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«Anoopum S. Gupta CMU-RI-TR-11-36 Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Robotics The ...»

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Behavioral Correlates of Hippocampal Neural Sequences

Anoopum S. Gupta

CMU-RI-TR-11-36

Submitted in partial fulfillment of the requirements for

the degree of Doctor of Philosophy in Robotics

The Robotics Institute

School of Computer Science

Carnegie Mellon University

Pittsburgh, Pennsylvania 15213

September 2011

Thesis Committee

David S. Touretzky (Chair)

Tai Sing Lee

Reid Simmons

George Stetten

A. David Redish, University of Minnesota

Copyright c 2011 by Anoopum S. Gupta. All rights reserved.

This work was supported by NIH National Research Service Award F30 MH-091821, NSF IGERT DGE-0549352, NIH T32 NS007433, NIM R01 MH-080318, and the Pennsylvania Tobacco Settlement Fund.

Abstract 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).

Acknowledgements I have been very fortunate to be surrounded by mentors, colleagues, and friends who have enriched my training and without whom this work would not have been possible. I am grateful to my advisor, David Touretzky, for his endless wisdom and guidance over the last four years. Dr. Touretzky has not only supported the research in this thesis, but has mentored me in all aspects of academic life. I want to thank David Redish for welcoming me into the Redish lab, teaching me the ways of electrophysiology, and for his constant mentorship throughout my training. Dr. Redish has been like a second thesis advisor to me and I am deeply thankful for all the time he has spent helping me grow as a scientist. George Stetten, Tai Sing Lee, and Reid Simmons have each contributed a unique perspective to the work in this thesis, and I am very thankful for their support and advice.

My experience as a graduate student would not have been the same without my interactions with the Redish lab: Matthijs van der Meer, Adam Johnson, Adam Steiner, John Ferguson, Chris Boldt, Adam Vogel, Nate Powell, Andrew Wikenheiser, and Andy Papale.





In particular, I want to thank Matt for all the impromptu discussions and for his invaluable help learning how to perform experiments in the Redish lab.

Thanks to Mark Fuhs, Andrew Maurer, Piotr Dollar, Rob Kass, Bruce McNaughton, Michael Hasselmo, the CMU Robotics community, the Pitt/CMU CNBC and MSTP, and friends and colleagues from NS&B 2009 for collaborations and discussions that have enriched my experience as a graduate student.

Thank you to Rachelle, Boz, and my friends for giving me a life outside of research. Finally, thank you to Mom, Dad, Sumi, Dan, Apurve, and Nitika for all their love and guidance.

Contents

–  –  –

1 Introduction The work described in this thesis was performed with the goal of understanding how the spiking of a population of neurons in a particular region of the brain may contribute to the region’s cognitive functions. The link between neural activity and cognition can be made in two steps. By observing the spiking of a particular neuron with respect to an animal’s behavior (e.g., movement in an environment), one can determine if the activity is informative (predictive) of the behavioral variable (the animal’s location). Methods can then be used to decode the behavioral content represented by the neural activity. These methods allow investigators to determine what the brain is representing purely from the neural activity.

And since cognitive functions such as memory and planning involve representing information that is distinct from the animal’s immediate sensory/behavioral state (i.e., non-local information), such techniques allow us to study how these processes are expressed in the brain.

Spiking neurons are a unique and advantageous window into brain function at multiple levels. For one, the ability to discern the activity of an individual neuron (thought to be the fundamental processing unit of the brain) allows for the extraction of information in systems in which neurons very near to one another in the brain are representing different behavioral or cognitive information. Without the ability to distinguish individual neuron activity, the information would be merged and lost in such systems. Furthermore, it has been shown that behavioral information is sometimes represented at fast time scales that require recording methods with fine temporal precision in order to resolve. The ability to record neural activity at fast time scales additionally provides us with a connection between the behavioral information represented in spike trains and neural plasticity. Spike timing dependent plasticity (STDP) has been shown to occur when the pre and post synaptic cells fire within 80 ms of one another. Recording from populations of neurons at sub-millisecond time scales enables the study of neural sequences to determine how sequences of behavioral information may be stored in the synaptic connections in the brain.

