scholarly journals Asymmetry of the temporal code for space by hippocampal place cells

2016 ◽  
Author(s):  
Bryan C. Souza ◽  
Adriano B. L. Tort
Keyword(s):  
Firing Rate ◽  
Spatial Information ◽  
Place Cells ◽  
Spike Timing ◽  
Temporal Coding ◽  
Place Field ◽  
Phase Precession ◽  
Wide Debate ◽  
Rate Coding ◽  
Theta Phase

Hippocampal place cells convey spatial information through spike frequency (“rate coding”) and spike timing relative to the theta phase (“temporal coding”). Whether rate and temporal coding are due to independent or related mechanisms has been the subject of wide debate. Here we show that the spike timing of place cells couples to theta phase before major increases in firing rate, anticipating the animal’s entrance into the classical, rate-based place field. In contrast, spikes rapidly decouple from theta as the animal leaves the place field and firing rate decreases. Therefore, temporal coding has strong asymmetry around the place field center. We further show that the dynamics of temporal coding along space evolves in three stages: phase coupling, phase precession and phase decoupling. These results suggest that place cells represent more future than past locations through their spike timing and that independent mechanisms govern rate and temporal coding.

scholarly journals Distinct hippocampal network states support theta phase precession and theta sequences in CA1

2021 ◽  
Author(s):  
Matteo Guardamagna ◽  
Federico Stella ◽  
Francesco P. Battaglia
Keyword(s):  
Spatial Information ◽  
Place Cells ◽  
Temporal Coding ◽  
Temporal Code ◽  
Network State ◽  
Phase Precession ◽  
Hippocampal Network ◽  
Theta Phase ◽  
Network States ◽  
Theta Phase Precession

The hippocampus likely uses temporal coding to represent complex memories via mechanisms such as theta phase precession and theta sequences. Theta sequences are rapid sweeps of spikes from multiple place cells, encoding past or planned trajectories or non-spatial information. Phase precession, the correlation between a place cell's theta firing phase and animal position has been suggested to facilitate sequence emergence. We find that CA1 phase precession varies strongly across cells and environmental contingencies. Phase precession depends on the CA1 network state, and is only present when the medium gamma oscillation (60-90 Hz, linked to Entorhinal inputs) dominates. Conversely, theta sequences are most evident for non-precessing cells or with leading slow gamma (20-45 Hz, linked to CA3 inputs). These results challenge the view that phase precession is the mechanism underlying the emergence of theta sequences and point at a 'dual network states' model for hippocampal temporal code, potentially supporting merging of memory and exogenous information in CA1.

scholarly journals Directional Tuning of Phase Precession Properties in the Hippocampus

2021 ◽  
Author(s):  
Yuk-Hoi Yiu ◽  
Jill K Leutgeb ◽  
Christian Leibold
Keyword(s):  
Place Cells ◽  
Spike Timing ◽  
Motor Drive ◽  
Place Field ◽  
Directional Information ◽  
Field Activity ◽  
Phase Precession ◽  
Cornu Ammonis ◽  
Theta Phase ◽  
Theta Phase Precession

Running direction in the hippocampus is encoded by rate modulations of place field activity but also by spike timing correlations known as theta sequences. Whether directional rate codes and the directionality of place field correlations are related, however, has so far not been explored and therefore the nature of how directional information is encoded in the cornu ammonis remains unresolved. Here, using a previously published dataset that contains the spike activity of rat hippocampal place cells in the CA1, CA2 and CA3 subregions during free foraging of male Long-Evans rats in a 2D environment, we found that rate and spike timing codes are related. Opposite to a place field's preferred firing rate direction spikes are more likely to undergo theta phase precession and, hence, more strongly impact paired correlations. Furthermore, we identified a subset of field pairs whose theta correlations are intrinsic in that they maintain the same firing order when the running direction is reversed. Both effects are associated with differences in theta phase distributions, and are more prominent in CA3 than CA1. We thus hypothesize that intrinsic spiking is most prominent when the directionally modulated sensory-motor drive of hippocampal firing rates is minimal, suggesting that extrinsic and intrinsic sequences contribute to phase precession as two distinct mechanisms.

