Courses

• NPRG030: Cvičení z Programování I
• NPRG031: Cvičení z Programování II
• NAIL087: Informatics and Cognitive Science I
• NAIL088: Informatics and Cognitive Science II
• NAIL128: Computational neuroscience seminar
  

Available student projects

Would you like to contribute to our research? This is a list of projects available for interested students. Most are designed to be completed within 3 to 6 month, but some can be expanded into longer projects, even full PhD scope. If you are interested in working on any of the projects please contact Ján Antolík.

If you want to get to know us, stop by at the Computational neuroscience seminar NAIL128 and have a chat with us.

Simulation of cortical implants for vision restoration

• Using DNNs to predicting perception from electrode stimulation data in macaque primary visual cortex.
  We have access to massive multi-electrode parallel recordings to tens of thousands visual stimuli from primary visual cortex (V1) of macaque. We have developed techniques DNN techniques for decoding visual stimulus from the responses of neurons in V1. In this project you will apply this model to decode responses from the massive multi-electrode recordings from macaque V1. We will then simulate electrode stimulation in this decoding DNN model and study what perecpetion the model predicts to this artificial stimulation.

• Modeling electrical stimulation in the visual cortex for visual prosthetic (brain-computer interface) applications
  Intracortical microstimulation (ICMS) describes the local stimulation of neurons in the cortex with penetrating electrodes. The technique enabled several breakthroughs in interfacing with the brain, among them the control of a cursor through neural activity in the motor cortex of a human patient and the visual perception of shapes in non-human primates. Only recently, a computational study presented a model unifying experimental observations how ICMS directly activates neurons in the close surrounding of the electrode: ICMS activates a sparse set of neurons around the electrode with the number of activated neurons in the sphere around the electrode tip decreasing over distance to the electrode. Yet, the way the brain responds to the direct activation of a set of neurons around the electrode with network activity (e.g. neural firing rates) remains poorly understood. The goal of this project is to implement an abstract model of ICMS for the our group’s large-scale model of cat primary visual cortex. Utilizing this model to simulate ICMS in cat primary visual cortex, the spatial extent of the network response to the stimulation shall be compared to the one reported in experimental recordings from monkey and human cortex.

• Characterizing the spatiotemporal activation profile of different neuron types for optogenetic stimulation
  New approaches to sensory prosthetics are being developed, wherby the cortex is stimulated via opto-genetic tools, which are being translated from mice to higher-order mammals including primates. However, it remains unclear how light activates optogenetically transfected neurons. We have recently developed computational simulations of how light propagates through neural tissue and how it activates a detailed morphological model of a V1 pyramidal (excitatory) neuron. It is of great interest to understand such light stimulation effects also in other morphological neural types, particularly in inhibitory neurons. In this project, the student will first learn to use the NEURON simulator and our existing simulation framework. In the following, they will integrate new single neuron models into the simulation framework and use them to simulate and analyze their neural activation through optogenetic stimulation.

• Stimulation protocols for vision restoration using brain-machine-interface.
  Recently we have applied the large-scale models developed in our team to the problem of cortical visual prosthesis. New approach to sensory prosthetics is being developed, wherby the the cortex is stimulated via opto-genetic tools, which are being translated from mice to higher-order mammals including primates. While all the technological components of the visual prosthesis are still under development, an important question remains open: how to stimulate the cortex to elicit percepts that are close to those due to the perception of the given stimulus under normal vision. This is where our large-scale modelling approach comes in. Using our V1 model simulations to test potenial stimulation strategies, we are making progress at answering this question. Currently, we have gained insights on how to eleicit simple canonical visual stimuli, specifically sinusoidal gratings. In the next step the student will be responsible for expanding the design and analysis to generic stimulation protocol capable of eliciting arbitrary visual stimuli. The current protocol can be straightforwardly expanded to this general case . The student’t responsibility will be to implement this new stimulation protocol in our simulation framework, test the protocol in our model of V1, and implement and perform all the required analysis. Strong programming and analytical skills required. Knowledge of Python, computation neuroscience or neurobiology of visual system a plus.

Machine Learning in Neuroscience

• Transfer learning: from spiking models to DNNs.
  We have recently showed that under the right circumstances, transfer learning, whereby we train a DNN model on a large synthetic dataset of image-response pairs generated by our large-scale biologically detailed model of V1, and than fine tuning the DNN model on smaller dataset of experimental recordings from V1 neurons, greatly improves the prediction power of the resulting DNN model on biological data. In this project you will take a state-of-the-art DNN model for predicting response of V1 neurons, pretrain it on our synthethic spiking model data, and then fine-tune it on recordings of macaque V1 neurons. You will analyse how the performance improves, or not, via the transfer learning, and characterise what ratio of synthetic and real data is optimal.

