P190 Simulating healthy and diseased behaviour using spiking neurons
Mavritsaki E.1,3, Klein, J.1, Porwal, N.1, Allen, H.A.2, Bowman, H.3, Amanatidou, V.4, Cook, A.1, Clibbens, J.1and Lintern, M.1
1College of Psychology, Birmingham City University, Birmingham, UK
2School of Psychology, University of Nottingham, Nottingham, UK
3School of Psychology, University of Birmingham, Birmingham, UK
4Worcestershire Health and Care Trust, UK
*Email: eirini.mavritsaki@bcu.ac.uk
Introduction
Spiking neural networks have proven highly effective in simulating both healthy and diseased neural behaviour. They offer researchers the opportunity to simultaneously study behaviour and understand its relationship with the underlying biological properties of the system. The approach is particularly valuable as these networks accurately mimic real neuronal communication, providing a more biologically accurate model compared to traditional methods, allowing researchers to analyse time-dependent patterns and providing deeper insights into neural dynamics and cognitive processes. Consequently, spiking neural networks have become an invaluable tool for advancing brain studies and neurological research. In this work, we present two studies utilizing spiking neural networks extending our previous work using the spiking Search over Time and Space (sSoTS) model.
Methods
The sSoTS model is a spiking neural model incorporating a fast excitatory AMPA recurrent current, a slow excitatory NMDA current, an inhibitory GABA current, and aIAHPslow[Ca+]activatedK+current.We built upon our previous research in visual search (Mavritsaki et al., 2011; Mavritsaki & Humphreys, 2016) to simulate behavioural findings in our lab in attention between adults, children and children that score high in Conners 3AI, testing ADHD. We also build upon our previous Alzheimer’s work (Mavritsaki et al., 2019) to simulate N400 and P600 components in the semantic category judgment task (Olichney et al., 2000), which has been used to track ERP changes in patients progressing through MCI to mild AD. Please see figure 1.
Results
Results from our visual search paradigm demonstrate that reducing coupling between neurons in the model successfully simulates the differences between adults and children. Furthermore, our findings suggest that temporal binding between feature items may be a key mechanism underlying differences observed between healthy children and those scoring high on the Conners 3AI test, as reducing this parameter in the model reproduced the observed differences. In our Alzheimer's work, we simulated the biomarkers found with the N400 and P600 ERP components by modelling the semantic category judgment task and modifying parameters related to pathological ionic, neurotransmitter, and atrophy modulations.
Discussion
Results from both studies demonstrate the importance of using spiking neural networks in computational modelling, as they provide valuable insights into brain functions, link different methodologies, and help understand changes that occur in diseased brains. Our Alzheimer's work shows that the disease's pathology can be measured through N400 and P600 congruency effects, thus validating ERPs as biomarkers for AD. Our visual search and ADHD work identifies the crucial role of binding in visual search and provides valuable insights into the ADHD condition that can support updates to the diagnostic criteria for ADHD.
Figure 1. The top part of the figure illustrates the key neuronal properties of the spiking neural network model. The bottom left panel shows the network connectivity implemented to simulate the semantic category judgment task in our Alzheimer's disease study, while the bottom right panel depicts the neural network configuration used to simulate visual search task performance in our ADHD behavioural study.
Acknowledgements
The computations described in this paper were performed using the University of Birmingham's BEAR Cloud service, which provides flexible resource for intensive computational work to the University's research community. Seehttp://www.birmingham.ac.uk/bearfor more details.
References
Mavritsaki, E., Bowman, H., & Su, L. (2019). Springer Intern.Publishing. https://doi.org/10.1007/978-3-030-18830-6_11
Mavritsaki, E., Heinke, D., Allen, H., Deco, G., & Humphreys, G. W. (2011). Bridging the Gap Between Physiology and Behavior:Psych.l Review,118(1), 3–41. https://doi.org/10.1037/A0021868
Mavritsaki, E., & Humphreys, G. (2016).Journal of Cognitive Neuroscience,28(10). https://doi.org/10.1162/jocn_a_00984
Olichney, J. M., Van Petten, C., Paller, K. A., Salmon, D. P., Iragui, V. J., & Kutas, M. (2000).Brain,123(9), 1948–1963. https://doi.org/10.1093/brain/123.9.1948