The work described in this thesis studies the behavioral information represented by a population of spiking neurons in the CA1 region of the rodent hippocampus during the performance of a spatial navigation decision task. The thesis focuses on the hippocampus because it is known to play an important role in episodic memory and spatial navigation, and because it has recently been implicated in higher cognitive functions such as self-projection and imagination. The rodent hippocampus is an extremely informative system because it is relatively easy to access with electrodes, and because the firing of hippocampal pyramidal cells has a clear behavioral correlate: the animal’s location in an environment. At any given time as an animal moves through an environment, the population of pyramidal cells in the hippocampus is representing the animal’s physical location. The strong correlation between neural activity and the animal’s physical location on the maze enables accurate decoding of the animal’s location from subsequent neural activity alone. It also allows us to determine (via decoding) if and when non-local information (e.g., a different location or path on the maze) is being represented, as has been found during pausing at a choice point, and during sleep and awake rest states. These studies have suggested that the nonlocal representations play a role in memory consolidation, decision making, reinforcement learning, and planning. In this thesis I explore the content of non-local representations on a novel task that has several advantages over previous studies, allowing me to test some of the suggested functions of non-local representations. Additionally, I construct a computational model to explore how the known synaptic learning rules in the hippocampus may enable the expression of some of the non-local representations that are observed.

The thesis is organized into eight additional chapters. Chapter 2 provides an overview of hippocampal function and physiology, while Chapter 3 focuses on experimental work on neural sequences observed in the hippocampus. Chapter 4 surveys different decoding methods for characterizing sequences of neural activity, and then describes the decoding method developed here and the advantages it imparts given data constraints. Chapter 5 describes the general methods used in the experimental studies in this thesis. Chapter 6 and Chapter 7 present original experimental work on neural sequences recorded from the hippocampus as animals are performing a novel behavioral task. Chapter 6 presents an analysis of neural sequences as animals are awake, but paused and resting at feeder locations on the maze. Chapter 7 presents an analysis of neural sequences as animals are actively navigating the maze to reach the feeder locations. Chapter 8 describes a computational model that explores how the backward and shortcut sequences presented in Chapter 6 may be produced, taking into account the anatomical and physiological constraints of the hippocampal network. Finally, Chapter 9 summarizes the advances made in this thesis and outlines important future work.

2 Hippocampal function and physiology Hippocampal roles in spatial navigation and episodic memory The hippocampus has been long known to play an important role in spatial navigation (O’Keefe and Nadel, 1978; Morris et al., 1982; Redish, 1999) and encoding the memory of autobiographical events (i.e., episodic memory, Scoville and Milner, 1957; Squire, 1992;

Eichenbaum et al., 1999; Tulving, 2002). Some of the first evidence of hippocampal involvement in episodic memory came from studies of patients who had lesions of the hippocampus and surrounding neural structures. Studies of the famous patient HM, who had a bilateral hippocampal resection due to intractable epilepsy, revealed a specific deficit in declarative memory (i.e., the acquisition of new fact and event memory). HM’s ability to form new memories of autobiographical experiences was lost (anterograde amnesia, Scoville and Milner, 1957). The memory of recent experiences was also impaired (retrograde amnesia), but past experiences and childhood memories were preserved, pointing to a role for the hippocampus in encoding memories, but not in the long term storage of episodic memory (Milner et al., 1968). Numerous studies performed on HM and other patients over a half century have revealed that hippocampal lesions specifically impair episodic memory and that other types of memories, including working memory and procedural memory (e.g., delay conditioning and habit learning) are left intact (Squire and Zola-Morgan, 1991).

Similarly, lesion studies in animals have elucidated the role of the hippocampus in spatial navigation. Morris et al. (1982), using a novel experimental paradigm (the now famous Morris water maze), demonstrated that animals with hippocampal lesions were impaired in their ability to learn a spatial navigation task. The water maze task involved a cylindrical tank of opaque water, with a hidden platform located at a fixed location in the tank, just beneath the water. Rodents were dropped in the tank, and finding the cold water unpleasant, searched for the hidden platform. Cues were present in the room such that normal rats quickly learned the location of the hidden platform and were able to navigate direct routes to the platform even when initially placed at a novel location in the tank. Hippocampus lesioned animals had no trouble taking direct routes to a visible platform in the tank, but could not learn to take direct routes to a hidden platform, thus demonstrating the impairment in spatial memory. This experiment supported the idea that the hippocampus is involved in the formation and use of cognitive maps (i.e., a mental representation of the environment) to flexibly navigate or take novel paths through space (Tolman, 1948; O’Keefe and Nadel, 1978). Several other experiments have supported a role for the hippocampus in enabling flexible navigation (Tolman et al., 1946; Nadel et al., 1975; Packard and McGaugh, 1996).



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