Artur Luczak
Elected member of the College of the Royal Society of Canada
Professor of Neuroscience, Canadian Centre for Behavioural Neuroscience
Department of Neuroscience, University of Lethbridge
4401 University Drive, Lethbridge, AB, T1K 3M4, Canada
e-mail: , phone: (403) 394-3974, office: SA8240, Lab: SA8118

Research Interests

We are using electrophysiological and machine learning methods to study information processing in the brain. One of our main contributions is a development of ‘neuronal packet’ concept, which describes basic building blocks of neuronal code (Nature Rev Neurosci 2015, Neuron 2013, J Neurosci 2013, Neuron 2009, PNAS 2007). Moreover, we derived a predictive learning algorithm from basic cellular principles, i.e. from maximizing metabolic energy of a neuron, which may offer a step toward a general theory of neuronal learning (Nature Machine Intelligence; 2022). We are also studying changes in neuronal activity caused by neurological disorders, especially epilepsy (Brain 2017). To facilitate it, our lab developed Deep Neural Networks for detecting neurological deficits (PLOS Biology 2019).

My talk about our research on Brain Learning Mechanisms and Consciousness given at the Royal Society of Canada meeting in 2022.

Research highlights from Luczak lab

Single neuron predictive learning. Neurons have biochemical mechanisms capable of performing complex computations. Thus, the simple models of neurons used in machine learning may be missing the essential computational elements of the brain. We showed that to maximize metabolic energy, individual neurons need to predict their own expected future activity. This results in a new learning algorithm which could be a crucial component of the brain’s learning mechanism.   Nature Machine Intelligence paper

Here is my talk about our epilepsy research and at 17:40, I also describe single neuron predictive learning.

Neuronal packet theory. We described that in the sensory cortex, information is not processed continuously, but rather is divided in 50~500ms long “packets”, which have specific sequential structure of neuronal activity. Each packet can be conceived of as a discrete ‘message’, with neurons active at the beginning of a packet providing general information (e.g. it is a face), while neurons active in the latter phase encode more precise information (e.g. this is face of my friend John). This packet-like organization of neuronal activity may provide an explanation for multiple puzzling observations about neuronal coding.  Nature Rev Neurosci paper.

Short video about our research on neuronal packets, and here is more in depth talk about packets (+ 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. In our lab we are also recording activity of hundreds of neurons in normal and epileptic animals, and successful candidate is welcome to participate in those projects. Preferred candidate should have strong background in modeling neurons and networks, and should be very familiar with models of synaptic plasticity (e.g. BCM). Our lab at the Canadian Centre for Behavioural Neuroscience in Lethbridge is located in the sunniest area of Canada and next to scenic Rocky Mountains.

Undergraduate students interested in any of the above topics may also apply for Independent study in our lab.


Short bio

Publications (Google Scholar profile)

  1. An orexigenic subnetwork within the human hippocampus.
      Barbosa D, Gattas S, Salgado J, Kuijper F, Wang A, Huang Y, Kakusa B, Leuze C, Luczak A, Rapp P, Malenka R, Miller K, Heifets B, Bohon C, McNab J, Halpern C
      Nature (2023) Paper
      Press coverage: Newswise, EurekAalert!, ScienceNewsNet.

  2. Reinforcement Learning with Brain-Inspired Modulation can Improve Adaptation to Environmental Changes.
      Chalmers E, Luczak A
      Lecture Notes in Computer Science (2023) Paper

  3. Editorial: Deciphering population neuronal dynamics: from theories to experiments.
      Yang H, Shew W, Yu S, Luczak A, Stringer C, Okun M.
      Front. Syst. Neurosci. (2023) Paper

  4. Hippocluster: an efficient, hippocampus-inspired algorithm for graph clustering.
      Chalmers E, Gruber A, Luczak A.
      Information Sciences (2023) Paper

  5. Biologically-inspired neuronal adaptation improves learning in neural networks.
      Kubo Y, Chalmers E, Luczak A.
      Communicative & Integrative Biology (2023) Paper, Code

  6. Neurons learn by predicting future activity.
      Luczak A, McNaughton BL, Kubo Y.
      Nature Machine Intelligence (2022). Paper , Suppl. , Code.
      Cover story for Jan. 2022 issue of Nature Mach. Intell. This paper was selected by PNAS for journal club, it was made a feature story in TechXplore, it was included in Nature special collection: A revolution in robotics and artificial intelligence, and is in the top 5% of papers tracked by Altmetric.

