Experience-Dependent Asymmetric Shape of Hippocampal Receptive Fields

Mehta, Mayank R.; Quirk, Michael C.; Wilson, Matthew A.

doi:10.1016/s0896-6273(00)81072-7

Cited by 452 publications

(523 citation statements)

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“…We conjecture that repeated pairing of different streams of input could generate robust associations between them through rapid Hebbian synaptic plasticity, resulting in stable spatial representations 48 and increased firing rates 19,[48][49][50] . Under this model, during random foraging in RW, distal visual cues are paired repeatedly with the same constellation of proximal cues at each location, resulting in a place code.…”

Section: Discussionmentioning

confidence: 99%

“…In the systematicpillar task, when we paired locomotion and visual cues repeatedly, this selectivity was strengthened and extended to the middle of the paths. Restricting the analysis to cells that fired on at least two nonoverlapping arms on the three-pillar task revealed that these cells exhibited a disto-code 21 , which is a specific case of the more general distance selectivity observed in the goal-directed tasks.We conjecture that repeated pairing of different streams of input could generate robust associations between them through rapid Hebbian synaptic plasticity, resulting in stable spatial representations 48 and increased firing rates 19,[48][49][50] . Under this model, during random foraging in RW, distal visual cues are paired repeatedly with the same constellation of proximal cues at each location, resulting in a place code.…”

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confidence: 98%

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Impaired spatial selectivity and intact phase precession in two-dimensional virtual reality

et al. 2014

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During real-world (RW) exploration, rodent hippocampal activity shows robust spatial selectivity, which is hypothesized to be governed largely by distal visual cues, although other sensory-motor cues also contribute. Indeed, hippocampal spatial selectivity is weak in primate and human studies that use only visual cues. To determine the contribution of distal visual cues only, we measured hippocampal activity from body-fixed rodents exploring a two-dimensional virtual reality (VR). Compared to that in RW, spatial selectivity was markedly reduced during random foraging and goal-directed tasks in VR. Instead we found small but significant selectivity to distance traveled. Despite impaired spatial selectivity in VR, most spikes occurred within ~2-s-long hippocampal motifs in both RW and VR that had similar structure, including phase precession within motif fields. Selectivity to space and distance traveled were greatly enhanced in VR tasks with stereotypical trajectories. Thus, distal visual cues alone are insufficient to generate a robust hippocampal rate code for space but are sufficient for a temporal code. npg © 2015 Nature America, Inc. All rights reserved.1 2 2 VOLUME 18 | NUMBER 1 | JANUARY 2015 nature neurOSCIenCe a r t I C l e S same physical location in space can be approached from multiple different directions at different speeds. We thus investigated the contribution of distal visual cues only in determining selectivity in such an experimental setup. RESULTS Nature of spatial selectivity of hippocampal responsesWe measured hippocampal activity during a two-dimensional randomforaging task in RW and VR [35][36][37] with similar distal visual cues (Fig. 1a). In VR, the rats were body-fixed, i.e., their bodies were held in place, with a harness on a floating ball, allowing for head movements but precluding full-body turns, thus minimizing vestibular cues (Online Methods) 21,36 . Rats quickly learned to avoid the virtual edges entirely on the basis of visual cues 36 and spent a similar amount of time away from the edges and in the center of the platform (Fig. 1a) compared to in RW. We measured the activity of 1,066 and 1,238 principal neurons in RW and VR, respectively, in the dorsal CA1 of four rats under a variety of conditions (Online Methods). Neurons fired vigorously in restricted regions of space in RW, as expected (Fig. 1b,c, Supplementary Fig. 1a and Supplementary Video 1) 1 . In contrast, the neurons showed little spatial selectivity in VR during random foraging (Fig. 1b,d and Supplementary Fig. 1b).Across the ensemble, neurons had moderately reduced (25%) mean firing rates but greatly reduced (68%) peak firing rates in VR ( Fig. 2a and Supplementary Fig. 2a). Neurons in VR also had greatly reduced spatial information content (75%), stability (59%), sparsity (42%) and coherence (40%) (Fig. 2b-d and Supplementary Fig. 2b,c) compared to spatially localized, stable and sparse RW rate maps (Fig. 2c). Although the mean firing rate was inversely correlated with information content (Supplementary Fig. 2d)...

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Section: Discussionmentioning

confidence: 99%

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confidence: 98%

Impaired spatial selectivity and intact phase precession in two-dimensional virtual reality

et al. 2014

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“…Phase precession is not prevented by the NMDA receptor antagonist CPP, which blocks experience-dependent expression of long-term potentiation and, as a result, the asymmetric expansion of hippocampal place fields during repeated route following (Mehta, Quirk, & Wilson, 2000;Ekstrom et al, 2001). This finding is consistent with our model because plasticity at mf synapses is independent of NMDA receptors (Nicoll & Malenka, 1995;Nicoll & Schmitz, 2005).…”

