Head of Neuronal Data Analytics Lab
Canadian Centre for Behavioural Neuroscience
Department of Neuroscience, University of Lethbridge
e-mail: email@example.com , phone: (403) 394-3974, office: EP 1216, Lab: EP 1155
The brain is composed of billions of interconnected cells, creating the most complex system within the body. To study how such combined neuronal activity underlies an animal’s processing of information, we record signal simultaneously from a large number of neurons using a novel technique: multi-site silicon microelectrodes. Such parallel recordings from groups of neurons in the cortex and in subcortical structures are helping to understand information processing and memory formation in the brain. Our lab also investigates how relations between neuronal populations are distorted by stroke and by different neurological disorders like multiple sclerosis and epilepsy. In pursuit of those research goals, we combine experiments with development of sophisticated data analysis methods and computer models.
Short video about my research.
Campus Alberta Neuroscience spotlight about my research.
Research highlights from Luczak lab (packets of neuronal activity)
Either spontaneously or in response to stimuli, neurons are active in a coordinated fashion. For example, an onset response to sensory stimuli usually evokes a 50-200ms long burst of population activity. We have shown that such bursts of neuronal activity are not randomly organized, but rather composed of stereotypical sequential spiking patterns. To underline this fine-scale internal organization of such population bursts, we referred to them as ‘packets’. It has been shown that packets are ubiquitous feature of spontaneous and stimulus evoked network activity, and are present across different brain states. Although these packets have a generally conserved sequential spiking structure, the exact timing and number of spikes fired by each neuron within a packet can be modified depending on the stimuli. This packet-like organization of neuronal activity may provide an explanation for multiple puzzling observations about neuronal coding. It is interesting to note that organizing population activity into packets resembles how engineers designed information transfer over internet, where information is divided in small, formatted network packets to increase communication efficiency and reliability. Nature Rev Neurosci paper, Talk, Slides.
Job opportunities for PhD student or Postdoc
Our lab seeks highly motivated individuals with strong computational backgrounds to work at the interface of neuroscience and machine learning. We are especially interested in application of deep neural networks to improve our understanding of neuronal data, and application of recent neuroscience findings to improve understanding and performance of deep neural networks.
Undergraduate students interested in any of the above topics may also apply for Independent study in my lab.
Current lab members:
- Hardeep Ryait, Ph.D. (postdoc)
- Edgar Bermudez Contreras, Ph.D. (postdoc co-supervised with Dr M. Mohajerani)
- Axita Shienh (research assistant / technician)
- Dillon Hambrook (PhD student co-supervised with Dr M. Tata)
- Introductory workshop on computational methods in neuroscience
- Statistics and Programming in Matlab (NEUR 3850A)
- Brain and Behavior (NEUR 2600B)
- Past courses:
- Applied Statistics for Neuroscience Research (NEUR 3850B)
- Introduction to Programming for Neurobiologists (Matlab) (NEUR 3850A)
- Stress and Brain Function (NEUR 4850 – together with Dr. GA Metz)
- Computational Neuroscience
- associate prof. – CCBN, University of Lethbridge
- visiting assoc. prof. – Stanford University (I. Soltesz lab)
- assistant prof. – CCBN, University of Lethbridge
- postdoc – Rutgers University (K.D. Harris lab)
- postdoc – Yale University (R. Coifman lab in Comp. Sci. Dept. and M. Laubach lab in Neurosci. Dept.)
- Marie Curie Fellowship – International School for Advanced Studies in Trieste, Italy (A. Treves lab)
- Ph.D – Jagiellonian University, Medical College, Poland. (thesis: The application of fractal geometry in neuroanatomy; J. Trabka lab)
- M.Sc in Biomedical Engineering – Wroclaw University of Technology.
Publications (papers of particular interest are marked with *** )
- *** Neumann AR, Raedt R, Steenland HW, Sprengers M, Bzymek K, Navratilova Z, Mesina L, Xie J, Lapointe V, Kloosterman F, Vonck K, Boon PAJM, Soltesz I, McNaughton BL, Luczak A. Involvement of fast-spiking cells in ictal sequences during spontaneous seizures in rats with chronic temporal lobe epilepsy. Brain (2017). Paper; Suppl. This paper was selected for commentary, and it was chosen for F1000 recommendation. Media coverage: U of L, MetroNews, Herald.
