Centre for Computational Neuroscience and Robotics - CCNR

Alergic Seminars

Alergic seminars are the CCNR seminar series, co-organised with the be.AI Doctoral Scholarship Program.

11/18/2021 | Alicia Garrido Peña

Combining experimental recordings, theoretical models and novel stimulation techniques to study sequential neural dynamics in Lymnaea Stagnalis.



During this talk I will go through the work I am doing in Universidad Autónoma de Madrid, during my PhD at the Computational Neuroscience Group (www.ii.uam.es/~gnb/ , github.com/GNB-UAM).

My research combines computational neuroscience, electrophysiological recordings and novel stimulation techniques to explore neural dynamics in invertebrates neurons. In particular, we focus on the identification of robust sequential dynamics and characterization of the variability of the time intervals that build that sequences. For that we are using the widely studied animal Lymnaea stagnalis, a pond snail whose neurons are well described and easy to access.

On the one hand we study the sequential activation of the Central Pattern Generator (CPG) of Lymnaea buccal ganglia. CPGs are neural circuits that generate and coordinate motor movements by producing rhythms composed of patterned sequences of activation in their constituent neurons. These robust rhythms are yet flexible and the time intervals that build the neural sequences can adapt as a functiopof the behavioral context. We have recently revealed the presence of robust dynamical invariants in the form of cycle-by-cycle linear relationships between two of the intervals of the crustacean pyloric CPG sequence and the period [1]. To show the universality of these invariants, we have explored them in a model of the CPG in the Lymnaea [2] and showed that the invariants appear among specific intervals of the sequences generated by the buccal circuit model [3]. We are also working to illustrate this in experimental data from intracellular recordings of the rhythm. 

We are also exploring the effect of a near-infrared laser in single neurons. It is a non-invasive technique that has been shown to modify neural activity and is becoming popular in medical treatments [4,5]. We are studying its exact mode of action in the neurons of Lymnaea, specifically in the generation of action potentials as recorded with intracellular electrodes. To narrow down the candidates producing this effect, we rely on conductance-based models [2,6] to contrast the experimental results against the theoretical ones. So far we saw how IR laser effectively changes spike waveform with a fully reversible effect. Our aim is to describe in detail the source of the observed outcome and explore the possibilities for using this technique as a stimulation method to assess neural sequences and associated biomedical applications.


1. Elices, I., Levi, R., Arroyo, D., Rodriguez, F. B., and Varona, P. (2019). Robust dynamical invariants in sequential neural activity. Sci. Rep. 9, 9048. doi:10.1038/s41598-019-44953-2.2. Vavoulis, D. V., Straub, V. A., Kemenes, I., Kemenes, G., Feng, J., & Benjamin, P. R. (2007). Dynamic control of a central pattern generator circuit: A computational model of the snail feeding network. European Journal of Neuroscience, 25(9), 2805–2818. https://doi.org/10.1111/j.1460-9568.2007.05517.x3. Garrido-Peña, A., Elices, I., Varona, P. (2021). Characterization of interval variability in the sequential activity of a central pattern generator model, Neurocomputing, Volume 461, 21 October 2021, Pages 667-678 , doi: https://doi.org/10.1016/j.neucom.2020.08.0934. Saucedo, C. L., Courtois, E. C., Wade, Z. S., Kelley, M. N., Kheradbin, N., Barrett, D. W., & Gonzalez-Lima, F. (2021). Transcranial laser stimulation: Mitochondrial and cerebrovascular effects in younger and older healthy adults. Brain Stimulation, 14(2), 440–449. https://doi.org/10.1016/j.brs.2021.02.0115. Liang, S., Yang, F., Zhou, C., Wang, Y., Li, S., Sun, C. K., Puglisi, J. L., Bers, D., Sun, C., & Zheng, J. (2009). Temperature-dependent activation of neurons by continuous near-infrared laser. Cell Biochemistry and Biophysics, 53(1), 33–42. https://doi.org/10.1007/s12013-008-9035-26. Vavoulis, D. V., Nikitin, E. S., Kemenes, I., Marra, V., Feng, J., Benjamin, P. R., & Kemenes, G. (2010). Balanced plasticity and stability of the electrical properties of a molluscan modulatory interneuron after classical conditioning: A computational study. Frontiers in Behavioral Neuroscience, 4, 19. https://doi.org/10.3389/fnbeh.2010.00019

11/04/2021 | Thomas Nowotny

GPU enhanced Neuronal Networks and the Role of Ephaptic Interactions in Drosophila Olfactory Sensilla


In today's talk I will present recent results in two areas of our work. In part one, I will discuss recent advances for our GPU enhanced Neuronal Networks (GeNN, https://github.com/genn-team/genn) framework, driven by Jamie Knight. We have identified and improved upon several challenges for the efficiency of the simulations, their initialisation, and their memory footprint. These improvements have allowed us to compete favourably with other technologies for spiking neural network simulations [1,2,3].
In the second part of the talk I will present work of Mario Pannunzi on the role of ephaptic interactions in the olfactory sensilla of the fruit fly, Drosophila. In brief, olfactory receptor neurons are paired in olfactory hairs (sensilla) on the antenna of Drosophila in a stereotypic way. Within the sensilla, the olfactory receptor neurons can interact electrically through so-called ephaptic interactions. Researchers have hypothesized three possible roles for these interactions, 1) to improve the encoding of odour ratios in mixtures, 2) to support the process of odour source separation in complex turbulent odour plumes, and 3) to increase the dynamic range of olfactory receptor neurons. In a computational model we find support for first two hypotheses but not for the third [4].


  1. J. C. Knight, T. Nowotny (2018) GPUs outperform current HPC and neuromorphic solutions in terms of speed and energy when simulating a highly-connected cortical model. Front Neurosci 12:941. doi: 10.3389/fnins.2018.00941
  2. J. C. Knight and T. Nowotny (2021) Larger GPU-accelerated brain simulations with procedural connectivity. Nat Comput Sci 1(2): 136-42. doi: 10.1038/s43588-020-00022-7, free full text

  3. J. C. Knight, A. Komissarov, T. Nowotny (2021) PyGeNN: A Python Library for GPU-Enhanced Neural Networks. Frontiers in Neuroinformatics 15: 10. doi: 10.3389/fninf.2021.659005
  4. M. Pannunzi, T. Nowotny (2021) Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies, PLoS Computational Biology, in press, preprint: https://www.biorxiv.org/content/10.1101/2020.07.23.217216v3