John D. Simeral, Ph.D.
Representation and rhythmicity in ensembles of hippocampal neurons

The laboratory of Dr. S.A. Deadwyler and Dr. R.E. Hampson investigates, among other things, the electrophysiology of short-term memory in rats.These studies are useful in establishing (a) the basic mechanisms of short-term memory, (b) the anatomical and functional organization of memory circuits in hippocampus, and (b) the effects of various drugs of abuse on these memory mechanisms. 

When in this lab, I measured the electrical activity of groups of neurons in animals as they perform tasks which require short term memory. We sought in the recorded neural signals new information regarding the principles which govern how individual cells interact in large ensembles to provide the computational power of the brain, specifically with regards to memory. 

While principal cells of the hippocampus are known to encode various features of tasks performed by rats and monkeys, less is known about the cooperative encoding among ensembles of neurons and about the role of modulation and interneurons in information encoding and communication between brain regions. 

Previous studies from this laboratory have shown that neurons in the hippocampal region of the brain are involved in the processing of short-term memory. During performance of a delayed memory task, ensembles of hippocampal cells are activated in a spatial and temporal pattern that reveals that aspects of the behavioral task are actually being represented by the electrical firing of these neurons. Our 1999 article in Nature describes how our data reveal a structural organization underlying short-term spatial and non-spatial memory in the hippocampus. 

Existing analyses of neuronal ensembles utilized linear methods to detect spatiotemporal firing patterns synchronized to external behavioral events (e.g. whenever the rat presses a lever, certain firing activity is seen in the population of neurons). By extending these methods to non-synchronized epochs, we developed a system to to track patterns of neural activity as they evolve through time, as decisions are made and ensembles of neurons change their encoding. We can now identify a select set of behaviorally-relevant ensemble codes in real-time with high reliability and reproducibility .



Research on these projects advanced on three fronts.

(1) Real-time neural interface and decoding [2002]: Using linear and nonlinear discriminant analyses, we developed the capability to categorize individual rat behaviors on the basis of the observed neural spiking activity alone. This involved isolating spikes from many neurons simultaneously and training either linear (canonical discriminant analysis) or nonlinear (supervised backpropogation artificial neural network) models to recognize various neuronal states. After such models were available, we decoded ensemble firing activity in real time by passing neural data from behaviring rats implanted with microwire electrode arrays across an ethernet link to a PC executing Matlab code running our model. Successful decoding of neural activity was demonstrated by successful classification of behavioral events at greater than 90%.

(2) Characterization of rhythmic hippocampal activity in a short-term memory task [2003]: The significance of rhythmic modulation of firing activity within and across brain regions is becoming clear in many lines of neuroscience research, from olfactory encoding to a putative role in consciousness.  Theta oscillations (4-12 Hz) in spatial representation in rat hippocampus are well characterized, though their functional significance has been less convincingly described. We sought to understand the role of theta and gamma oscillations during cognitive, non-spatial processing that occurs during the short-term memory test paradigm DNMTS (delayed-nonmatch-to-sample). We applied a variety of tools to this problem including spectral and modulation analyses, spike train cross-correlations, directed coherence , ICA, and classification testing. 

(3) Independent Components Analysis of human fMRI [2001]: We investigated (2001) independent components analysis (ICA) developed by Bell and Sejnowski at the Salk Institute (and described for fMRI data by McKeown, now at Duke). This method has the potential of revealing novel dynamics of brain functioning during memory tasks because, like nonlinear time series analysis of neuron recordings, it enables continuous, ongoing analysis of patterns of neuron firing activity even when that  activity is not correlated with any obvious, observable event or stimulus. An interactive Matlab software shell was developed around the Bell and Sejnowski neural network implementation of ICA adapted for fMRI data (largely by McKeown) for analysis of fMRI data acquired at Baptist Medical Center under the Biomedical Engineering Program. Capabilities were added including standard fMRI preprocessing, algorithmic out-of-brain voxel removal, multi-frame alignment, spatial- and temporal smoothing and filtering, principle components analysis (PCA), and various visualization and significance testing capabilities. The primary objective of this study was to establish ICA capabilities and to benchmark ICA against our in-house statistical parametric mapping (SPM) capabilities.