Models of behavior and spike timing in sequence memory

Boston University (Charles River Campus), Boston MA

Investigators

Linked publications & trials

Abstract

Computational models of the hippocampal formation, entorhinal cortex and prefrontal cortex will be used to[unreadable] simulate memory-guided behavior in both rat and human behavioral tasks. These simulations will extend[unreadable] previous modeling work in this laboratory (Hasselmo et al., 2002b; Fransen et al., 2002; Koene et al., 2003;[unreadable] Hasselmo, 2005a; Koene and Hasselmo, 2005; Hasselmo and Eichenbaum, 2005) to generate predictions[unreadable] about physiological data in a range of different memory-guided tasks. Selection of behavioral actions in the[unreadable] model depends on the encoding and context dependent retrieval of sequences of input stimuli (i.e.[unreadable] episodes). The models will generate predictions for specific research projects in the center, including:[unreadable] 1.) generation of predictions about the timing of neuron firing relative to stimuli and hippocampal theta[unreadable] rhythm during performance of the order recognition task with odors in rats, and the[unreadable] magnitude of fMRI activation associated with correct versus incorrect performance of an order recognition[unreadable] task in humans,[unreadable] 2.) generation of predictions about context-dependent properties and theta phase of neuronal firing in the[unreadable] odor sequence disambiguation task, and the magnitude of fMRI activation[unreadable] associated with choice in a disambiguation task in humans,[unreadable] 3.) generation of predictions about the delayed non-match to place (DNMP) task in the T-maze, concerning the timing of splitter cell responses and sequence readout relative to theta rhythm and[unreadable] behavior and the disruption of behavioral responses caused by stimulation at different phases of theta during[unreadable] different task periods.[unreadable] These simulations will use networks of neurons starting with threshold units and building to use of detailed[unreadable] compartmental biophysical simulations, to relate network dynamics to intrinsic currents and subclasses of[unreadable] neurons within the hippocampus and entorhinal cortex. The insights gained from this work will improve our[unreadable] understanding of the normal function of the hippocampus and associated cortex, potentially contributing to[unreadable] treatment of memory impairments in disorders such as Alzheimer's disease or schizophrenia.