, G.Is. Detorakis and N.P. Rougier, Frontiers in Computational Neuroscience, doi: 10.3389/fncom.2014.00076.|
|SITopMaps||SITopMaps is a Python and C implementation of a self-organized neural field model of the primary somatosensory cortex. This source code implements the article: A Neural Field Model of the Somatosensory Cortex: Formation, Maintenance and Reorganization of Ordered Topographic Maps, G.Is. Detorakis and N.P Rougier, PLoS ONE, doi:10.1371/journal.pone.0040257.|
|pygpsa||A Python script that computes the pseudospectra of a rectangular matrix based on the algorithm provided in the article: Pseudospectra of rectangular matrices, T.G. Wright and L.N. Trefethen, IMA Journal of Numerical Analysis, 22, 501--519, 2002.|
|CRKSolvers||CRKSolvers is a C collection of Runge-Kutta methods. The collection contains the classical Euler's method, the RK45, the refined RK45 and the Fehlberg's method.|
|LaTeXTools||LaTeXTools, is a collection of simple python scripts
which provides useful tools for latex productivity.
|SOM-DyDx||SOM-DyDx, is a python script which implements Demartines dy-dx representation method. This method is useful in terms of inspecting if a self-organized map is consistent and well-organized. More details can be found in the original paper of Demartines: Organization measures and representations of the Kohonen maps, IFIP Working Group 10.6 Workshop, 1992.|
|BinCat||BinCat, is a very simple C programm which reads a binary file and prints to stdout its contents.|
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