Statistical Analysis of Hippocampal Information Encoding


Brown E.(Executive)

Project Supported by Public Organizations in Other Countries, 2000 - 2009

  • Project Type: Project Supported by Public Organizations in Other Countries
  • Begin Date: February 2000
  • End Date: March 2009

Project Abstract

The spiking activity of CA1 place cells in the rodent hippocampus correlates with both the animal's position in its environment and the phase of the theta rhythm as the rodent performs spatial behavioral tasks. Recently developed multiple electrode arrays techniques for recording simultaneous spiking activity of many hippocampal place cells while measuring neurobehavioral and physiological variables, greatly facilitates the study of spatial information encoded by these neurons. The study of hippocampal spatial information encoding with multiple electrode arrays is representative of an increasingly common and challenging experimental/data analysis problem in neuroscience: the use of high- dimensional, simultaneously recorded discrete and continuous data to analyze the relation between neuronal population activity and physiologic correlates or external stimuli. The proper analysis of these data requires the development of new statistical methods because currently available techniques for signal processing are not entirely appropriate. Therefore, the specific aims of this study are to use spiking activity of data from ensembles of place cells in the CA1 region of the rodent hippocampus collected under specifically designed, linear and open environment protocols to test the following hypotheses: 1) Accurate statistical models can be developed to describe how the ensemble spiking activity of hippocampal place cells encodes position of the animal in its environment, and how this encoding is modulated by phase of the theta rhythm, direction of motion and running velocity; 2) accurate statistical models can be developed to describe how the stochastic structure in the animal's path on the linear track and in the open environment protocols; 3) Statistically sound algorithms can be developed to decode the representation of spatial information (animal's position in its environment) in the ensemble firing patterns of the hippocampal place cells: and 4) The methods developed in Specific Aims 1-3 can be used to study 4 problems in hippocampal information encoding: a) characterizing the dynamics of place cell formation during the animal's exposures to novel environments; b) assessing evidence in spike train firing patterns for trajectory planning by the animal as it executes specific behavioral tasks; c) measuring the magnitude of position information replay when place cell ensembles reactivate during the animal's sleep episodes; and 4) measuring the effect of NMDA receptor loss in the CA1 region on the ability of the animal to form spatial representations in CA1 and in downstream structures such as the sibiculum and the deep entorhinal cortex. The experimental methods will include analysis of multi-unit spike train data with inhomogeneous gamma distribution point processes to model place cell spiking activity; discrete and continuous time Gaussian autoregression processes to model the path of the animal through its environment; and non-linear recursive filter algorithms based on Bayesian statistical theory for spike train decoding and for determining confidence states for the position estimates. The broad long-term objectives are to develop a statistical paradigm tailor-made for the analysis of multiple electrode array data and to apply it in current research on how the hippocampus represents spatial information. The health benefits will include methods to help improve current knowledge of the basic neurophysiology of short-memory formation and the encoding of information into long-term memory. This research will also provide a set of statistical tools with which neuroscientists will be able to study information representation and transmission in neural systems using multi-unit activity data recorded along with relevant biological signals.