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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

A representative sample of firing activity in the rhombus-track experiment as produced by our simulations. Each row depicts the firing activity of one cell during both clockwise and counter-clockwise traversal of the open field along the pre-trained rhombus-shaped path. All cells can be seen to be sensitive to a specific orientation and lose their distinct firing fields when traversed in the other direction.
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Figure 16: A representative sample of firing activity in the rhombus-track experiment as produced by our simulations. Each row depicts the firing activity of one cell during both clockwise and counter-clockwise traversal of the open field along the pre-trained rhombus-shaped path. All cells can be seen to be sensitive to a specific orientation and lose their distinct firing fields when traversed in the other direction.

Mentions: In Markus et al. (1995) animals were trained to follow a rhombus shaped path within an open environment. Measuring the animals' place code revealed directional dependent firing. Figure 15 shows one such cell as reported in Markus et al. (1995) in a side by side comparison with a hand-picked cell from our simulation. Figure 16 shows a number of representative cells from our simulation results and their firing activity when the animal follows its trained path in a clockwise and counter-clockwise fashion. Of the 32 cells measured in our simulation, 16 cells showed clear place fields in one direction and little to no activity in the other; 8 cells showed clear fields in one direction and significant but unstructured activity in the other; while the remaining 8 cells showed either unstructured activity in both directions or stayed silent. Figure 17 shows the directional consistency of the cell population, which for this second direction sensitive setup again peeks at zero.


Modeling place field activity with hierarchical slow feature analysis.

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

A representative sample of firing activity in the rhombus-track experiment as produced by our simulations. Each row depicts the firing activity of one cell during both clockwise and counter-clockwise traversal of the open field along the pre-trained rhombus-shaped path. All cells can be seen to be sensitive to a specific orientation and lose their distinct firing fields when traversed in the other direction.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 16: A representative sample of firing activity in the rhombus-track experiment as produced by our simulations. Each row depicts the firing activity of one cell during both clockwise and counter-clockwise traversal of the open field along the pre-trained rhombus-shaped path. All cells can be seen to be sensitive to a specific orientation and lose their distinct firing fields when traversed in the other direction.
Mentions: In Markus et al. (1995) animals were trained to follow a rhombus shaped path within an open environment. Measuring the animals' place code revealed directional dependent firing. Figure 15 shows one such cell as reported in Markus et al. (1995) in a side by side comparison with a hand-picked cell from our simulation. Figure 16 shows a number of representative cells from our simulation results and their firing activity when the animal follows its trained path in a clockwise and counter-clockwise fashion. Of the 32 cells measured in our simulation, 16 cells showed clear place fields in one direction and little to no activity in the other; 8 cells showed clear fields in one direction and significant but unstructured activity in the other; while the remaining 8 cells showed either unstructured activity in both directions or stayed silent. Figure 17 shows the directional consistency of the cell population, which for this second direction sensitive setup again peeks at zero.

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