Memory Encoding by Theta Phase Precession in the Hippocampal Network

2003 ◽  
Vol 15 (10) ◽  
pp. 2379-2397 ◽  
Author(s):  
Naoyuki Sato ◽  
Yoko Yamaguchi
Keyword(s):  
Spike Timing ◽  
Temporal Sequence ◽  
Memory Storage ◽  
Trial Experience ◽  
Phase Precession ◽  
Rate Coding ◽  
Hippocampal Network ◽  
Theta Phase ◽  
Theta Phase Precession

Recent experimental evidence on spike-timing-dependent plasticity and on phase precession (i.e., the theta rhythm dependent firing of rat hippocampalcells) associates the contribution of phase precession to episodic memory. This article aims at clarifying the role of phase precession in memory storage. Computer simulations show that the memory storage in the behavioral timescale varies in timescale of the temporal sequence from half a second to several seconds. In contrast, the memory storage caused by traditional rate coding is restricted to the temporal sequence within 40 ms. During phase precession, memory storage of a single trial experience is possible, even in the presence of noise. It is therefore concluded that encoding by phase precession is appropriate for memory storage of the temporal sequence in the behavioral timescale.

scholarly journals Behavior-dependent spatial maps enable efficient theta phase coding

2021 ◽  
Author(s):  
Eloy Parra-Barrero ◽  
Kamran Diba ◽  
Sen Cheng
Keyword(s):  
Place Cells ◽  
Spatial Representations ◽  
Place Field ◽  
Phase Code ◽  
Phase Precession ◽  
Relevant Variables ◽  
Theta Phase ◽  
Hippocampal Theta ◽  
Exact Relationship ◽  
Speed Characteristic

AbstractNavigation through space involves learning and representing relationships between past, current and future locations. In mammals, this might rely on the hippocampal theta phase code, where in each cycle of the theta oscillation, spatial representations start behind the animal’s location and then sweep forward. However, the exact relationship between phase and represented and true positions remains unclear and even paradoxical. Here, we formalize previous notions as ‘spatial’ or ‘temporal’ sweeps, analyze single-cell and population variables in recordings from rat CA1 place cells, and compare them to model simulations. We show that neither sweep type quantitatively accounts for all relevant variables. Thus we introduce ‘behavior-dependent’ sweeps, which fit our key observation that sweep length, and hence place field properties, such as size and phase precession, vary across the environment depending on the running speed characteristic of each location. This structured heterogeneity is essential for understanding the hippocampal code.

scholarly journals Bimodality of Theta Phase Precession in Hippocampal Place Cells in Freely Running Rats

2002 ◽  
Vol 87 (6) ◽  
pp. 2629-2642 ◽  
Author(s):  
Yoko Yamaguchi ◽  
Yoshito Aota ◽  
Bruce L. McNaughton ◽  
Peter Lipa
Keyword(s):  
Place Cells ◽  
Distribution Functions ◽  
Data Sets ◽  
Place Field ◽  
Spike Density ◽  
Phase Versus ◽  
Normal Probability ◽  
Phase Precession ◽  
Theta Phase ◽  
Theta Phase Precession

The firing of hippocampal principal cells in freely running rats exhibits a progressive phase retardation as the animal passes through a cell's “place” field. This “phase precession” is more complex than a simple linear shift of phase with position. In the present paper, phase precession is quantitatively analyzed by fitting multiple (1–3) normal probability density functions to the phase versus position distribution of spikes in rats making repeated traversals of the place fields. The parameters were estimated by the Expectation Maximization method. Three data sets including CA1 and DG place cells were analyzed. Although the phase-position distributions vary among different cells and regions, this complexity is well described by a superposition of two normal distribution functions, suggesting that the firing behavior consists of two components. This conclusion is supported by the existence of two distinct maxima in the mean spike density in the phase versus position plane. In one component, firing phase shifts over a range of about 180°. The second component, which occurs near the end of the traversal of the place field, exhibits a low correlation between phase and position and is anti-phase with the phase-shift component. The functional implications of the two components are discussed with respect to their possible contribution to learning and memory mechanisms.

Inheritance of Hippocampal Place Fields Through Hebbian Learning: Effects of Theta Modulation and Phase Precession on Structure Formation

2015 ◽  
Vol 27 (8) ◽  
pp. 1624-1672 ◽  
Author(s):  
Tiziano D’Albis ◽  
Jorge Jaramillo ◽  
Henning Sprekeler ◽  
Richard Kempter
Keyword(s):  
Hebbian Learning ◽  
Learning Rule ◽  
Place Cells ◽  
Spike Timing ◽  
Upstream Region ◽  
Place Field ◽  
Hebbian Learning Rule ◽  
Phase Precession ◽  
Place Fields ◽  
Field Formation

A place cell is a neuron that fires whenever the animal traverses a particular location of the environment—the place field of the cell. Place cells are found in two regions of the rodent hippocampus: CA3 and CA1. Motivated by the anatomical connectivity between these two regions and by the evidence for synaptic plasticity at these connections, we study how a place field in CA1 can be inherited from an upstream region such as CA3 through a Hebbian learning rule, in particular, through spike-timing-dependent plasticity (STDP). To this end, we model a population of CA3 place cells projecting to a single CA1 cell, and we assume that the CA1 input synapses are plastic according to STDP. With both numerical and analytical methods, we show that in the case of overlapping CA3 input place fields, the STDP learning rule leads to the formation of a place field in CA1. We then investigate the roles of the hippocampal theta modulation and phase precession on the inheritance process. We find that theta modulation favors the inheritance and leads to faster place field formation whereas phase precession changes the drift of CA1 place fields over time.

