Rhythmic oscillations at various well identified frequency bands have been recorded in the brain using EEG (electroencephalogram) techniques during both wakefulness and sleep, and have been linked to various important cognitive and behavioral tasks. This project focuses on two of these rhythms, theta (4 - 12 Hz) and gamma (30 - 80 Hz), that have been observed in the hippocampus and the entorhinal cortex (EC), and have been implicated in learning, memory, spatial navigation and path integration (the ability to calculate a path on the basis of self motion cues). Using biophysical (conductance-based) modeling, dynamical systems techniques and computational simulations, the investigator explores how these rhythms emerge at the single cell and network leves, and what are their dynamic properties. The goal is to understand the basic dynamic and biophysical principles governing the generation of these rhythms over a wide spectrum of interacting levels of organization, ranging from the subcellular, through the cellular to the network leves, and how all this contributes to the functional role of these rhythmic oscillations. At the cellular level, the focus is on the so called stellate cells (SCs) from layer II of the medial EC that display mixed-mode oscillations (subthreshold oscillations interspersed with spikes) in the theta frequency regime. Using reduction of dimensions techniques we uncover a minimal biophysically plausible model that reproduces the observed mixed-mode oscillatory patterns. This model is both nonlinear and multi-scale. The study of its underlying dynamic structure, the so called canard structure, allows the investigator to understand how the observed patterns emerge from the interaction between a persistent sodium and a hyperpolarization-activated currents, as experimentally observed. This knowledge will be used to understand two important aspects of network activity: How SCs process structured information (sinusoidal, noisy and synaptic inputs), in particular how the intrinsic and synaptic currents interact to maintain the SC activity in the theta frequency regime, and how all these properties cooperate to generate rhythmic activity at theta and gamma frequencies in networks that include SCs along with interneurons, pyramidal cells and other cell types. More specifically, the questions of how and under what conditions the same network is able to generate theta and gamma rhythmic activity will be investigated, as well as how the abrupt transitions between both rhythms occur. Single SCs have the potential ability to spike in the gamma frequency regime, but the associated time scale is hidden in single isolated cells and it is uncover in the network level when the level of inhibition is deficient.
This project addresses the general issue of how the brain is able to generate rhythmic activity at various frequency bands as the result of the biophysical properties of the networks that are substrate to these rhythms. A set of problems that are motivated by experimental results and are key to the understanding of the neural circuitries that are substrate to the observed rhythmic oscillations in the EC are considered. The results of this research provide valuable information about the biophysical mechanism of generation of these rhythms, not only in the EC, but also in the hippocampus where cells and networks with similar biophysical and dynamic properties can be found, and which receives direct inputs from the EC. In addition, the results will provide important insights into behavioral issues such as navigation where the theta rhythms in both the hippocampus and the EC plays a relevant role. Finally, this research will shed light into the role that the transition from theta to a hyper-excitable (gamma) frequency regime plays in the generation of epileptic seizures.
Rhythmic oscillations at characteristic frequency bands are ubiquitous in the nervous system. They emerge from the cooperative activity of the participating neurons. Many neuron types display subthreshold membrane potential oscillations in addition to action potentials (spikes). The goal of this project was to understand how these subthreshold oscillations are generated, how the corresponding frequencies are selected, how they depend on the participating ionic currents and other biophysical parameters, and how they affect spiking and network activity. We focused on a particular cell type, the so-called stellate cells of the medial entorhinal cortex, which are part of neuronal networks implicated in cognition and motor behavior. We found that the so-called canard phenomenon is the main player in the mechanism of generation of subthreshold oscillations and mixed-mode oscillations (subthreshold oscillations interpersed with spikes) in stellate cells. Two important ingredientes of the canard mechanism are the characteristic type of nonlinearities and time scale separation between the voltage and ionic current variables. Importantly, we found that the same principles that govern the generation of subthreshold and mixed-mode oscillations in stellate cells are also responsible for the generation of similar phenomena in other models. Moreover, we found that the dynamic structure that gives rise to canard-based oscillations is implicated in the mechanisms of abrupt transitions between firing frequency regimes as the result of recurrent excitation and reduced inhibition. The new paradigm uncovered by this research is expected to have mechanistic implications for future research in the field. This project focused on the mechanisms of generation of subthreshold oscillations and mixed-mode oscillations in stellate cells of the medial entorhinal cortex and their implications for neuronal network dynamics. The results of our research are valid for a large class of models. Therefore, our results and the biophysical and dynamic mechanisms uncovered by this research had, and are expected to continue to have, an impact on the research carried in other areas of the brain as well as other fields of neurosience, chemistry and biology. The activities of this grant had a direct impact on the training of the graduate students working with the PI as well as the diverse undergraduate and graduate students community at both NJIT and Rutgers through direct interaction with the PI, or by attending the Mathematical Biology seminar and Computational neuroscience courses taught by the PI. The results of this research was discussed in weekly seminars at NJIT and presented at international, national and local conferences.
