A Noise Compensation Mechanism for an RGNG-Based Grid Cell Model
Grid cells of the entorhinal cortex provide a rare view on the deep stages of information processing in the mammalian brain. Complementary to earlier grid cell models that interpret the behavior of grid cells as specialized parts within a system for navig
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Abstract Grid cells of the entorhinal cortex provide a rare view on the deep stages of information processing in the mammalian brain. Complementary to earlier grid cell models that interpret the behavior of grid cells as specialized parts within a system for navigation and orientation we developed a grid cell model that facilitates an abstract computational perspective on the behavior of these cells. Recently, we investigated the ability of our model to cope with increasing levels of input signal noise as it would be expected to occur in natural neurobiological circuits. Here we investigate these results further and introduce a new noise compensation mechanism to our model that normalizes the output activity of simulated grid cells irrespective of whether or not input noise is present. We present results from an extended series of simulation runs to characterize the involved parameters.
1 Introduction The parahippocampal-hippocampal region takes part in the deep stages of information processing in the mammalian brain. It is generally assumed to play a vital role in the formation of declarative, in particular episodic, memory as well as navigation and orientation. The discovery of grid cells, whose activity correlates with the animal’s location in a regular pattern, facilitates a rare view on the neuronal processing that occurs in this region of the brain [7, 9]. Complementary to earlier computational models of grid cells that interpret the behavior of grid cells as specialized parts within a system for navigation and orientation [1, 5, 8, 18, 19, 23] we introduced a new grid cell model that views the behavior of grid cells as just one instance of a general information processing scheme [10, 11]. The model relies on principles of self-organisation facilitated by the recursive growing neural gas (RGNG) algorithm.
J. Kerdels (B) · G. Peters University of Hagen, Universitätsstrasse 1, 58097 Hagen, Germany e-mail: [email protected] © Springer Nature Switzerland AG 2019 J. J. Merelo et al. (eds.), Computational Intelligence, Studies in Computational Intelligence 792, https://doi.org/10.1007/978-3-319-99283-9_13
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J. Kerdels and G. Peters
We could demonstrate [10, 12] that our model can not only describe the basic properties of grid cell activity but also recently observed phenomena like grid rescaling [2, 3] as well as grid-like activity in primates that correlates with eye movements [14] instead of environmental location. In addition, we recently investigated the ability of our model to cope with increasing levels of noise in it’s input signal as it would be expected to occur in natural neurobiological circuits [13]. Even with noise levels up to 90% of the input signal amplitude the model was able to establish the expected activity patterns. However, with increasing levels of noise the average maximum activity of the model’s output dropped by two orders of magnitude. Here we investigate this aspect further and present a noise compensation mechanism that we integrated in our grid cell model to
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