Response properties of avian hippocampal formation cells in an environment with unstable goal locations

Kahn, Meghan C.; Siegel, Jennifer J.; Jechura, Tammy J.; Bingman, Verner P.

doi:10.1016/j.bbr.2008.03.023

Cited by 40 publications

(54 citation statements)

References 37 publications

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“…Recordings are made in dMEC by tetrodes that capture multiple single-unit extracellular neural responses. Grid cells have also been found in mice and are likely to be found in other animals as well (Bingman and Sharp, 2006;Ulanovsky and Moss, 2007;Kahn et al, 2008).…”

Section: Background and Experimental Observationsmentioning

confidence: 91%

Grid cells: The position code, neural network models of activity, and the problem of learning

2008

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ABSTRACT:We review progress on the modeling and theoretical fronts in the quest to unravel the computational properties of the grid cell code and to explain the mechanisms underlying grid cell dynamics. The goals of the review are to outline a coherent framework for understanding the dynamics of grid cells and their representation of space; to critically present and draw contrasts between recurrent network models of grid cells based on continuous attractor dynamics and independentneuron models based on temporal interference; and to suggest open questions for experiment and theory. V

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Section: Background and Experimental Observationsmentioning

confidence: 91%

Grid cells: The position code, neural network models of activity, and the problem of learning

2008

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“…These patches of activity have distinct boundaries with abrupt decreases in firing rate between the patch and the immediate surrounding area in which the pattern cell is not active. Pattern cells are not observed when pigeons move through a maze; location and path cells are not observed when pigeons move through an open arena in which pattern cells are active [Kahn et al, 2008]. Pattern cells also have a low firing rate compared to location and path cells.…”

Section: Pattern Cellsmentioning

confidence: 96%

“…When the configuration of lights was rotated 90° the fields of maximum activity of location cells also rotated by 90° [Hough and Bingman, 2008]. Evidence that location cells of the pigeon hippocampus do not behave like place cells of the rodent hippocampus, however, comes from an experiment in which pigeons explored an open arena instead of maze arms in a plus or radial configuration [Kahn et al, 2008]. In such an environment, location cells essentially do not occur.…”

Section: Location Cellsmentioning

confidence: 99%

“…Research with homing pigeons by Bingman and his colleagues has directly addressed this question, and the answer appears to be, no, neurons in the homing pigeon hippocampus do not have the characteristics of classic rat place cells [Siegel et al, 2002;Hough and Bingman, 2004;Siegel et al, 2005;Hough and Bingman, 2008;Kahn et al, 2008]. But current understanding of rat hippocampal place cells has become more nuanced than their classic description as more has been discovered about the space-specific response properties of neurons in the rat hippocampus, and the homing pigeon hippocampus clearly does contain cells with firing rates that vary predictably in space.…”

Section: Introductionmentioning

confidence: 99%

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Are There Place Cells in the Avian Hippocampus?

et al. 2017

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Birds possess a hippocampus that serves many of the same spatial and mnemonic functions as the mammalian hippocampus but achieves these outcomes with a dramatically different neuroanatomical organization. The properties of spatially responsive neurons in birds and mammals are also different. Much of the contemporary interest in the role of the mammalian hippocampus in spatial representation dates to the discovery of place cells in the rat hippocampus. Since that time, cells that respond to head direction and cells that encode a grid-like representation of space have been described in the rat brain. Research with homing pigeons has discovered hippocampal cells, including location cells, path cells, and pattern cells, that share some but not all properties of spatially responsive neurons in the rodent brain. We have recently used patterns of immediate-early gene expression, visualized by the catFISH method, to investigate how neurons in the hippocampus of brood-parasitic brown-headed cowbirds respond to spatial context. We have found cells that discriminate between different spatial environments and are re-activated when the same spatial environment is re-experienced. Given the differences in habitat and behaviour between birds and rodents, it is not surprising that spatially responsive cells in their hippocampus and other brain regions differ. The enormous diversity of avian habitats and behaviour offers the potential for understanding the general principles of neuronal representation of space.

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“…This perspective suggests that similar spatial modulation (including grid cells, place cells, head-direction cells) may be expected to be observed in species with similar behavioral patterns to rodents, irrespective of whether they present similar neural circuitry (e.g., bats, Ulanovsky and Moss, 2007) or quite different circuitry (e.g., birds, Bingman and Sharp, 2006; see the pattern cells in Kahn et al, 2008). Such modulation would not necessarily be expected, instead, in species with similar circuitry but very different spatial behavior, e.g., large primates (Rolls, 1999).…”

Section: Do Grids Stem From Attractors or From Oscillations?mentioning

confidence: 99%

The emergence of grid cells: Intelligent design or just adaptation?

2008

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ABSTRACT:Individual medial entorhinal cortex (mEC) 'grid' cells provide a representation of space that appears to be essentially invariant across environments, modulo simple transformations, in contrast to multiple, rapidly acquired hippocampal maps; it may therefore be established gradually during rodent development. We explore with a simplified mathematical model the possibility that the self-organization of multiple grid fields into a triangular grid pattern may be a single-cell process, driven by firing rate adaptation and slowly varying spatial inputs. A simple analytical derivation indicates that triangular grids are favored asymptotic states of the self-organizing system, and computer simulations confirm that such states are indeed reached during a model learning process, provided it is sufficiently slow to effectively average out fluctuations. The interactions among local ensembles of grid units serve solely to stabilize a common grid orientation. Spatial information, in the real mEC network, may be provided by any combination of feedforward cortical afferents and feedback hippocampal projections from place cells, since either input alone is likely sufficient to yield grid fields. V V C 2008 Wiley-Liss, Inc. KEY WORDS:hippocampus; entorhinal cortex; firing rate adaptation; attractor network; memory DO GRIDS STEM FROM ATTRACTORS OR FROM OSCILLATIONS?Among the complex memory processes operating within the medial temporal lobe (see e.g., Eichenbaum and Lipton, 2008), the core contribution of the hippocampus may be its capacity to retrieve multiple arbitrary representations, a capacity that has been associated to the 'collateral effect' (Marr, 1971). McNaughton and Morris (1987) and Rolls (1989) proposed that the collateral effect is implemented by the recurrent connections of the CA3 region, which led to the insight that the CA3 network may 'compute' just by following attractor dynamics (Amit, 1989), as described by the simplified Hopfield (1982) model. The striking demonstration of abrupt global remapping, indicative of attractor dynamics, in rat place cells (Wills et al., 2005) has reinforced the notion that attractors are a key to understanding hippocampal memory computation. The concurrent discovery of grid cells in neighboring medial entorhinal cortex (mEC;Fyhn et al., 2004;Hafting et al., 2005) has led to the attractor idea reverberating into mEC networks: grid cells have been interpreted as the stable attractor states of spinglass-like interactions among pyramidal cells, mediated by recurrent connections (Fuhs and Touretzky, 2006). Unlike the multiple representations in CA3, however, which require global remapping transitions from one to the other (Leutgeb et al., 2005), local ensembles of mEC grid cells seem to demonstrate a single representation, which shifts and rotates coherently in different environments (Fyhn et al., 2007). If so, and if attractor computation were its core design principle, what the recurrent network in mEC would produce is merely the recovery of this single representation, e....

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Response properties of avian hippocampal formation cells in an environment with unstable goal locations

Cited by 40 publications

References 37 publications

Grid cells: The position code, neural network models of activity, and the problem of learning

Grid cells: The position code, neural network models of activity, and the problem of learning

Are There Place Cells in the Avian Hippocampus?

The emergence of grid cells: Intelligent design or just adaptation?

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