• Capturing spatio-temporal dynamics in primary visual cortex using deep neural network model using synthetic and macaque data.
  Application of deep neural networks to large datasets of neural data recorded in response to library of visual stimuly become the dominant method of unraveling the function of neurons in visual cortex. The standard approaches however (i) ignore known anatomical structure of visual cortex, (ii) use purely feed-forward NN as opposed to the intrinsically recurrent biological networks, (iii) only capture the mean steady state response. To address this, in this project you will build a DNN model composed of multiple recurrent neural network stages, that will be constrained to follow various know anatomical structures, and train the resulting model on fine temporal recordings of V1 responses to natural images. You will use combination of massive parallel multielectrode array recordings from macaque V1 to tens of thousands of images, and even more large-scale synthetic data from our spiking V1 modle to develop the new DNN models. We can also explore if replacing each neuron of the RNNs with one small DNN network (shared accross all neurons of the given type) that will model the transfer faction represented by the more complex non-linear spiking neurons will improve the performance of the RNN model.

• Novel DNN model for decoding visual stimuli from population recordings in primary visual cortex of macaque data.
  Recent years have witnessed a major breakthrough in DNN models ability to predict neural population activity of V1 neurons to novel visual stimuli. The inverse problem of predicting the natural image based on the activity it elicits in population of V1 neurons, however, remains much less studied, and consequently mastered. In this project you will implement and test a novel DNN architecture designed to predict visual inputs from population activity of V1 neurons.

• Determining maximally exciting and suppressive surround stimuli in a spiking model of primary visual cortex.
  Recently a novel method of studying coding properties of neurons in the visual system has been developed ref. It is based on two steps. First a forward deep neural network model - a model that predicts responses on neurons to images - is trained on neural recordings. Next by applying backpropagation to the model while keeping its weights fixed, a maximally exciting image is determined. Even more recdntly, this methods has been extended to identify the maximally exciting and maximally inhibiting modulatory surround stimuli ref. We have recently constructed a detailed large-scale model of cat primary visual cortex (V1). In this project student will apply this new method to synthetic responses of neurons from our large-scale model of V1 to (a) determine wether our model conforms to the aforemntioned recent experimental findings and (b) to obtain a mechanistic understanding how the discovered surround modulation effects emerge in cortical network.

• Machine learning from macaque and synthetic data to predict orientation preference maps.
  Being able to decode functional representations, specifically orientation maps, from spontaneous activity recorded in primary visual cortex (V1) is an essential prerequisite for developing effective stimulation protocols for visual prosthetic devices. In this project you will combine macaque V1 data with synthetic data from our V1 model to develop a ANN based approach for decoding these maps. You’ll first create a dataset by combining events obtained from multiple Utah arrays and augment it by shuffling electrode position. This step will be crucial to make the method work for a novel array/orientation maps. Each event will be labelled according to the orientation map that it resembles the most. Then you’ll use a ANN to find metric that returns distance between the labels of events. This ANN takes as input the every event from a given array and return the pairwise distance between events. Then you’ll develop a contrastive learning and ANN based mapping to a lower dimensional space where events are data points, and positive and negative samples decided according to the developed metrics (inspired by CEBRA). From this low dimensional space it should be possible to decode orientation of events and of electrodes (up to a global orientation shift per Utah array). </small

Disease

• Modelling dopaminergicaly and visualy driven theory of schyzophrenia development.
  Our clinical collaborators developed a new theory that postulates that schyzophrenia is driven by dopaminergicaly driven failure of developement of predictive signalling along the visual pathway. In this project you will take our detailed model of primary visual cortex (V1) to simulate the normal and diseased condition. In the diseased condition, the assumption is that the disruption of dompaminergic system cause changes of receptive field size in retina over time. Subsequently, you will use DNN techniques developed in our group to decode visual stimuli from the responses of the control and disease model. The prediction of the hypothesis is that the disruption of the dopaminergic system should to reduce ability to appropriately decode visual stimuli from the V1 neural response.

