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Modeling place field activity with hierarchical slow feature analysis.

Schönfeld F, Wiskott L - Front Comput Neurosci (2015)

Bottom Line: Simulations are based on a hierarchical Slow Feature Analysis (SFA) network topped by a principal component analysis (ICA) output layer.The slowness principle is shown to account for the main findings of the presented experimental studies.Future iterations of the model will thus have to incorporate additional information, such as path integration and grid cell activity, in order to be able to also replicate studies that take place during darkness.

View Article: PubMed Central - PubMed

Affiliation: Theory of Neural Systems Group, Institut für Neuroinformatik, Ruhr Universität Bochum Bochum, Germany.

ABSTRACT
What are the computational laws of hippocampal activity? In this paper we argue for the slowness principle as a fundamental processing paradigm behind hippocampal place cell firing. We present six different studies from the experimental literature, performed with real-life rats, that we replicated in computer simulations. Each of the chosen studies allows rodents to develop stable place fields and then examines a distinct property of the established spatial encoding: adaptation to cue relocation and removal; directional dependent firing in the linear track and open field; and morphing and scaling the environment itself. Simulations are based on a hierarchical Slow Feature Analysis (SFA) network topped by a principal component analysis (ICA) output layer. The slowness principle is shown to account for the main findings of the presented experimental studies. The SFA network generates its responses using raw visual input only, which adds to its biological plausibility but requires experiments performed in light conditions. Future iterations of the model will thus have to incorporate additional information, such as path integration and grid cell activity, in order to be able to also replicate studies that take place during darkness.

No MeSH data available.


Related in: MedlinePlus

Changes in place cell firing patterns after the removal of previously learned local cues (three white cue cards fixed to the walls). Left column: activity from a single cell recording during cue card removal [Reprinted by permission from APA: Behavioral Neuroscience (Hetherington and Shapiro, 1997)]. Middle column: activity of the closest matching cell as produced in our simulations. Right column: the same cell activity as depicted by the center column formatted with the jet-scale heat map used throughout this work for coherence.
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Figure 8: Changes in place cell firing patterns after the removal of previously learned local cues (three white cue cards fixed to the walls). Left column: activity from a single cell recording during cue card removal [Reprinted by permission from APA: Behavioral Neuroscience (Hetherington and Shapiro, 1997)]. Middle column: activity of the closest matching cell as produced in our simulations. Right column: the same cell activity as depicted by the center column formatted with the jet-scale heat map used throughout this work for coherence.

Mentions: When removing one of three available cue cards in a square box, Hetherington and Shapiro (1997) report that place field firing patterns change depending on their relation to the removed cue. Fields closer to a missing cue have been observed to display a greater reduction in firing rate than fields near a cue that is still available. Figure 8 shows the firing patterns of a cell in the original experimental study (Hetherington and Shapiro, 1997) and a side by side comparison with the activity of a similarly located place field in our simulation framework. Figure 9 shows the activity of a range of our simulated cells as well as the reaction of the network to the removal of two cue cards at once. As can be seen, our model replicates the findings of Hetherington and Shapiro (1997) and place fields close to removed cue cards tend to be affected more than fields located further away. We also observed cells that reacted in different ways though, such as developing additional, usually symmetric, firing fields; cells that try to compensate the loss of their associated cue by relocating to a position featuring a similar visual experience (see last row for an example); and cells that were specifically anchored to a single cue card. Such fields ignore the absence of cue cards they were not bound to and almost vanish upon removal of their associated cue. Furthermore, removing the smallest cue affects cell activity the least, with only place fields located close to the missing cue displaying a significant reduction in firing rate. Removal of the medium sized cue card leads to nearby firing fields displaying a distinctive loss in firing activity, while fields positioned further away are impacted considerably less. Removing the largest cue card has the largest overall effect on place field firing. All fields display an explicit reduction in firing activity with fields located near the cue being the ones affected the most.


