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Michael E. Hasselmo

Michael Hasselmo is Professor of Psychology and Director of the Computational Neurophysiology Laboratory at Boston University, where he is also a faculty member in the Center for Memory and Brain and the Program in Neuroscience and principal investigator on grants from the National Institute of Mental Health and the Office of Naval Research.

Titles by This Author

Brain Mechanisms of Episodic Memory

Episodic memory proves essential for daily function, allowing us to remember where we parked the car, what time we walked the dog, or what a friend said earlier. In How We Remember, Michael Hasselmo draws on recent developments in neuroscience to present a new model describing the brain mechanisms for encoding and remembering such events as spatiotemporal trajectories. He reviews physiological breakthroughs on the regions implicated in episodic memory, including the discovery of grid cells, the cellular mechanisms of persistent spiking and resonant frequency, and the topographic coding of space and time. These discoveries inspire a theory for understanding the encoding and retrieval of episodic memory not just as discrete snapshots but as a dynamic replay of spatiotemporal trajectories, allowing us to "retrace our steps" to recover a memory.

In the main text of the book, he presents the model in narrative form, accessible to scholars and advanced undergraduates in many fields. In the appendix, he presents the material in a more quantitative style, providing mathematical descriptions appropriate for advanced undergraduates and graduate students in neuroscience or engineering.

Titles by This Editor

Proceedings of the 1995 Conference

The past decade has seen greatly increased interaction between theoretical work in neuroscience, cognitive science and information processing, and experimental work requiring sophisticated computational modeling. The 152 contributions in NIPS 8 focus on a wide variety of algorithms and architectures for both supervised and unsupervised learning. They are divided into nine parts: Cognitive Science, Neuroscience, Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Vision, Applications, and Control.

Chapters describe how neuroscientists and cognitive scientists use computational models of neural systems to test hypotheses and generate predictions to guide their work. This work includes models of how networks in the owl brainstem could be trained for complex localization function, how cellular activity may underlie rat navigation, how cholinergic modulation may regulate cortical reorganization, and how damage to parietal cortex may result in neglect.

Additional work concerns development of theoretical techniques important for understanding the dynamics of neural systems, including formation of cortical maps, analysis of recurrent networks, and analysis of self- supervised learning.

Chapters also describe how engineers and computer scientists have approached problems of pattern recognition or speech recognition using computational architectures inspired by the interaction of populations of neurons within the brain. Examples are new neural network models that have been applied to classical problems, including handwritten character recognition and object recognition, and exciting new work that focuses on building electronic hardware modeled after neural systems.

A Bradford Book