Spiking Activity Of Place Cells Research Articles

Position–theta-phase model of hippocampal place cell activity applied to quantification of running speed modulation of firing rate

Spiking activity of place cells in the hippocampus encodes the animal's position as it moves through an environment. Within a cell's place field, both the firing rate and the phase of spiking in the local theta oscillation contain spatial information. We propose a position-theta-phase (PTP) model that captures the simultaneous expression of the firing-rate code and theta-phase code in place cell spiking. This model parametrically characterizes place fields to compare across cells, time, and conditions; generates realistic place cell simulation data; and conceptualizes a framework for principled hypothesis testing to identify additional features of place cell activity. We use the PTP model to assess the effect of running speed in place cell data recorded from rats running on linear tracks. For the majority of place fields, we do not find evidence for speed modulation of the firing rate. For a small subset of place fields, we find firing rates significantly increase or decrease with speed. We use the PTP model to compare candidate mechanisms of speed modulation in significantly modulated fields and determine that speed acts as a gain control on the magnitude of firing rate. Our model provides a tool that connects rigorous analysis with a computational framework for understanding place cell activity.

Hippocampal place cell dysfunction and the effects of muscarinic M1 receptor agonism in a rat model of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease that disproportionately impacts memory and the hippocampus. However, it is unclear how AD pathology influences the activity of surviving neurons in the hippocampus to contribute to the memory symptoms in AD. One well-understood connection between spatial memory and neuronal activity in healthy brains is the activity of place cells, neurons in the hippocampus that fire preferentially in a specific location of a given environment (the place field of the place cell). In the present study, place cells were recorded from the hippocampus in a recently-developed rat model of AD (Tg-F344 AD) at an age (12-20 months) at which the AD rats showed marked spatial memory deficits. Place cells in the CA2 and CA3 pyramidal regions of the hippocampus in AD rats showed sharply reduced spatial fidelity relative to wild-type (WT) rats. In contrast, spiking activity of place cells recorded in region CA1 in AD rats showed good spatial fidelity that was similar to CA1 place cells in WT rats. Oral administration of the M1 muscarinic acetylcholine receptor agonist VU0364572 impacted place cell firing rates in CA1 and CA2/3 hippocampal regions, but did not improve the spatial fidelity of CA2/3 hippocampal place cells in AD rats. The results indicated that, to the extent the spatial memory impairment in AD rats was attributable to hippocampal dysfunction, the memory impairment was more attributable to dysfunction in hippocampal regions CA2 and CA3 rather than CA1.

From spikes to space: reconstructing features of the environment from spikes alone

A common stimulus reconstruction paradigm involves first computing the receptive fields of recorded neurons (using both spike trains and the presented stimuli), and then using the receptive field information together with neuronal activity in order to predict the pattern of stimuli based on local stimulus features. Some brain regions (such as hippocampus) undergo remapping of receptive fields, depending on context. How do downstream neurons integrate the mosaic of individual neuronal responses, with potentially varying receptive fields, to extract global characteristics of presented stimuli? In rodent dorsal hippocampus spatial information is encoded by place cells, i.e. pyramidal cells that fire in a restricted convex area of the spatial environment, and are mostly silent outside. The receptive fields of individual place cells (place fields) can be thus thought of as small convex domains in a two-dimensional environment. The place fields for the same neurons re-map from one spatial environment to another. In this work we show how certain global features of a spatial environment can be computed from hippocampal spiking activity alone. In particular, we consider a variety of two-dimensional environments which differ in the number of obstacles (or holes) constraining the region accessible to a freely-foraging rat. Assuming only basic properties of hippocampal place fields, we construct an algorithm that distinguishes between these different environments by computing standard topological invariants (homology groups) from population spiking activity. These invariants precisely determine the numbers of obstacles/holes in the environment – and can be computed without ever using any position-tracking information, or any other feature of the rat's trajectory through space. In particular, we never compute place fields or any other correlations between cell firing and external stimuli. We tested the algorithm using simulated data, staying as close as possible to physiological parameters seen in real data. For each of five distinct environments, open field and N-obstacle environments for N = 1,..,4, we simulated place cell spike trains corresponding to a random walk. (Place fields in different environments were completely unrelated.) The algorithm correctly identified each environment from the population spiking data. Furthermore, on shuffled data sets, the computed homology groups reflected high-dimensional, non-physical environments. We have thus shown that global features of the spatial environment can be reconstructed from hippocampal place cell spiking activity alone.

Construction and analysis of non-Gaussian spatial models of neural spiking activity

The spiking activity of rat CA1 hippocampal place cells during open field foraging can be described with stimulus-response models based on an inhomogeneous Poisson (IP) interspike interval probability model. The spatial structure of this model has been previously represented as a simple Gaussian surface. We analyze four new spatial models, two extensions of the Gaussian surface, a surface based on a logistic transformation, and a surface constructed by using Zernike polynomials. Goodness-of-fit analysis based on the Bayesian information criterion (BIC) shows that the Zernike polynomial surfaces give the most accurate description of the spatial place cell spiking activity under this experimental paradigm.

