UC Irvine

The hippocampus plays a crucial role in organizing the memory of daily events, yet unraveling its mechanisms poses challenges. Decoding the information encoded in the hippocampus is particularly challenging due to sparse neuron activity in non-spatial tasks. However, accurate decoding is crucial for understanding how the hippocampus represents and processes information. This work focuses on decoding neural activity using multivariate point processes, specifically Poisson and Hawkes processes. The first two chapters concentrate on Hawkes processes, which are well-suited for modeling history-dependent phenomena characterized by clustered events, such as the spiking activity in the brain. The study introduces several self-exciting/inhibiting Hawkes process models that effectively capture the interactions among an ensemble of neurons in the hippocampus of rats. The study demonstrates that this approach enables more accurate decoding with significantly lower computational complexity. In the final chapter, we present two novel models for encoding and de- coding neural activity using non-homogeneous Poisson processes. The encoding model captures neuronal dynamics in response to stimuli, revealing temporal variations in neuronal activity over time. On the other hand, the decoding model focuses on decoding the underlying stimulus patterns from the spike train data, leveraging the estimated firing rates from the encoding model. These models demonstrate improved decoding accuracy, emphasizing the importance of incorporating temporal dynamics in decoding stimulus patterns from neural activity.