Section: Perturbations Of the Hippocampusmentioning

confidence: 99%

Phase Precession Through Synaptic Facilitation

et al. 2008

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Phase precession is a relational code that is thought to be important for episodic-like memory, for instance, the learning of a sequence of places. In the hippocampus, places are encoded through bursting activity of socalled place cells. The spikes in such a burst exhibit a precession of their firing phases relative to field potential theta oscillations (4-12 Hz); the theta phase of action potentials in successive theta cycles progressively decreases toward earlier phases. The mechanisms underlying the generation of phase precession are, however, unknown. In this letter, we show * Kay Thurley is now at the Institute of Physiology, University of Bern, 3012 Bern, Switzerland. Christian Leibold is now at the Department of Biology II, LudwigMaximilians-Universität München, 82152 Planegg-Martinsried, Germany. through mathematical analysis and numerical simulations that synaptic facilitation in combination with membrane potential oscillations of a neuron gives rise to phase precession. This biologically plausible model reproduces experimentally observed features of phase precession, such as (1) the progressive decrease of spike phases, (2) the nonlinear and often also bimodal relation between spike phases and the animal's place, (3) the range of phase precession being smaller than one theta cycle, and (4) the dependence of phase jitter on the animal's location within the place field. The model suggests that the peculiar features of the hippocampal mossy fiber synapse, such as its large efficacy, long-lasting and strong facilitation, and its phase-locked activation, are essential for phase precession in the CA3 region of the hippocampus. Neural Computation

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“…For the development of asymmetric place fields (Figure 1b), rate coding is sufficient [10,11,29,30] and a precise temporal code is not required. The development of asymmetric place fields [24,30] or, similarly, of visual receptive fields [31,32] could therefore be interpreted as a system-level signature of asymmetric Hebbian plasticity.…”

Section: Asymmetric Place Fields and Asymmetric Learningmentioning

confidence: 99%

“…Mehta and colleagues found the shift from a symmetrical to asymmetrical place field during training, but they also found that this development of asymmetry was completely reset the following day [30]. Combining a symmetrical receptive field with an oscillatory inhibition would lead to a phase precession and then a recession, the result being that the spiking at a particular phase could not reliably represent the location.…”

Section: Open Questionsmentioning

confidence: 99%

Coding and learning of behavioral sequences

Melamed

Gerstner

Maass

et al. 2004

Trends in Neurosciences

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A major challenge to understanding behavior is how the nervous system allows the learning of behavioral sequences that can occur over arbitrary timescales, ranging from milliseconds up to seconds, using a fixed millisecond learning rule. This article describes some potential solutions, and then focuses on a study by Mehta et al. that could contribute towards solving this puzzle. They have discovered that an experience-dependent asymmetric shape of hippocampal receptive fields combined with oscillatory inhibition can serve to map behavioral sequences on a fixed timescale.Behavioral sequences of events span several orders of magnitude of timescales. A piano player is able to play rapid scales or repetitions of a single tone at a rate of 12 or more keys per second, probably using an internal sequence of motor commands that is even faster. However, when playing a slow tune, commands are executed ten or twenty times slower. Such different temporal sequences should ideally all be learned using a synaptic learning rule that operates on a fixed neuronal timescale of milliseconds [1 -3]. How then, is learning of behavioral sequences across arbitrary timescales possible? Several studies have recently addressed this issue from different points of view, and have suggested potential solutions.Coding sequences at millisecond resolution One potential solution of the problem could be that all sequences, fast or slow, are in fact stored and retrieved at a millisecond resolution. Synfire chains (SFC) [4] -dominantly feedforward multi-layered architecture (chains) in which spiking activity can propagate in a synchronous wave of neuronal firing (a pulse packet) from one layer of the chain to the next -provide a conceptual framework for such an approach. The chain of neuronal activity evolves on a millisecond scale and, hence, on the natural timescale of spike-time-dependent plasticity. Because all information is represented by a sequence of firing neuronal times, the SFC would automatically solve the problem of sequence learning using a fixed-timescale learning rule. The fastest behavioral sequence would be one in which each time step of the neuronal chain corresponds to one behavioral event. A slow behavioral sequence would then simply be represented by several neuronal time steps per behavioral event. The problem with such an approach is that the representation of slow sequences of events (e.g. five events in one second) is rather expensive, because it requires hundreds of transitions that need to be precisely stored in neuronal connections [4][5][6].A recent study suggested that a mechanism closely related to neuronal SFCs is implemented in the premotor area of song-birds [7]. Neurons projecting from the nucleus hyperstriatum ventralis pars caudalis (HVC) to the forebrain robust nucleus of the archistriatum (RA) are active in a tight temporal sequence or 'clock'. In contrast to the SFC concept, however, each neuron emits not a single spike but a burst of three to four spikes within ,10 ms. Learning a song could then occur in the...

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Experience-Dependent Asymmetric Shape of Hippocampal Receptive Fields

Cited by 452 publications

References 33 publications

Impaired spatial selectivity and intact phase precession in two-dimensional virtual reality

Impaired spatial selectivity and intact phase precession in two-dimensional virtual reality

Phase Precession Through Synaptic Facilitation

Coding and learning of behavioral sequences

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