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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

Spatial consistency of the overall cell population during cue removal. Each plot depicts cell activity in a different version of the familiar environment holding all three cue cards. Inlets in the top right corner depict which cues (marked red) were still available during sampling of the environment. The order from smallest to largest change in the visual environment is indicated by grew arrows in the bottom right corner of each plot.
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Figure 11: Spatial consistency of the overall cell population during cue removal. Each plot depicts cell activity in a different version of the familiar environment holding all three cue cards. Inlets in the top right corner depict which cues (marked red) were still available during sampling of the environment. The order from smallest to largest change in the visual environment is indicated by grew arrows in the bottom right corner of each plot.

Mentions: Data analysis for all cases shows directional consistency to be higher for environments closer to the initial, familiar state. With the removal of the learned cues, orientation becomes more and correlation values drop accordingly—with larger cues again influencing activity patterns more than smaller cues (Figure 10). The spatial consistency of the cell population can be seen to be influenced in a similar way: cells tend to either lose distinct firing areas or acquire another one, depending on which subset of cues is removed (Figure 11).


Modeling place field activity with hierarchical slow feature analysis.

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

Spatial consistency of the overall cell population during cue removal. Each plot depicts cell activity in a different version of the familiar environment holding all three cue cards. Inlets in the top right corner depict which cues (marked red) were still available during sampling of the environment. The order from smallest to largest change in the visual environment is indicated by grew arrows in the bottom right corner of each plot.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 11: Spatial consistency of the overall cell population during cue removal. Each plot depicts cell activity in a different version of the familiar environment holding all three cue cards. Inlets in the top right corner depict which cues (marked red) were still available during sampling of the environment. The order from smallest to largest change in the visual environment is indicated by grew arrows in the bottom right corner of each plot.
Mentions: Data analysis for all cases shows directional consistency to be higher for environments closer to the initial, familiar state. With the removal of the learned cues, orientation becomes more and correlation values drop accordingly—with larger cues again influencing activity patterns more than smaller cues (Figure 10). The spatial consistency of the cell population can be seen to be influenced in a similar way: cells tend to either lose distinct firing areas or acquire another one, depending on which subset of cues is removed (Figure 11).

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