Physiological mechanisms of hippocampal memory processing : experiments and applied adaptive filtering

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2008.Includes bibliographical references (p. 144-156).The hippocampus is necessary for the formation and storage of episodic memory, however, the computations within and between hippocampal subregions (CA1, CA3, and dentate gyrus) that mediate these memory processing functions are not completely understood. We investigate by recording in the hippocampal subregions as rats execute an augmented linear track task. From these recordings, we construct ensemble rate representations using a point process adaptive filter to characterize single-unit activity from each subregion. We compared the dynamics of these rate representations by computing average max rate and average rate modulation during different experimental epochs and on different segments of the track. We found that the representations in CA3 were modulated most when compared to CAl and DG during the first 5 minutes of experience. With more experience, we found the average rate modulation decreased gradually across all areas and converged to values that were not statistically different. These results suggest a specialized role for CA3 during initial context acquisition, and suggest that rate modulation becomes coherent across HPC subregions after familiarization. Information transfer between the hippocampus and neocortex is important for the consolidation of spatial and episodic memory. This process of information transfer is referred to as memory consolidation and may be mediated by a phenomena called "replay." We know that the process of replay is associated with a rise in multi-unit activity and the presence of ripples (100-250 Hz oscillations lasting from 75ms to 100ms) in CAl. Because ripples result from the same circuits as replay activity, the features of the ripple may allow us to deduce the mechanisms for replay induction and the nature of information transmitted during replay events.(cont.) Because ripples are relatively short events, analytical methods with limited temporal-spectral resolution are unable to fully characterize all the structure of ripples. In the thesis, we develop a framework for characterizing, classifying, and detecting ripples based on instantaneous frequency and instantaneous frequency modulation. The framework uses an autoregressive model for spectral-temporal analysis in combination with a Kalman filter for sample-to-sample estimates of frequency parameters. We show that the filter is flexible in the degree of smoothing as well as robust in the estimation of frequency. We demonstrate that under the proposed framework ripples can be classified based on high or low frequency, and positive or negative frequency modulation; can combine amplitude and frequency information for selective detection of ripple events; and can be used to determine the number of ripples participating in "long ripple" events.by David P. Nguyen.Ph.D

Glucose-powered neuroelectronics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 157-164).A holy grail of bioelectronics is to engineer biologically implantable systems that can be embedded without disturbing their local environments, while harvesting from their surroundings all of the power they require. As implantable electronic devices become increasingly prevalent in scientific research and in the diagnosis, management, and treatment of human disease, there is correspondingly increasing demand for devices with unlimited functional lifetimes that integrate seamlessly with their hosts in these two ways. This thesis presents significant progress toward establishing the feasibility of one such system: A brain-machine interface powered by a bioimplantable fuel cell that harvests energy from extracellular glucose in the cerebrospinal fluid surrounding the brain. The first part of this thesis describes a set of biomimetic algorithms and low-power circuit architectures for decoding electrical signals from ensembles of neurons in the brain. The decoders are intended for use in the context of neural rehabilitation, to provide paralyzed or otherwise disabled patients with instantaneous, natural, thought-based control of robotic prosthetic limbs and other external devices. This thesis presents a detailed discussion of the decoding algorithms, descriptions of the low-power analog and digital circuit architectures used to implement the decoders, and results validating their performance when applied to decode real neural data. A major constraint on brain-implanted electronic devices is the requirement that they consume and dissipate very little power, so as not to damage surrounding brain tissue. The systems described here address that constraint, computing in the style of biological neural networks, and using arithmetic-free, purely logical primitives to establish universal computing architectures for neural decoding. The second part of this thesis describes the development of an implantable fuel cell powered by extracellular glucose at concentrations such as those found in the cerebrospinal fluid surrounding the brain. The theoretical foundations, details of design and fabrication, mechanical and electrochemical characterization, as well as in vitro performance data for the fuel cell are presented.by Benjamin Isaac Rapoport.Ph.D