The time-rescaling theorem and its application to neural spike train data analysis.

Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model's validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point process neural spike train models, especially for histogram-based models such as perstimulus time histograms (PSTH) and rate functions estimated by spike train smoothing. The time-rescaling theorem is a well-known result in probability theory, which states that any point process with an integrable conditional intensity function may be transformed into a Poisson process with unit rate. We describe how the theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains. We apply these tests in two examples: a comparison of PSTH, inhomogeneous Poisson, and inhomogeneous Markov interval models of neural spike trains from the supplementary eye field of a macque monkey and a comparison of temporal and spatial smoothers, inhomogeneous Poisson, inhomogeneous gamma, and inhomogeneous inverse gaussian models of rat hippocampal place cell spiking activity. To help make the logic behind the time-rescaling theorem more accessible to researchers in neuroscience, we present a proof using only elementary probability theory arguments. We also show how the theorem may be used to simulate a general point process model of a spike train. Our paradigm makes it possible to compare parametric and histogram-based neural spike train models directly. These results suggest that the time-rescaling theorem can be a valuable tool for neural spike train data analysis.

Diagnostic methods for statistical models of place cell spiking activity

We analyze the stochastic structure of CA1 hippocampal place cell spiking activity with stimulus–response models based on inhomogeneous Poisson (IP), inhomogeneous gamma (IG) and inhomogeneous inverse Gaussian (IIG) interspike interval probability densities that have Markov dependence. We present a technique based on quantile–quantile (Q–Q) plots derived from the intensity-rescaling transformation, and use it along with Akaike's (AIC) and Bayesian (BIC) information critieria to assess model goodness of fit. The Q–Q plots give readily interpretable, graphical diagnostic methods of the model fits, and show that the IG and IIG models give more accurate descriptions of place cell spiking activity than the IP model.

Construction and analysis of non-Poisson stimulus-response models of neural spiking activity

A paradigm for constructing and analyzing non-Poisson stimulus-response models of neural spike train activity is presented. Inhomogeneous gamma (IG) and inverse Gaussian (IIG) probability models are constructed by generalizing the derivation of the inhomogeneous Poisson (IP) model from the exponential probability density. The resultant spike train models have Markov dependence. Quantile–quantile (Q–Q) plots and Kolmogorov–Smirnov (K–S) plots are developed based on the rate-rescaling theorem to assess model goodness-of-fit. The analysis also expresses the spike rate function of the neuron directly in terms of its interspike interval (ISI) distribution. The methods are illustrated with an analysis of 34 spike trains from rat CA1 hippocampal pyramidal neurons recorded while the animal executed a behavioral task. The stimulus in these experiments is the animal's position in its environment and the response is the neural spiking activity. For all 34 pyramidal cells, the IG and IIG models gave better fits to the spike trains than the IP. The IG model more accurately described the frequency of longer ISIs, whereas the IIG model gave the best description of the burst frequency, i.e. ISIs≤20 ms. The findings suggest that bursts are a significant component of place cell spiking activity even when position and the background variable, theta phase, are taken into account. Unlike the Poisson model, the spatial and temporal rate maps of the IG and IIG models depend directly on the spiking history of the neurons. These rate maps are more physiologically plausible since the interaction between space and time determines local spiking propensity. While this statistical paradigm is being developed to study information encoding by rat hippocampal neurons, the framework should be applicable to stimulus-response experiments performed in other neural systems.

A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells

The problem of predicting the position of a freely foraging rat based on the ensemble firing patterns of place cells recorded from the CA1 region of its hippocampus is used to develop a two-stage statistical paradigm for neural spike train decoding. In the first, or encoding stage, place cell spiking activity is modeled as an inhomogeneous Poisson process whose instantaneous rate is a function of the animal's position in space and phase of its theta rhythm. The animal's path is modeled as a Gaussian random walk. In the second, or decoding stage, a Bayesian statistical paradigm is used to derive a nonlinear recursive causal filter algorithm for predicting the position of the animal from the place cell ensemble firing patterns. The algebra of the decoding algorithm defines an explicit map of the discrete spike trains into the position prediction. The confidence regions for the position predictions quantify spike train information in terms of the most probable locations of the animal given the ensemble firing pattern. Under our inhomogeneous Poisson model position was a three to five times stronger modulator of the place cell spiking activity than theta phase in an open circular environment. For animal 1 (2) the median decoding error based on 34 (33) place cells recorded during 10 min of foraging was 8.0 (7.7) cm. Our statistical paradigm provides a reliable approach for quantifying the spatial information in the ensemble place cell firing patterns and defines a generally applicable framework for studying information encoding in neural systems.