Browsing by Author "Ackermann, Etienne Rudolph"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Latent variable models for hippocampal sequence analysis
    (2019-06-27) Ackermann, Etienne Rudolph; Kemere, Caleb
    Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Indeed, the activity of ensembles of neurons within the hippocampus is thought to enable memory formation, storage, recall, and even decision making. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. Notably, these PBEs occur during times of inactivity, so that their representations cannot easily be matched with observable animal behavior. In my thesis, I present an approach to uncover temporal structure within hippocampal output patterns during PBEs. More specifically, I use hidden Markov models (HMMs) to study PBEs observed in rats during exploration of both linear tracks and open fields, and I demonstrate that estimated models are consistent with a spatial map of the environment. Moreover, I demonstrate how the model can be used to identify hippocampal replay without recourse to the place code. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Moreover, by forming models independent of animal behavior, I lay the groundwork for studies of non-spatial memory. Next, I present a new model, the "clusterless" switching Poisson hidden Markov model, which extends my work on HMMs of PBEs to the case where we only have multiunit (unsorted) spikes. Indeed, spike sorting is challenging, time-consuming, often subjective (not reproducible), and throws away potentially valuable information from unsorted spikes, as well as our certainty about the cluster assignments. It has previously been shown that we can often do just as well, or in some cases even better, if we forego the spike sorting process altogether, and work directly with the unsorted data. Consequently, my clusterless HMM will enable us to combine the benefits of unsupervised learning for internally generated neural activity, with the benefits of clusterless approaches (more data leading to higher fidelity, especially at fine temporal scales, and additional probabilistic / soft information to exploit). I demonstrate the model's ability to recover model parameters for simulated data, and show that it is able to learn a spatially-consistent representation of the environment from real experimental data.