- *** Jercog D, Roxin A, Bartho P, Luczak A, Compte A, de la Rocha J. UP-DOWN cortical dynamics reflect state transitions in a bistable balanced network. eLife (2017). Paper
- Gerrard B, Singh V, Babenko O, Gauthier I, Yong WV, Kovalchuk I, Luczak A, Metz GAS. Chronic Mild Stress Exacerbates Severity of Experimental Autoimmune Encephalomyelitis in Association with Non-coding RNA and Metabolic Biomarkers. Neuroscience (2017). Paper.
- Turchenko V, Luczak A. Creation of a Deep Convolutional Auto-Encoder in Caffe. The 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS) (2017) Paper, Code.
- Chalmers E, Bermudez Contreras E, Robertson B, Luczak A, Gruber A. Learning to Predict Consequences as a Method of Knowledge Transfer in Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems (2017). Paper.
- Turchenko V, Chalmers E, Luczak A. Deep Convolutional Auto-Encoder with Pooling – Unpooling Layers in Caffe. arXiv preprint (2017) Preprint, Code.
- Chalmers E, Luczak A, Gruber A. Computational Properties of the Hippocampus Increase the Efficiency of Goal-Directed Foraging through Hierarchical Reinforcement Learning. Front. Comput. Neurosci. (2016) Paper.
- Chalmers E, Bermudez Contreras E, Robertson B, Luczak A, Gruber A. Context-Switching and Adaptation: Brain-Inspired Mechanisms for Handling Environmental Changes (proceedings of the IEEE 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver (2016) Paper.
- Luczak A. Head-down self-treatment of choking. Resuscitation (2016) Paper; expanded version.
Press coverage: The Huffington Post , Popular Science , AOL, 98.5 KTK , NewsCaf , The News Commenter , LifeHacker , OOYUZ , 24.hu , ChuanSong , Curioso , Slate.fr .
- *** Luczak A, McNaughton BL, Harris KD. Packet-based communication in the cortex . Nature Rev Neurosci (2015) Paper; Talk; Slides. This paper has done particularly well and is in the 97th percentile of papers ever tracked by Altmetric.
- *** Bermudez Contreras E, Gomez Palacio Schjetnan A, Muhammad A, Bartho P, McNaughton BL, Kolb B, Gruber AJ, Luczak A. Formation and reverberation of sequential neural activity patterns evoked by sensory stimulation is enhanced during cortical desynchronization. Neuron (2013) Paper.
- Faraji J, Gomez-Palacio-Schjetnan A, Luczak A, Metz GA. Beyond the Silence: Bilateral Somatosensory Stimulation Enhances Skilled Movement Quality and Neural Density in Intact Behaving Rats. Behavioural Brain Research (2013) Paper.
- *** Luczak A, Bartho P, Harris KD. Gating of sensory input by spontaneous cortical activity. J.Neurosci. (2013). This paper was chosen for Research Highlights in Nature Reviews Neuroscience. Paper.
- Gomez Palacio Schjetnan A, Faraji J, Metz GA, Tatsuno M, Luczak A. Transcranial Direct Current Stimulation in Stroke Rehabilitation – A Review of Recent Advancements. Stroke Research and Treatment (2013) Paper.
- Luczak A, Bartho P. Consistent sequential activity across diverse forms of UP states under ketamine anesthesia. Eur. J. Neurosci. (2012) Paper.
- Dowdall JR, Luczak A, and Tata MS. The Neural Signatures of Efficient and Inefficient Visual Search: Posterior-contralateral Evoked Theta and its Relation to the N2pc. Neuropsychologia 50 (2012) Paper.
- Luczak A, MacLean JN. Default activity patterns at the neocortical microcircuit level. Front. Integr. Neurosci. 6:30 (2012) Paper.
- Ponjavic-Conte KD, Dowdall JR, Hambrook DA, Luczak A, Tata MS. Neural correlates of auditory distraction revealed in theta-band EEG. NeuroReport: 23 (2012) Paper.
- Gomez Palacio Schjetnan A, Luczak A. Recording Large-scale Neuronal Ensembles with Silicon Probes in the Anesthetized Rat. J Vis Exp. (2011). Paper, Video. This video is highly popular as evidenced by over 10,000 downloads.
- Luczak A. Measuring neuronal branching patterns using model-based approach. Front. Comput. Neurosci. 4:135 (2010). Paper, Code.
- Harris KD, Bartho P, Chadderton P, Curto C, de la Rocha J, Hollender L, Itskov V, Luczak A, Marguet SL, Renart A, Sakata S. How do neurons work together? Lessons from auditory cortex. Hearing Research, 1-17 (2010). Paper.