Modeling place field activity with hierarchical slow feature analysis.

Schönfeld F, Wiskott L - Front Comput Neurosci (2015)

Changes in place cell firing patterns after the removal of previously learned local cues (three white cue cards fixed to the walls). Left column: activity from a single cell recording during cue card removal [Reprinted by permission from APA: Behavioral Neuroscience (Hetherington and Shapiro, 1997)]. Middle column: activity of the closest matching cell as produced in our simulations. Right column: the same cell activity as depicted by the center column formatted with the jet-scale heat map used throughout this work for coherence.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4441153&req=5

Figure 8: Changes in place cell firing patterns after the removal of previously learned local cues (three white cue cards fixed to the walls). Left column: activity from a single cell recording during cue card removal [Reprinted by permission from APA: Behavioral Neuroscience (Hetherington and Shapiro, 1997)]. Middle column: activity of the closest matching cell as produced in our simulations. Right column: the same cell activity as depicted by the center column formatted with the jet-scale heat map used throughout this work for coherence.
Mentions: When removing one of three available cue cards in a square box, Hetherington and Shapiro (1997) report that place field firing patterns change depending on their relation to the removed cue. Fields closer to a missing cue have been observed to display a greater reduction in firing rate than fields near a cue that is still available. Figure 8 shows the firing patterns of a cell in the original experimental study (Hetherington and Shapiro, 1997) and a side by side comparison with the activity of a similarly located place field in our simulation framework. Figure 9 shows the activity of a range of our simulated cells as well as the reaction of the network to the removal of two cue cards at once. As can be seen, our model replicates the findings of Hetherington and Shapiro (1997) and place fields close to removed cue cards tend to be affected more than fields located further away. We also observed cells that reacted in different ways though, such as developing additional, usually symmetric, firing fields; cells that try to compensate the loss of their associated cue by relocating to a position featuring a similar visual experience (see last row for an example); and cells that were specifically anchored to a single cue card. Such fields ignore the absence of cue cards they were not bound to and almost vanish upon removal of their associated cue. Furthermore, removing the smallest cue affects cell activity the least, with only place fields located close to the missing cue displaying a significant reduction in firing rate. Removal of the medium sized cue card leads to nearby firing fields displaying a distinctive loss in firing activity, while fields positioned further away are impacted considerably less. Removing the largest cue card has the largest overall effect on place field firing. All fields display an explicit reduction in firing activity with fields located near the cue being the ones affected the most.

Bottom Line: Simulations are based on a hierarchical Slow Feature Analysis (SFA) network topped by a principal component analysis (ICA) output layer.The slowness principle is shown to account for the main findings of the presented experimental studies.Future iterations of the model will thus have to incorporate additional information, such as path integration and grid cell activity, in order to be able to also replicate studies that take place during darkness.

View Article: PubMed Central - PubMed

Affiliation: Theory of Neural Systems Group, Institut für Neuroinformatik, Ruhr Universität Bochum Bochum, Germany.

ABSTRACT
What are the computational laws of hippocampal activity? In this paper we argue for the slowness principle as a fundamental processing paradigm behind hippocampal place cell firing. We present six different studies from the experimental literature, performed with real-life rats, that we replicated in computer simulations. Each of the chosen studies allows rodents to develop stable place fields and then examines a distinct property of the established spatial encoding: adaptation to cue relocation and removal; directional dependent firing in the linear track and open field; and morphing and scaling the environment itself. Simulations are based on a hierarchical Slow Feature Analysis (SFA) network topped by a principal component analysis (ICA) output layer. The slowness principle is shown to account for the main findings of the presented experimental studies. The SFA network generates its responses using raw visual input only, which adds to its biological plausibility but requires experiments performed in light conditions. Future iterations of the model will thus have to incorporate additional information, such as path integration and grid cell activity, in order to be able to also replicate studies that take place during darkness.

No MeSH data available.


Related in: MedlinePlus