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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 cells that were trained in both a rectangular and a circular environment: each of the two rows depicts the firing activity of one cell as the environment morphs from a rectangular layout to a circular one. Firing rates decrease steadily as the environment morphs into the shape a cell is not anchored to while the network attempts to keep the fields in the same positions.
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Figure 18: Firing activity of cells that were trained in both a rectangular and a circular environment: each of the two rows depicts the firing activity of one cell as the environment morphs from a rectangular layout to a circular one. Firing rates decrease steadily as the environment morphs into the shape a cell is not anchored to while the network attempts to keep the fields in the same positions.

Mentions: Both Wills et al. (2005) and Leutgeb et al. (2005b) studied the effects of morphing the environment from a familiar square box to an also familiar circular arena, with the in-between morph states being unfamiliar to the animals. Figure 18 shows the activity of two representative cells over the whole arena during the different morph stages. In our simulations, cells usually have one distinct firing field in either the box or the circular arena. Field activity stays in the same location (as far as the geometry of the maze layout permits) and gradually fades out as the environment morphs from a rectangular configuration into a circular one and vice versa. Figure 19 shows how this forms the statistics of the overall population: in both the square box and circular arena around half of the cells show directional consistency. It can also be seen how this value drops for all cells during the in-between morph stages of the arena. Similarly, in both the square box and the circular arena around half of the population shows a single firing field, while the other half remains silent (Figure 20).


Modeling place field activity with hierarchical slow feature analysis.

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

Firing activity of cells that were trained in both a rectangular and a circular environment: each of the two rows depicts the firing activity of one cell as the environment morphs from a rectangular layout to a circular one. Firing rates decrease steadily as the environment morphs into the shape a cell is not anchored to while the network attempts to keep the fields in the same positions.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 18: Firing activity of cells that were trained in both a rectangular and a circular environment: each of the two rows depicts the firing activity of one cell as the environment morphs from a rectangular layout to a circular one. Firing rates decrease steadily as the environment morphs into the shape a cell is not anchored to while the network attempts to keep the fields in the same positions.
Mentions: Both Wills et al. (2005) and Leutgeb et al. (2005b) studied the effects of morphing the environment from a familiar square box to an also familiar circular arena, with the in-between morph states being unfamiliar to the animals. Figure 18 shows the activity of two representative cells over the whole arena during the different morph stages. In our simulations, cells usually have one distinct firing field in either the box or the circular arena. Field activity stays in the same location (as far as the geometry of the maze layout permits) and gradually fades out as the environment morphs from a rectangular configuration into a circular one and vice versa. Figure 19 shows how this forms the statistics of the overall population: in both the square box and circular arena around half of the cells show directional consistency. It can also be seen how this value drops for all cells during the in-between morph stages of the arena. Similarly, in both the square box and the circular arena around half of the population shows a single firing field, while the other half remains silent (Figure 20).

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