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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

Firing activity of a range of cells while traversing a narrow linear track in both directions. Top row: cell activity as recorded in animals [Reprinted by permission from Macmillan Publishers Ltd: Nature Neuroscience (Dombeck et al., 2010), copyright 2010]. Bottom row: cell activity as produced by our simulations. Left and right images depict activity while the agent moves to the right and left, respectively. In both, the experiment and the simulation, place fields cover the whole track and mostly are only active while the rat/camera faces a particular direction. Note that no simulated place fields are active at the very ends of the linear track, as the virtual animal is told to stop a specified distance before hitting any wall to simulate the extent of a physical body.
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Figure 13: Firing activity of a range of cells while traversing a narrow linear track in both directions. Top row: cell activity as recorded in animals [Reprinted by permission from Macmillan Publishers Ltd: Nature Neuroscience (Dombeck et al., 2010), copyright 2010]. Bottom row: cell activity as produced by our simulations. Left and right images depict activity while the agent moves to the right and left, respectively. In both, the experiment and the simulation, place fields cover the whole track and mostly are only active while the rat/camera faces a particular direction. Note that no simulated place fields are active at the very ends of the linear track, as the virtual animal is told to stop a specified distance before hitting any wall to simulate the extent of a physical body.

Mentions: Figure 13 shows the firing activity of cells while the agent follows a long narrow corridor. In their study, Dombeck et al. (2010) report two distinct properties of the examined fields: (a) they are sensitive to the direction the animal/agent is facing and (b) cover the full length of the linear track. Figure 13 depicts these for both the original recordings and our simulation results. Of the 32 simulated cells, 11 cells did not display any significant firing, 9 cells showed clear direction sensitivity, and 12 cells featured bi-directional activity (two of which featuring two distinct firing locations). Compared to the distribution of cells reported in McNaughton et al. (1983), our results match the percentage of cells that were discarded due to inactivity (34%), while of 25 cells recorded in CA1, McNaughton et al. (1983) reports that “(…) 14 were subjectively classified as highly directional, 6 relatively non-directional, and 5 were ambiguous.” Figure 14 depicts the directional consistency of the overall population, which peeks at zero as expected.


Modeling place field activity with hierarchical slow feature analysis.

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

Firing activity of a range of cells while traversing a narrow linear track in both directions. Top row: cell activity as recorded in animals [Reprinted by permission from Macmillan Publishers Ltd: Nature Neuroscience (Dombeck et al., 2010), copyright 2010]. Bottom row: cell activity as produced by our simulations. Left and right images depict activity while the agent moves to the right and left, respectively. In both, the experiment and the simulation, place fields cover the whole track and mostly are only active while the rat/camera faces a particular direction. Note that no simulated place fields are active at the very ends of the linear track, as the virtual animal is told to stop a specified distance before hitting any wall to simulate the extent of a physical body.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 13: Firing activity of a range of cells while traversing a narrow linear track in both directions. Top row: cell activity as recorded in animals [Reprinted by permission from Macmillan Publishers Ltd: Nature Neuroscience (Dombeck et al., 2010), copyright 2010]. Bottom row: cell activity as produced by our simulations. Left and right images depict activity while the agent moves to the right and left, respectively. In both, the experiment and the simulation, place fields cover the whole track and mostly are only active while the rat/camera faces a particular direction. Note that no simulated place fields are active at the very ends of the linear track, as the virtual animal is told to stop a specified distance before hitting any wall to simulate the extent of a physical body.
Mentions: Figure 13 shows the firing activity of cells while the agent follows a long narrow corridor. In their study, Dombeck et al. (2010) report two distinct properties of the examined fields: (a) they are sensitive to the direction the animal/agent is facing and (b) cover the full length of the linear track. Figure 13 depicts these for both the original recordings and our simulation results. Of the 32 simulated cells, 11 cells did not display any significant firing, 9 cells showed clear direction sensitivity, and 12 cells featured bi-directional activity (two of which featuring two distinct firing locations). Compared to the distribution of cells reported in McNaughton et al. (1983), our results match the percentage of cells that were discarded due to inactivity (34%), while of 25 cells recorded in CA1, McNaughton et al. (1983) reports that “(…) 14 were subjectively classified as highly directional, 6 relatively non-directional, and 5 were ambiguous.” Figure 14 depicts the directional consistency of the overall population, which peeks at zero as expected.

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