Models that contain the Implementer : Hasselmo, Michael E [hasselmo at bu.edu]

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    Models   Description
1.  A reinforcement learning example (Sutton and Barto 1998)
This MATLAB script demonstrates an example of reinforcement learning functions guiding the movements of an agent (a black square) in a gridworld environment. See at the top of the matlab script and the book for more details.
2.  Biologically-plausible models for spatial navigation (Cannon et al 2003)
Hypotheses about how parahippocampal and hippocampal structures may be involved in spatial navigation tasks are implemented in a model of a virtual rat navigating through a virtual environment in search of a food reward. The model incorporates theta oscillations to separate encoding from retrieval and yields testable predictions about the phase relations of spiking activity to theta oscillations in different parts of the hippocampal formation at various stages of the behavioral task. See paper for more and details.
3.  Fixed point attractor (Hasselmo et al 1995)
"... In the model, cholinergic suppression of synaptic transmission at excitatory feedback synapses is shown to determine the extent to which activity depends upon new features of the afferent input versus components of previously stored representations. ..." See paper for more and details. The MATLAB script demonstrates the model of fixed point attractors mediated by excitatory feedback with subtractive inhibition in a continuous firing rate model.
4.  Hippocampal context-dependent retrieval (Hasselmo and Eichenbaum 2005)
"... The model simulates the context-sensitive firing properties of hippocampal neurons including trial-specific firing during spatial alternation and trial by trial changes in theta phase precession on a linear track. ..." See paper for more and details.
5.  Prefrontal cortical mechanisms for goal-directed behavior (Hasselmo 2005)
".. a model of prefrontal cortex function emphasizing the influence of goal-related activity on the choice of the next motor output. ... Different neocortical minicolumns represent distinct sensory input states and distinct motor output actions. The dynamics of each minicolumn include separate phases of encoding and retrieval. During encoding, strengthening of excitatory connections forms forward and reverse associations between each state, the following action, and a subsequent state, which may include reward. During retrieval, activity spreads from reward states throughout the network. The interaction of this spreading activity with a specific input state directs selection of the next appropriate action. Simulations demonstrate how these mechanisms can guide performance in a range of goal directed tasks, and provide a functional framework for some of the neuronal responses previously observed in the medial prefrontal cortex during performance of spatial memory tasks in rats."

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