Spiking Network Models of Visual System

• Model of size dependent contrast invariance.
  Unlike the classical results that say that V1 neurons have tuning width invariant to the contrast of the stimulus, our collaborators show that this contrast invariance falls apart when the stimuli are small. You will use our large-scale spiking model of V1 to explain how this happens. We hypothesize that this is due to the insufficient lateral interactions that small stimuli recruit. You will simulate the same experimental conditions as those used by our collaborators, demonstrate that our model exhibits the same behaviour, and ivestigate the mechanisms in the model that lead to this phenomenon through analysis and inactivation experiments that disable parts of the model.

• Modeling different inhibitory neural types in large-scale spiking model of V1.
  The major inhibitory neuron sub-types (SOM,VIP and PV) were identified in mouse cortex. A major smaller evidence shows that such divsion exists also in cortex of higher mammals. Due to the lack of data our current large-scale model of cat V1 only considers a singe ‘abstract’ model of inhibitory neurons that simulates the average impact of these three sub-types. In this project you will take the know facts about the inhibitory neural sub-types in mouse and use them to simulate the potential impact of such subdivision on higher-mammalian cortex, using our V1 model.

• Porting model of cortico-thalamic loop to latest version of Mozaik
  This is an ideal introductory project to get acquainted with spiking neural networks, models of visual system, and our Mozaik simulation framework, and hence gateway to more advanced modelling projects. It is thus suitable as a volunteer project or bachelor thesis. The goal of the project is to take a model cortico-thalamic loop that was developed by a PhD student a while ago in a very old version of our simulator environment Mozaik and port it into the current version of the simulator. The main challange of the project will be to get acquinted with the simulator stack, understand the code of the model, and then once the porting is done, which in itself should be straighforwad, to repeat the series of virtual experiments undertaken in the linked original study and verify that all results still hold in the ported model version.

• Asymmetric On & Off responses in Retina, LGN and V1.
  ON and OFF pathways in the early visual system were long thought of as symmetric in their spatiotemporal properties. However, lately there has been a collection of studies in the retina (Chichilnisky 2002, Ratliff 2010, Sneha 2018) and in the visual cortex (Rahimi-Nasrabadi 2021), which show significant differences between the two pathways, mirroring the statistical differences in natural scenes. The goal of the project will be to expand the current Retina/LGN/V1 model used by the CSNG lab to reflect these findings.

• Model of monkey visual system.
  We have recently constructed a detailed large-scale model of cat primary visual cortex. Along with cat, macaque is the most common animal model in which vision in higher mammals is studied. Recently, a comprehensive dataset on macaque physiology and function has been published. The goal of this project would be to utilize this new data and reparametrize the existing model of cat V1 to obtain analogouse model of macaque V1. Exploration of the implication of species differences on V1 processing is a possible future extension of the project.

• Unified model of cat visual system.
  We have recently constructed a detailed large-scale model of cat primary visual cortex. Since, we have expanded the model in different directions in several followup studies: addition of cortico-thalamic loop, simulation of proshetic vision , and exploration of conductance dynamics. The goal of this project is to unify the existing models into single model instance and demonstrate that it can reproduce all the findings shown in the inidividual previous studies.

• OFF centred thalamic V1 convergence.
  Recent work by Alonso Lab has shown that thalamic ON and OFF afferents converging onto neurons in primary visual cortex have a very specific organization, which is OFF dominated, OFF centric and runs orthogonal to ocular dominance columns. Our current large-scale integrative model of V1 does not feature this specific organization of thalamo-cortical afferents. The goal of this project will be to integrate this specific thalamo-cortical convergence into the model, and then analyze the impact of this more specfific connectivity on the functional properties of the model.

• Embedding of detailed compartmental neuron models into large-scale model of primary visual cortex.
  One of the ongoing projects in our group is development of large-scale integrative model of cat primary visual cortex (V1). This model is based on the Adaptive-Exponential Leaky Integrate and Fire neuron model, which reduces biological neurons to a point process, ignoring its geometrical properties. In this project student will embed single compartmental model of V1 pyramidal neuron into the large scale point process simulation available in the group, and investigate the behavior of the added detailed neuron under the influence of the input coming from the large scale V1 simulation, focusing on properties influenced by the neuron’s geometry.