Position Reconstruction From an Ensemble of Hippocampal Place Cells: Contribution of Theta Phase Coding

2000 ◽  
Vol 83 (5) ◽  
pp. 2602-2609 ◽  
Author(s):  
Ole Jensen ◽  
John E. Lisman
Keyword(s):  
Hippocampal Neurons ◽  
Place Cells ◽  
Place Field ◽  
Reconstruction Accuracy ◽  
Relative Contribution ◽  
Three Phase ◽  
Phase Precession ◽  
Rate Information ◽  
Theta Phase ◽  
Theta Phase Precession

Previous analysis of the firing of individual rat hippocampal place cells has shown that their firing rate increases when they enter a place field and that their phase of firing relative to the ongoing theta oscillation (7–12 Hz) varies systematically as the rat traverses the place field, a phenomenon termed the theta phase precession. To study the relative contribution of phased-coded and rate-coded information, we reconstructed the animal's position on a linear track using spikes recorded simultaneously from 38 hippocampal neurons. Two previous studies of this kind found no evidence that phase information substantially improves reconstruction accuracy. We have found that reconstruction is improved provided epochs with large, systematic errors are first excluded. With this condition, use of both phase and rate information improves the reconstruction accuracy by >43% as compared with the use of rate information alone. Furthermore, it becomes possible to predict the rat's position on a 204-cm track with very high accuracy (error of <3 cm). The best reconstructions were obtained with more than three phase divisions per theta cycle. These results strengthen the hypothesis that information in rat hippocampal place cells is encoded by the phase of theta at which cells fire.

Independence of the unimodal tuning of firing rate from theta phase precession in hippocampal place cells

2009 ◽  
Vol 102 (2) ◽  
pp. 95-107 ◽  
Author(s):  
Zhihua Wu ◽  
Yoko Yamaguchi
Keyword(s):  
Firing Rate ◽  
Place Cells ◽  
Phase Precession ◽  
Theta Phase ◽  
Theta Phase Precession

scholarly journals Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 500 ◽  
Author(s):  
Sergey A. Lobov ◽  
Andrey V. Chernyshov ◽  
Nadia P. Krilova ◽  
Maxim O. Shamshin ◽  
Victor B. Kazantsev
Keyword(s):  
Supervised Learning ◽  
Hebbian Learning ◽  
Back Propagation ◽  
Spike Timing ◽  
Temporal Coding ◽  
Competitive Learning ◽  
Neuron Activity ◽  
Synaptic Competition ◽  
Rate Coding ◽  
Emg Patterns

One of the modern trends in the design of human–machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular, we have shown that sensory neurons in the input layer of SNNs can simultaneously encode the input signal based both on the spiking frequency rate and on varying the latency in generating spikes. In the case of such mixed temporal-rate coding, the SNN should implement learning working properly for both types of coding. Based on this, we investigate how a single neuron can be trained with pure rate and temporal patterns, and then build a universal SNN that is trained using mixed coding. In particular, we study Hebbian and competitive learning in SNN in the context of temporal and rate coding problems. We show that the use of Hebbian learning through pair-based and triplet-based spike timing-dependent plasticity (STDP) rule is accomplishable for temporal coding, but not for rate coding. Synaptic competition inducing depression of poorly used synapses is required to ensure a neural selectivity in the rate coding. This kind of competition can be implemented by the so-called forgetting function that is dependent on neuron activity. We show that coherent use of the triplet-based STDP and synaptic competition with the forgetting function is sufficient for the rate coding. Next, we propose a SNN capable of classifying electromyographical (EMG) patterns using an unsupervised learning procedure. The neuron competition achieved via lateral inhibition ensures the “winner takes all” principle among classifier neurons. The SNN also provides gradual output response dependent on muscular contraction strength. Furthermore, we modify the SNN to implement a supervised learning method based on stimulation of the target classifier neuron synchronously with the network input. In a problem of discrimination of three EMG patterns, the SNN with supervised learning shows median accuracy 99.5% that is close to the result demonstrated by multi-layer perceptron learned by back propagation of an error algorithm.

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