- *** Luczak A, Barthó P, Harris KD. Spontaneous events outline the realm of possible sensory responses in the auditory cortex. Neuron 62 (2009). This paper was highlighted as of special interest in review: Ringach DL, Curr Opin Neurobiol. 2009. Paper.
- Barthó P, Curto C, Luczak A, Marguet S, Harris KD. Population coding of tone stimuli in auditory cortex: dynamic rate vector analysis. Eur. J. Neurosc. 30 (2009). Paper.
- *** Luczak A, Barthó P, Marguet SL, Buzsáki G, Harris KD. Sequential structure of neocortical spontaneous activity in vivo. Proc. Natl. Acad. Sci. 104 (2007). Paper.
- Luczak A. Spatial embedding of neuronal trees modeled by diffusive growth. J. Neurosci. Methods 157 (2006). Paper, Code.
- Luczak A, Narayanan NS. Spectral representation – analyzing single-unit activity in extracellularly recorded neuronal data without spike sorting. J. Neurosci. Methods 144 (2005). Paper , Code.
- Luczak A, Hackett T, Kajikawa Y, Laubach M. Multivariate receptive field mapping in marmoset auditory cortex. J. Neurosci. Methods 136 (2004). Paper, Code.
- Luczak A, Hackett T, Kajikawa Y, Laubach M. “Modeling stimulus-response functions in the auditory system” Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference, NJIT, Newark, NJ, 2003.
- Laubach M, Arieh Y, Luczak A, Oh J, Xu Y. “A cluster of workstations for on-line analyses of neurophysiological data” Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference, NJIT, Newark, NJ, 2003.
- Oh J, Luczak A, Laubach M. “Estimating neuronal variable importance with Random Forest” Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference, NJIT, Newark, NJ, 2003.
- Luczak A, Skrzat J, Trabka J. “Model of neuronal distribution during development in rat cortex based on cellular automata” Proceedings of V National Conference: Modelling of Biological Systems (MBS2000), Krakow, Poland, 2000.
- Skrzat J, Luczak A, Trabka J. “Fractal modelling of dendritic structures as a paradigm for morphogenetic structure of neurons” Proceedings of MBS2000, Krakow, Poland, 2000.
- Trabka J(jun.), Trąbka J, Luczak A. Simulation and modelling as the cognitive procedures. Proceedings of V National Conference: Modelling of Biological Systems (MBS2000), Krakow, Poland, 2000.
- Luczak A. “Modeling of growth and shape of neurons by the application of fractal geometry” Proceedings of the 1st European Interdisciplinary School on nonlinear Dynamics for System and Signal Analysis, EUROATTRACTOR 2000, Pabst Science Publishers, Warsaw, 2000.
- Luczak A, Skrzat J, Trabka J. “Parametric description of neuron shape on the basis of a generator of artificial neurons” Proceedings of XI National Meeting – Artificial Intelligence, Siedlce, Poland, 1999.
- Luczak A. Packets of sequential neural activity in sensory cortex; In “Analysis and modeling of coordinated multi-neuronal activity – Sequence phenomena and memory-trace replay”. Editor: Tatsuno M. Springer, Series in Computational Neuroscience. 2015 Chapter
- Luczak A. Shaping of neurons by environmental interaction; In “Dendritic computations through morphology and connectivity”. Editors: Torben-Nielsen B, Remme M, Cuntz H. Springer, Series in Computational Neuroscience. 2014 Chapter
Brain state dependent therapy for improved neural training and rehabilitation (patent pending in USA and Canada; filed in June 2015). full text
Description: This invention provides means to assess how receptive to learning (plastic) the brain is at any given time. This invention has several commercial applications: (1) Rehabilitation centers could use devices based on this technology to measure brain responsiveness to therapy, which could improve rehabilitation after e.g. brain injury. (2) In addition, a consumer version for the general public could be used to focus learning/training to times of maximal brain receptivity, and as a biofeedback device for self-training to produce plastic brain states. Considering that this idea could result in significant health benefits, my colleagues and I started a company DeepBrain Analytics Inc., to facilitate bringing this invention to the market.
I am President of the Lethbridge Chapter of the Society for Neuroscience (SfN), where I am responsible for organizing multiple events to promote brain research, which engage over 500 people annually. The main events include:
- NSERC Discovery Accelerator Supplement (awarded to the top ~4% out of over 3000 applicants across Natural Sciences and Engineering fields in Canada)
- NSERC Discovery Grant
- University of Lethbridge Health Research Accelerator Fund