• Local-field potentials (LFP) in a large-scale model of cat primary visual cortex.
  One of the ongoing projects in our group is development of large-scale integrative model of cat primary visual cortex (V1). LFP is an electrophysiological signal generated by the summed electric current flowing from multiple nearby neurons within a small volume of nervous tissue. The goal of this project is to investigate the LFP signals that would be generated in our simulations of V1. The V1 model under investigation does not explicitly contain LFP signals, only the sub-threshold and spiking responses of individual neurons are available. Therefore one of previously proposed models of LFP signals such as this one will be used to generate artifical LFP signals based on the outputs of the V1 simulation. This will be followed by thourough analysis of the resulting LFPs and results compared to previous findings, including recent data recorded at UNIC by the Yves Frégnac group.

• Travelling waves.
  During spontaneous activity, mammalina cortex exhibits regular spontaneous emergence of waves of activity that travel across the cortical surface. Furthermore, spatially, these waves tend to be correlated with the functional organization across cortical surface. Such highly structured spontaneous activity, present even in low-level sensory cortical areas, has been hypothesized to be linked to such phenomena, as imagination, dreams, formation of long-term memory and other high-level cognitive phenomena. In this project student will explore the presence of such spontaneos waves in our comprehensive model of cat primary visual cortex. He/she will expand the Mozaik framework with the ability to record Local Field Potential type of signal. Perform experiments in which the waves will be recorded and will compare such in-silico generated data to in-vivo data from our international collaborators.

• Modeling sonogenetic stimulation in spiking model of V1.
  Brain machine interfaces hold promise for wide range of clinical applications including remediation of lost vision through direct stimulation in visual cortex. The current BMI techniques, dominated by direct electrical stimulation, face major limitation due to the inability to spatially precisely stimulate cortical tissue. Sono-genetics is a exciting new technique which allows activating of transfected neurons in neural tissue using ultrasound, which can be shaped in 3D space. In this project you will simulate how could such sono-genetic stimulation activate primary visual cortex. You will identify the potential but also limitations of this technique in facilitating effective interface with visual cortex.

Methods for analyzing experimental and simulated neural data

• Impact of traveling waves on decoding of functional cortical organisation from spontaneous activity.
  We have recently developed a method for decoding functional organisation, specifically orientation maps, from spontaneous population activity in primary visual cortex. Apart from teaching us about the nature of spontaneous activity in cortex, this is a crucial step for developing future visual neuro-prosthetic devices for vision restoration. In this method we used a PCA method to identify a low-dimensional subspace of the spontaneous population activity in which the orientation maps reside. Interestingly, these were not the first 2 PCA components but components 3-5. But what do the first two components correspond to? Our hypothesis is that they correspond to large-scale, perhaps whole brain, spontaneous waves, that are know to be present. In this project you will test this hypothesis by analysing a unique state-of-the-art data from 10 Utah electrode arrays implanted accross V1,V2 and V4 of 2 macaque monkeys.

• Relationship of traveling waves and oscillations in the cat primary visual cortex.
  Both traveling waves and oscillations have been observed in the primary visual cortex, but their relationship is unclear. The aim of the project is to first detect traveling waves in electro-corticographic recordings from the primary visual cortex of a cat and subsequently to analyze the spectral properties of the signal during the ocurrence of traveling waves. The question whether traveling waves lead to an increase of power in the gamma frequency band is of particular interest.

• Topological analysis of population activity in a large-scale V1 model.
  Topological methods offer a promissing new direction in the analysis of neural data [Saggar 2018]. It was previously reported that population activity in the primary visual cortex (V1) of macaque monkeys occupies a sphere [Singh 2008]. The aim of this thesis is to replicate the study by Singh et al 2010 for population activity generated synthetically by a large-scale model of a cat V1 [Antolík 2019] and investigate robustness of the topological structure of the activity to parameter changes.

• Fractal dimension of population activity in a large-scale V1 model.
  The activity elicited in the primary visual cortex (V1) by a visual stimulus may directly reflect its spatial properties such as the frequency of spatially periodic structure. The aim of this thesis is to measure the fractal dimension of activity patterns elicited in a large-scale V1 model [Antolík 2019] as a function of the stimulus. It would be particularly interesting to compare the topological properties of activity triggered by natural vs artificial stimuli such as drifting gratings typically used in experiments.

