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 30th Annual Computational Neuroscience Meeting (CNS*2021). Due to the current travel restrictions, this year’s conference was moved online from 3rd to 7th of July 2021.
The purpose of the Organization for Computational Neurosciences is to create a scientific and educational forum for students, scientists, other professionals, and the general public to learn about, to share, contribute to, and advance the state of knowledge in computational neuroscience.
Computational neuroscience combines mathematical analyses and computer simulations with experimental neuroscience, to develop a principled understanding of the workings of nervous systems and apply it in a wide range of technologies.
The Organization for Computational Neurosciences promotes meetings and courses in computational neuroscience and organizes the Annual CNS Meeting which serves as a forum for young scientists to present their work and to interact with senior leaders in the field.
Nikolas presented his research Modelling the effects of perforant path in the recall performance of a CA1 microcircuit with excitatory and inhibitory neurons.
From recollecting childhood memories to recalling if we turn off the oven before we left the house, memory defines who we are. Losing it can be very harmful to our survival. Recently we quantitatively investigated the biophysical mechanisms leading to memory recall improvement of a computational CA1 microcircuit model of the hippocampus . In the present study, we investigated the synergistic effects of the EC excitatory input (sensory input) and the CA3 excitatory input (contextual information) on the recall performance of the CA1 microcircuit. Our results showed that when the EC input was exactly the same as the CA3 input then the recall performance of our model was strengthened. When the two inputs were dissimilar (degree similarity: 40% – 0%), then the recall performance was reduced. These results were positively correlated with how many “active cells” represented a memory pattern. When the number of active cells increased and the degree of similarity between the two inputs decreased, then the recall performance of the model was reduced. The latter finding confirms previous results of ours where the number of cells coding a piece of information plays a significant role in the recall performance of our model.
1. Andreakos, N., Yue, S. & Cutsuridis, V. Quantitative investigation of memory recall performance of a computational microcircuit model of the hippocampus. Brain Inf 8, 9 (2021). https://doi.org/10.1186/s40708-021-00131-7