  7. Predictive neuronal adaptation as a basis for consciousness.
      Luczak A, Kubo Y.
      Frontiers in Systems Neurosci. (2022). Paper , Suppl.
      Here we propose theory of consciousness derived from basic cellular properties, which offers testable predictions. In this paper we also derived an equation to quantify consciousness.

  8. Combining Backpropagation with Equilibrium Propagation to improve an Actor-Critic Reinforcement Learning framework.
      Kubo Y, Chalmers E, Luczak A.
      Frontiers in Computational Neuroscience (2022) Paper, Code

  9. Epileptic seizures and link to memory processes.
      Das R, Luczak A.
      AIMS Neuroscience (2022) Paper

  10. Sensory experience selectively reorganizes the late component of evoked responses.
      Bermudez Contreras E, Palacio-Schjetnan AG, Luczak A, Mohajerani MH.
      Cerebral Cortex (2022) Paper

  11. Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice.
      Afrashteh N, Inayat S, Bermudez-Contreras E, Luczak A, McNaughton BL, Mohajerani MH.
      Cell reports (2021). Paper

  12. A Neural Network Reveals Motoric Effects of Maternal Preconception Exposure to Nicotine on Rat Pup Behavior: A New Approach for Movement Disorders Diagnosis.
      Torabi R, Jenkins S, Harker A, Whishaw IQ, Gibb R, Luczak A.
      Frontiers in Neurosci. (2021). Paper

  13. Spatiotemporal patterns of neocortical activity around hippocampal sharp-wave ripples.
      Abadchi JK, Nazari-Ahangarkolaee M, Gattas S, Bermudez-Contreras E, Luczak A, McNaughton BL, Mohajerani MH.
      eLife (2020). Paper

  14. Diverse Perspectives on Interdisciplinarity from the Members of the College of The Royal Society of Canada.
      Cooke S et al.
      FACETS (2020). Paper

  15. Data-driven analyses of motor impairments in animal models of neurological disorders.
      Ryait H, Bermudez-Contreras E, Harvey M, Faraji J, Mirza Agha B, Gomez-Palacio Schjetnan A, Gruber A, Doan J,  Mohajerani M, Metz G.A.S, Whishaw IQ, Luczak A..
      PLOS Biology (2019) Paper, Code.
      Here we developed Deep Neural Network for discovering novel markers of neurological deficits. Media coverage:, Science Daily, @VentureHealth, UNews.

  16. Using neuron spiking activity to trigger closed-loop stimuli in neurophysiological experiments.
      Molina L, Ivan V, Gruber A, Luczak A.
      JoVE (2019) Paper, Code, Video

  17. Direct Current Stimulation Improves Limb Use After Stroke by Enhancing Inter-hemispheric Coherence.
      Gomez-Palacio Schjetnan A, Gidyk D, Metz G.A.S, Luczak A.
      Acta Neurobiologiae Experimentalis (2019) Paper

  18. Effect of body position on relieve of foreign body from the airway.
      Luczak A.
      AIMS Public Health (2019) Paper

  19. Phase of EEG theta oscillation during stimulus encoding affects accuracy of memory recall.
      Jalali A, Tata MS, Gruber A, Luczak A.
      NeuroReport (2019) Paper

  20. Deep Convolutional Auto-Encoder with Pooling – Unpooling Layers in Caffe.
      Turchenko V, Chalmers E, Luczak A.
      International Journal of Computing (2019) Paper, Code.

  21. Predict Consequences as a Method of Knowledge Transfer in Reinforcement Learning.
      Chalmers E, Bermudez Contreras E, Robertson B, Luczak A, Gruber A.
      IEEE Transactions on Neural Networks and Learning Systems (2018). Paper.

  22. Involvement of fast-spiking cells in ictal sequences during spontaneous seizures in rats with chronic temporal lobe epilepsy.
      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.
      Brain (2017). Paper; Suppl.
      This paper was selected for commentary in Brain, commentary in Epilepsy Currents, and it was chosen for F1000 recommendation. Media coverage: U of L, MetroNews, Herald.

  23. UP-DOWN cortical dynamics reflect state transitions in a bistable balanced network.
      Jercog D, Roxin A, Bartho P, Luczak A, Compte A, de la Rocha J.
      eLife (2017). Paper

  24. Chronic Mild Stress Exacerbates Severity of Experimental Autoimmune Encephalomyelitis in Association with Non-coding RNA and Metabolic Biomarkers.
      Gerrard B, Singh V, Babenko O, Gauthier I, Yong WV, Kovalchuk I, Luczak A, Metz GAS.
      Neuroscience (2017). Paper.