Models of neural system development

• Biologically plausible model of body representation development (collaboration with the robotics group of Matej Hoffman, CVUT).
  This project is performed in tight collaboration with the robotics group of Matej Hoffman. The goal of this project is to explain how body representations can be learned in humanoid robots during haptic self-exploration based on inputs provided by ‘artificial skin’ covering the robot’s body. We hypothesize that our model of cortical development proposed can aid this goal in following two ways:  (i) the model itself, when fed with the somatosensory data will form effective, biologically plausible representation of body surface, (ii) the novelty signal that can be straightforwardly obtained from the model can within the closed loop paradigm guide self-exploration behavior towards efficient exploration of the body space. The novelty signal is readily available in the model, as novel inputs are poorly represented by the evolving cortical representation and thus the input will have high distance from the most representative (close within input space) cortical neuron. Thus a simple winner-take-all mechanism at the cortical level, that outputs the distance between the input and the point in input space the winner neuron represents will yield effective novelty signal. The student will test these hypotheses in collaboration with the Hoffman group guided by following milestones. He/she will implement and validate the model of somatosensory map formation from artificial skin inputs, implement the novelty signal extraction mechanism, test its impact on map formation in closed-loop system, integrate the resulting model within the humanoid robotic system at Hoffman group, and perform experiments to confirm effectiveness of the model and search for bio-morphic correlates in the resulting behavior.

• Development of long-range correlations in spontaneous activity.
  In a recent paper, Smith et al. demonstrate that spontaneous activity in early post-natal V1 in ferrets, before eye opening, is already highly structured with spontaneous spatial correlations that are linked to the orientation maps that develop few days later. Furthermore, it was shown, that no afferent input from thalamus (or retina) is needed for these structure in spontaneous activity to appear. The authors hypothesize, that local maxican-hat-like connectivity that is anysothropic is sufficent for such correlation patterns to appear. In this project we will verify the hypothesis that the anaysothropy of local connections, which is questionable, is not neccesary if hebbian learning on the cortico-corticl synapses is assumed, and furthermore, such mechanisms can explain further development and refinenement of orientation maps. The goal of this project is to build a firing-rate model with hebbian-learning that will demonstrate test this hypothesis.

• Reconciling activity driven development of orientation maps with ON/OFF V1 convergence.
  During post-natal development, primary visual cortex undergoes remarkable functional organization resulting in expression of topologically smooth orientation map across it’s surface. The most common type of explenation for this phenomena is activity based development, whereby internally generated or visually driven activity coupled with plasticity in the thalamo-cortical and corico-cortical pathway induces gradual establishment of the orientation maps. LISSOM based familiy of models is an example of such activity + plasticity driven theoretical explanation of this phenomena. Recent work by Alonso Lab has shown that thalamic ON and OFF afferents converging onto neurons in primary visual cortex have a very specific organization, which is OFF dominated, OFF centric and runs orthogonal to ocular dominance columns. The current activity driven models of V1 development cannot explain this specific organization of thalamo-cortical afferents. The goal of this project will be the expand these models to account for these new findings.

• Unifying retinal mozaik model with activity based development.
  During post-natal development, primary visual cortex undergoes remarkable functional organization resulting - among others - in expression of topologically smooth orientation map across it’s surface. The most common type of explenation for this phenomena is activity based development, whereby internally generated or visually driven activity coupled with plasticity in the thalamo-cortical and corico-cortical pathway induces gradual establishment of the orientation maps. LISSOM based familiy of models is an example of such activity + plasticity driven models. An alternative explanation has been proposed by Ringach (see also this) , in which the initial orientation maps are directly established by the very specific geometric properties of retinal ganglion cells RFs positions in visual space: retinal mozaiks. However, this explanation can account only for initial very weak orientation maps, and low orienation selectivities of individual neurons in particular, and it is clear that the system has to undergo major further refinement in order to match the experimentally observed adult state. The goal of this project is to combine the two hypothesis of orientation map development and investigate their possible interactions. Specifically retinal mozaiks will be introduced into a LISSOM model, thus inducing the initial orientation maps based on Ringach et al. theory. This will be followed by simulation of the activity and plasticity driven development, which should lead to refinement of the intial maps. The correspondance between the initial retinal mozaik induced map with the final developed map will be assesed, and possible advantages of such dual orientation map development mechanism will be investigated.

Software engineering projects

• Mozaik dockerization.
  Mozaik is an automated workflow for large-scale neural simulations. At the present moment, it relies on a range of packages, with a multi-step installation process. Being the basis of the majority of CSNG lab projects and publications, we would like to make the installation process easier, for easier replicability and adoption outside the CSNG lab. The project aims to dockerize Mozaik to simplify the installation process.

