Carnegie Mellon Robotics Institute
Anoopum S. Gupta
doctoral dissertation, tech. report CMU-RI-TR-11-36, Robotics Institute, Carnegie Mellon University, September, 2011
|Sequences of neural activity representing paths in an environment are expressed in the rodent hippocampus at three distinct time scales, with different hypothesized roles in hippocampal function. As an animal moves through an environment and passes through a series of place ﬁelds, place cells activate and deactivate in sequence, at the time scale of the animal’s movement (i.e., the behavioral time scale).
Moreover, at each moment in time, as the animal’s location in the environment overlaps with the ﬁring ﬁelds of many place cells, the active place cells ﬁre in sequence during each cycle of the 4-12 Hz theta oscillation observed in the hippocampal local ﬁeld potentials (i.e., the theta time scale), such that the neural activity, in general, represents a short path that begins slightly behind the animal and ends slightly ahead of the animal. These sequences have been hypothesized to play a role in the encoding and recall of episodes of behavior.
Sequences of neural activity occurring at the third time scale are observed during both sleep and awake but restful states, when animals are paused and generally inattentive, and are associated with sharp wave ripple complexes (SWRs) observed in the hippocampal local ﬁeld potentials. During the awake state, these sequences have been shown to begin near the animal’s location and extend forward (forward replay) or backward (backward replay), and have been hypothesized to play a role in memory consolidation, path planning, and reinforcement learning.
This thesis uses a novel sequence detection method and a novel behavioral spatial decision task to study the functional signiﬁcance of theta sequences and SWR sequences. The premise of the thesis is that by investigating the behavioral content represented by these sequences, we may further our understanding of how these sequences contribute to hippocampal function.
The ﬁrst part of the thesis presents an analysis of SWR sequences or replays, revealing several novel properties of these sequences. In particular it was found that instead of preferentially representing the more recently experienced parts of the maze, as might be expected for memory consolidation, paths that were not recently experienced were more likely to be replayed. Additionally, paths that were never experienced, including shortcut paths, were observed. These observations suggest that hippocampal replay may play a role in constructing and maintaining a "cognitive map" of the environment.
The second part of the thesis investigates the properties of theta sequences. A recent study found that theta sequences extend further forward at choice points on a maze and suggested that these sequences may be partly under cognitive control. In this part of the thesis I present an analysis of theta sequences showing that there is diversity in theta sequences, with some sequences extending more forward and others beginning further backward. Furthermore, certain components of the environment are preferentially represented by theta sequences, suggesting that theta sequences may reﬂect the cognitive "chunking" of the animal’s environment.
The third part of the thesis describes a computational model of the hippocampus which explores how synaptic learning due to neural activity during navigation (i.e., theta sequences) may enable the hippocampal network to produce forward, backward, and shortcut sequences during awake rest states (i.e., SWR sequences).
Number of pages: 131
|Anoopum S. Gupta, "Behavioral Correlates of Hippocampal Neural Sequences," doctoral dissertation, tech. report CMU-RI-TR-11-36, Robotics Institute, Carnegie Mellon University, September, 2011|
author = "Anoopum S. Gupta",
title = "Behavioral Correlates of Hippocampal Neural Sequences",
booktitle = "",
school = "Robotics Institute, Carnegie Mellon University",
month = "September",
year = "2011",
address= "Pittsburgh, PA",
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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