  25. Creation of a Deep Convolutional Auto-Encoder in Caffe.
      Turchenko V, Luczak A.
      The 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems  (IDAACS) (2017) Paper, Code.

  26. Head-down self-treatment of choking.
      Luczak A.
      Resuscitation (2016) Paper.
      Press coverage: The Huffington Post , Popular Science , NewsCaf , The News Commenter , LifeHacker , OOYUZ , , ChuanSong Curioso , .

  27. Computational Properties of the Hippocampus Increase the Efficiency of Goal-Directed Foraging through Hierarchical Reinforcement Learning.
      Chalmers E, Luczak A, Gruber A.
      Front. Comput. Neurosci. (2016) Paper.

  28. Context-Switching and Adaptation: Brain-Inspired Mechanisms for Handling Environmental Changes
      Chalmers E, Bermudez Contreras E, Robertson B, Luczak A, Gruber A.
      (proceedings of the IEEE 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver (2016) Paper.

  29. Packet-based communication in the cortex.
      Luczak A, McNaughton BL, Harris KD.
      Nature Rev Neurosci (2015) Paper; Talk; Slides
      Here we proposed Neuronal Packet Theory, which describes how information in cortex is divided in 50~500ms long packets with specific sequential structure of neuronal activity. This paper is in the top 5% of papers tracked by Altmetric.

  30. Beyond the Silence: Bilateral Somatosensory Stimulation Enhances Skilled Movement Quality and Neural Density in Intact Behaving Rats.
      Faraji J, Gomez-Palacio-Schjetnan A, Luczak A, Metz GA.
      Behavioural Brain Research (2013) Paper.

  31. Formation and reverberation of sequential neural activity patterns evoked by sensory stimulation is enhanced during cortical desynchronization.
      Bermudez Contreras E, Gomez Palacio Schjetnan A, Muhammad A, Bartho P, McNaughton BL, Kolb B, Gruber AJ, Luczak A.
      Neuron (2013) Paper.
      We found that after auditory stimulation, the same neuronal activity patterns were later spontaneously replayed.

  32. Transcranial Direct Current Stimulation in Stroke Rehabilitation – A Review of Recent Advancements.
      Gomez Palacio Schjetnan A, Faraji J, Metz GA, Tatsuno M, Luczak A.
      Stroke Research and Treatment (2013) Paper.

  33. Gating of sensory input by spontaneous cortical activity.
      Luczak A, Bartho P, Harris KD.
      J.Neurosci. (2013). Paper.
      This paper shows that in the cortex sensory information is not processed continuously but rather in form of discrete packets of spiking activity. This paper was chosen for Research Highlights in Nature Reviews Neuroscience.

  34. Consistent sequential activity across diverse forms of UP states under ketamine anesthesia.
      Luczak A, Bartho P.
      Eur. J. Neurosci. (2012) Paper.

  35. Temporal variability of the N2pc during efficient and inefficient visual search.
      Dowdall JR, Luczak A, and Tata MS.
      Neuropsychologia 50 (2012) Paper.

  36. Default activity patterns at the neocortical microcircuit level.
      Luczak A, MacLean JN.
      Front. Integr. Neurosci. 6:30 (2012) Paper.

  37. Neural correlates of auditory distraction revealed in theta-band EEG.
      Ponjavic-Conte KD, Dowdall JR, Hambrook DA, Luczak A, Tata MS.
      NeuroReport: 23 (2012) Paper.

  38. Recording Large-scale Neuronal Ensembles with Silicon Probes in the Anesthetized Rat.
      Gomez Palacio Schjetnan A, Luczak A.
      JoVE (2011). Paper, Video. This video is highly popular as evidenced by over 10,000 downloads.

  39. Measuring neuronal branching patterns using model-based approach.
      Luczak A.
      Front. Comput. Neurosci. 4:135 (2010). Paper, Code.

  40. How do neurons work together? Lessons from auditory cortex.
      Harris KD, Bartho P, Chadderton P, Curto C, de la Rocha J, Hollender L, Itskov V, Luczak A, Marguet SL, Renart A, Sakata S.
      Hearing Research, 1-17 (2010). Paper.

