From grids to places

Franzius, Mathias; Vollgraf, Roland; Wiskott, Laurenz

doi:10.1007/s10827-006-0013-7

Cited by 65 publications

(62 citation statements)

References 9 publications

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“…The observed fields resemble those produced by self-organization of feedforward inputs from grid-like units (Rolls et al 2006;Franzius et al 2007), redefining the feedforward models as relevant for studying granule cell activity and its changes after different manipulations. At the same time, the question arises as to whether the observed fields in the dentate can serve as effective inputs to drive the establishment of new spatial representations in CA3.…”

Section: The Challenge Of Confronting Grid Cellsmentioning

confidence: 77%

“…even the most effective algorithm, a variant of Independent Component Analysis subject to a sparsity constraint, was found to produce rather implausible place fields even when summing from 100 grid units (Franzius et al 2007). The main difficulty, for the algorithm that has to structure the weights appropriately, is how to suppress the periodicity inherent in the grid fields to lead to a single-peaked place field: A competitive learning algorithm may only reduce the mean number of peaks of the output units (Rolls et al 2006).…”

Section: The Challenge Of Confronting Grid Cellsmentioning

confidence: 99%

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The CA3 network as a memory store for spatial representations

Papp¹,

Witter²,

Treves³

2007

Learning & Memory

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Comparative neuroanatomy suggests that the CA3 region of the mammalian hippocampus is directly homologous with the medio-dorsal pallium in birds and reptiles, with which it largely shares the basic organization of primitive cortex. Autoassociative memory models, which are generically applicable to cortical networks, then help assess how well CA3 may process information and what the crucial hurdles are that it may face. The analysis of such models points at spatial memories as posing a special challenge, both in terms of the attractor dynamics they can induce and how they may be established. Addressing such a challenge may have favored the evolution of elements of hippocampal organization observed only in mammals.

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Section: The Challenge Of Confronting Grid Cellsmentioning

confidence: 77%

Section: The Challenge Of Confronting Grid Cellsmentioning

confidence: 99%

The CA3 network as a memory store for spatial representations

Papp¹,

Witter²,

Treves³

2007

Learning & Memory

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show abstract

“…This created a matrix of synaptic connectivity W EH allowing the 75 entorhinal cells with activity g(t) to cause spiking in 400 hippocampal cells with activity p(t) as follows: p t ð Þ ¼ W EH g t ð Þ. Many other papers have explored how convergent input from grid cells causes place cell activity (O'Keefe and Solstad et al, 2006;Blair et al, 2007Blair et al, , 2008Franzius et al, 2007;Hayman and Jeffery, 2008).…”

Section: Phase Reset and Context-dependent Firingmentioning

confidence: 99%

Grid cell mechanisms and function: Contributions of entorhinal persistent spiking and phase resetting

2008

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ABSTRACT:This article presents a model of grid cell firing based on the intrinsic persistent firing shown experimentally in neurons of entorhinal cortex. In this model, the mechanism of persistent firing allows individual neurons to hold a stable baseline firing frequency. Depolarizing input from speed-modulated head direction cells transiently shifts the frequency of firing from baseline, resulting in a shift in spiking phase in proportion to the integral of velocity. The convergence of input from different persistent firing neurons causes spiking in a grid cell only when the persistent firing neurons are within similar phase ranges. This model effectively simulates the two-dimensional firing of grid cells in open field environments, as well as the properties of theta phase precession. This model provides an alternate implementation of oscillatory interference models. The persistent firing could also interact on a circuit level with rhythmic inhibition and neurons showing membrane potential oscillations to code position with spiking phase. These mechanisms could operate in parallel with computation of position from visual angle and distance of stimuli. In addition to simulating two-dimensional grid patterns, models of phase interference can account for context-dependent firing in other tasks. In network simulations of entorhinal cortex, hippocampus, and postsubiculum, the reset of phase effectively replicates context-dependent firing by entorhinal and hippocampal neurons during performance of a continuous spatial alternation task, a delayed spatial alternation task with running in a wheel during the delay period (Pastalkova et al., Science, 2008), and a hairpin maze task.

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“…Although the current ICA implementation may seem biologically unrealistic, a more biologically plausible learning scheme for generating place cells at the top ICA layer from the nonlocalized representation of SFA units can be implemented by competitive learning (Franzius et al, 2007b) or non-linear Hebbian learning (Hyvärinen and Oja, 1998).…”

Section: Discussionmentioning

confidence: 99%

Learning slow features with reservoir computing for biologically-inspired robot localization

Antonelo

Schrauwen

2012

Neural Networks

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This work proposes a hierarchical biologically-inspired architecture for learning sensor-based spatial representations of a robot environment in an unsupervised way. The first layer is comprised of a fixed randomly generated recurrent neural network, the reservoir, which projects the input into a high-dimensional, dynamic space. The second layer learns instantaneous slowly-varying signals from the reservoir states using Slow Feature Analysis (SFA), whereas the third layer learns a sparse coding on the SFA layer using Independent Component Analysis (ICA). While the SFA layer generates non-localized activations in space, the ICA layer presents high place selectivity, forming a localized spatial activation, characteristic of place cells found in the hippocampus area of the rodent's brain. We show that, using a limited number of noisy short-range distance sensors as input, the proposed system learns a spatial representation of the environment which can be used to predict the actual location of simulated and real robots, without the use of odometry. The results confirm that the reservoir layer is essential for learning spatial representations from low-dimensional input such as distance sensors. The main reason is that the reservoir state reflects the recent history of the input stream. Thus, this fading memory is essential for detecting locations, mainly when locations are ambiguous and characterized by similar sensor readings.

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From grids to places

Cited by 65 publications

References 9 publications

The CA3 network as a memory store for spatial representations

The CA3 network as a memory store for spatial representations

Grid cell mechanisms and function: Contributions of entorhinal persistent spiking and phase resetting

Learning slow features with reservoir computing for biologically-inspired robot localization

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