Exploring the animal GPS system: a machine learning approach to study the hippocampal function
Name
Zurab Bzhalava
Abstract
The 2014 Nobel prize in Physiology was awarded to Dr. John M. O’Keefe, Dr. May-Britt Moser and Dr. Edvard I for discovering particular cells in the brain that provide the sense of place and navigation. These discoveries suggest that the brain creates internal map-like representation of the environment which helps us recognize familiar places and navigate well. In this thesis, we used a computational approach to study the animal "GPS" system. In particular, we set to compare how well different machine learning algorithms are able to predict a rat's position just based on its hippocampal neural activity. Methods compared included Random Forest, Support Vector Machines, k-Nearest Neighbors, and several sparse linear regression algorithms. Data was obtained multi-neuron electrophysiological data recorded from the Buzsaki lab in New York, and we focus on the activity of rat hippocampus, the brain region where most the place cells have been identified.
In a first step, we divided the experimental arena into 4 blocks and tried to classify in which one of those blocks the rat was at a given time. In this case, we found that Random Forest gave the best accuracy which was 57.8%, well beyond the chance level. However, in some particular regions of the arena, Support Vector Machine was sometimes better than Random Forest. For the next step, we made the classification problem even harder by dividing the arena into 16 blocks. Random Forest and SVM produced highly significant results with 38% and 37% accuracy respectively (random classifier accuracy would be approximately ~11%). We also used K-Nearest Neighbors for both classification problems but its accuracy was less in both cases than the above mentioned algorithms. Since the rat position is a continuous variable we also considered the continuous prediction problem. Most regression algorithms we analyzed (Ridge Regression, LASSO, Elastic Net) provided results near chance level while Random Forest outperformed the algorithms and gave the best results in this case.
Furthermore, we analysed data recorded from an experiment where rats were trained to choose left or right direction in a 8-shaped maze while they were running in a wheel. In this case we perform a dimensionality reduction of the neuronal data to visualize its dynamics during the decision time. We also identified and provided plots of episodic cells (neurons who are more active at particular times in the task) which might contribute to the sense of time and create episodic memory. Also, we visualized neuronal trajectories while animal makes decisions in order to predict its future decision.
In conclusion, from the algorithms we analysed Random Forest gave the best accuracy while predicting a rat's location. This might also indicate that the information about rat location is contained in non-linear patterns of neuronal activity, which linear regression methods were unable to extract. In future research we plan to decode a rat position using a method more similar to the brain own mechanisms such as neural networks, which as Random Forest can detect non-linear patterns. More generally, the pipelines developed during this thesis to handle the complex pre-processing, feature extraction, and visualization of the dataset will set the basis for future studies on hippocampal dynamics by the group of computational neuroscience in the University of Tartu.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Raul Vicente Zafra
Defence year
2015