Nikolas Andreakos is a PhD candidate at the University of Lincoln, who is working on developing computational models of associative memory formation and recognition in the mammalian hippocampus.
Recently Nikolas attended the 13th International Conference on Brain Informatics (BI 2020). Due to the current travel restrictions, this year’s conference, which was scheduled to take place on 19th September 2020 in Padova, Italy, was moved online.
About Brain Informatics 2020
The Brain Informatics (BI) conference series has established itself as the world’s premier research forum on Brain Informatics, which is an emerging interdisciplinary and multidisciplinary research field with joint efforts from neuroscience, cognitive science, medicine and life sciences, data science, artificial intelligence, neuroimaging technologies, and information and communication technologies.
The 13th International Conference on Brain Informatics (BI2020) provided a premier international forum to bring together researchers and practitioners from diverse fields for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences on Brain Informatics research, brain-inspired technologies and brain/mental health applications.
The theme of BI2020 was: Brain Informatics in the Virtual World.
The BI2020 solicits high-quality original research and application papers (both full paper and abstract submissions). Relevant topics included but were not limited to:
- Track 1: Cognitive and Computational Foundations of Brain Science
- Track 2: Human Information Processing Systems
- Track 3: Brain Big Data Analytics, Curation and Management
- Track 4: Informatics Paradigms for Brain and Mental Health Research
- Track 5: Brain-Machine Intelligence and Brain-Inspired Computing
Nikolas presented his research Recall performance improvement in a bio-inspired model of the mammalian hippocampus.
Mammalian hippocampus is involved in short-term formation of declarative memories. We employed a bio-inspired neural model of hippocampal CA1 region consisting of a zoo of excitatory and inhibitory cells. Cells’ firing was timed to a theta oscillation paced by two distinct neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the other to the peak of theta. To systematically evaluate the model’s recall performance against number of stored patterns, overlaps and ‘active cells per pattern’, its cells were driven by a non-specific excitatory input to their dendrites. This excitatory input to model excitatory cells provided context and timing information for retrieval of previously stored memory patterns. Inhibition to excitatory cells’ dendrites acted as a non-specific global threshold machine that removed spurious activity during recall. Out of the three models tested, ‘model 1’ recall quality was excellent across all conditions. ‘Model 2’ recall was the worst. The number of ‘active cells per pattern’ had a massive effect on network recall quality regardless of how many patterns were stored in it. As ‘active cells per pattern’ decreased, network’s memory capacity increased, interference effects between stored patterns decreased, and recall quality improved. Key finding was that increased firing rate of an inhibitory cell inhibiting a network of excitatory cells has a better success at removing spurious activity at the network level and improving recall quality than increasing the synaptic strength of the same inhibitory cell inhibiting the same network of excitatory cells, while keeping its firing rate fixed.
When asked about his experience, Nikolas said:
“I really enjoyed the conference and learned a lot. It was a valuable and absorbing experience for me. The atmosphere was friendly. I shared my research and my experience with other attendants, and exchange ideas which would help me to improve my existing work”.