  41. Spontaneous events outline the realm of possible sensory responses in the auditory cortex.
      Luczak A, Barthó P, Harris KD.
      Neuron 62 (2009).  Paper.
      This paper was cited over 500 times and was highlighted as of special interest in review: Ringach DL, Curr Opin Neurobiol. 2009 and in Maass W, Curr Opin Behav Sci 2016.

  42. Population coding of tone stimuli in auditory cortex: dynamic rate vector analysis.
      Barthó P, Curto C, Luczak A, Marguet S, Harris KD.
      Eur. J. Neurosc. 30 (2009). Paper.

  43. Sequential structure of neocortical spontaneous activity in vivo.
      Luczak A, Barthó P, Marguet SL, Buzsáki G, Harris KD.
      Proc. Natl. Acad. Sci. 104 (2007). Paper.
      We described that neuronal activity can propagate as traveling waves, with consistent sequential firing pattern across waves. This paper was cited over 500 times.

  44. Spatial embedding of neuronal trees modeled by diffusive growth.
      Luczak A.
      J. Neurosci. Methods 157 (2006). Paper, Code.

  45. Spectral representation – analyzing single-unit activity in extracellularly recorded neuronal data without spike sorting.
      Luczak A, Narayanan NS.
      J. Neurosci. Methods 144 (2005). Paper , Code.

  46. Multivariate receptive field mapping in marmoset auditory cortex.
      Luczak A, Hackett T, Kajikawa Y, Laubach M.
      J. Neurosci. Methods 136 (2004). Paper, Code.

  47. Modeling stimulus-response functions in the auditory system.
      Luczak A, Hackett T, Kajikawa Y, Laubach M.
      Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference, NJIT, Newark, NJ, 2003.

  48. A cluster of workstations for on-line analyses of neurophysiological data.
      Laubach M, Arieh Y, Luczak A, Oh J, Xu Y.
      Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference, NJIT, Newark, NJ, 2003.

  49. Estimating neuronal variable importance with Random Forest.
      Oh J, Luczak A, Laubach M.
      Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference, NJIT, Newark, NJ, 2003.

  50. Model of neuronal distribution during development in rat cortex based on cellular automata
      Luczak A, Skrzat J, Trabka J.
      Proceedings of V National Conference: Modelling of Biological Systems (MBS2000), Krakow, Poland, 2000.

  51. Fractal modelling of dendritic structures as a paradigm for morphogenetic structure of neurons
      Skrzat J, Luczak A, Trabka J.
      Proceedings of V National Conference: Modelling of Biological Systems (MBS2000), Krakow, Poland, 2000.

  52. Simulation and modelling as the cognitive procedures.
      Trabka J(jun.), Trąbka J, Luczak A.
      Proceedings of V National Conference: Modelling of Biological Systems (MBS2000), Krakow, Poland, 2000.

  53. Modeling of growth and shape of neurons by the application of fractal geometry
      Luczak A.
      Proc. of the 1st European Interdisciplinary School on nonlinear Dynamics for System and Signal Analysis, EUROATTRACTOR 2000, Pabst Science Publishers, Warsaw, 2000.

  54. Parametric description of neuron shape on the basis of a generator of artificial neurons
      Luczak A, Skrzat J, Trabka J.
      Proc. of XI National Meeting – Artificial Intelligence, Siedlce, Poland, 1999.


Book chapters

  1. 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.
      Luczak A.
      Series in Computational Neuroscience. 2015, Springer, Chapter

  2. Shaping of neurons by environmental interaction; In "Dendritic computations through morphology and connectivity". Editors: Torben-Nielsen B, Remme M, Cuntz H.
      Luczak A.
      Series in Computational Neuroscience. 2014, Springer, 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.


Outreach activities:

I am past President of the Lethbridge Chapter of the Society for Neuroscience (SfN), where I was responsible for organizing multiple events to promote brain research, which engaged over 500 people annually. The main annual events included:

  • I co-organize with M. Mohajerani Satellite Symposium at the Canadian Neuroscience Meeting: Neural Signal and Image Processing: Quantitative Analysis of Neural Activity; in Montreal (2017); and also with T. Murphy in Vancouver, (2018), and with S. Prescott in Toronto (2019). Each year we have ~50 participants from across Canada, and in 2019 we expanded format of this workshop to two days to accommodate student demand.


  • CIHR Project Grant (2019-2024)
  • CIHR Priority funding: Data science (2019-2021)
  • NSERC Discovery Grant (2010-2025)
  • NSERC DG Accelerator (2015-2018, awarded to the top ~4%)
  • University of Lethbridge Research Fund (2018)