• Mozaik analysis parallelization.
  Mozaik is an automated workflow for large-scale neural simulations. At the present moment, it is using MPI parallel execution for running simulations, but not in data analysis, which can thus take a long time. The project aims to parallelize the Mozaik analysis codebase to remedy this issue.

• Data-store module based on object oriented database for biological neural network simulator.
  Mozaik is a an automated workflow for large-scale neural simulations, with a highly modular architecture. One of the core Mozaik modules is a data-store, in which recordings from simulations richly annotated with metadata regarding experimental context are stored. Currently the data-store module is implemented as a database-like system based on Neo library for internal representation of recorded data. The goal of this project is to develop an alternative data-store module based around dedicated key-value database such as BerkelyDB or CodernityDB.

• A 3D model visualization of detailed spiking neural network models.
  Mozaik is a an automated workflow for large-scale neural simulations. The model of primary visual cortex developed in our lab, and implemented in Mozaik, has a complex connectivity structure. Although there are various tests that the connectivity has been realized as expected, currently, there is no easy way to visualize the network spatial structure and connectivity in Mozaik. The aim of this project is to develop a 3D model visualization tool, for Mozaik, possibly building on existing tools such as Moogli, and NeurAnim.

• Sumatra integration with Mozaik.
  Mozaik is a an automated workflow for large-scale neural simulations. Sumatra is a tool for provenance tracking. Sumatra shares several features with Mozaik, but it also posses features that would enhance the Mozaik workflow. The goal of this project is to integrate Sumatra with Mozaik, and remove overlapping features from Mozaik and delegating them to Sumatra, in line with long term goal of outsourcing as much functionality from Mozaik to dedicated tools. This project is suitable for students with interest in Neuroinformatics and moderate skills in Python and versioning systems.

• Parameter searches in Mozaik
  Mozaik is a an automated workflow for large-scale neural simulations. A common need in computational modeling is the need to perform a parameter search of model paramaters to assess how it behaves under different parametrisations. The Mozaik framework currently possess a module for automating such parameter searches, but with number of important limitations. The goal of this project will be to improve the implementation of this module to surprass these limitations.

Web development projects

• Arkheia update & expansion.
  Arkheia is an Angular-based data visualization tool used by much of the CSNG team to interpret simulation results, with a tech stack that needs updating. The project would consist of updating/rewriting Arkheia such that it would use an up-to-date tech stack, and potential expansion of Arkheia functionality.

• Open Vision project.
  Mozaik is a an automated workflow for large-scale neural simulations. Inspired by the OpenWorm initiative, this project strives to bring neural based modelling of vision to the public. It will seek to engage the cognitive sciences enthusiast community into coordinate effort to build a comprehensive model of early and higher vision. We envision multiple phases of the project:
  (1) Build a server running mozaik based V1 model and serve it on the new Open Vision website. The website will allow any member of public to submit a video and receive back the responses of selected model cells.
  (2) Develop a web frontend to the Mozaik toolkit and use it to expand the Open Vision website to allow full configuration of the served model. Publish more models and experimental protocols already develop in our group.
  (3) Expand upon 1 and 2 to build full open science platform similar to OpenWorm project, and build striving community around it.

• Graphical user interface for biological neural network simulator.
  Mozaik is a an automated workflow for large-scale neural simulations. Mozaik automatically records data from simulations, annotates it with metadata regarding experimental context, and stores them in an internal data-store. An query based interface allows analysis and visualization modules to efficiently navigate through the stored data based on the attached metadata. Currently, Mozaik offers only programatic API to perform these interactions with data-store. The goal of this project would be to write a HTML based graphical user interface frontend, to the Mozaik data-store, that will allow users to conveniently and interactively navigate and select data from the data-store and subsequently execute on them anaysis and visualization routines from Mozaik libraries.
  

• Deployment of a biological neural network simulator on a HPC platrform.
  Mozaik is a an automated workflow for large-scale neural simulations. Mozaik depends on a moderate software stack including PyNN as a simulator independent model specification language, and Nest as the simulator of choice in our projects. Currently we deploy Mozaik (together with the software stack) on a local cluster, however already at this relatively small scale we are aware of number of inefficiencies in terms of its performance in the parallel environment. Furthermore, in future we would like to deploy Mozaik on a large-scale High Performance Computing (HPC) platform such as ADA. The goal of this project is to test and optimize Mozaik and it’s underlying software stack to run efficiently on the local cluster, and subsequently scale it up to a large-scale HPC platform. This project is suitable for students with experience and interest in parallel programming and HPC.