The loss of memory or even the loss of the ability to store new declarative memories is devastating, as we know from the life of the patient H.M. Unfortunately, such types of memory loss, although perhaps not as severe, also occur from head injury, with aging, and sometimes with menopause. Because an adequate ability to learn and to remember is fundamental to normal cognitive behavior and because many cognitive behaviors hinge upon stored declarative memories, it is important to understand the role various brain structures play in forming such memories. it is our long-term goal to understand how the hippocampus initially forms declarative memories and then interacts with the cerebral cortex to store long-term memories there. Such knowledge should facilitate repairing the effects of aging or preventing the effects of menopause on memory processes. Thus, our goal here is to provide a quantitative understanding of hippocampal function by simulating biological plausible hippocampal-like networks with inputs that are relevant to cognitive/behavioral theories of hippocampal function.
The specific aims of this proposal are: 1) to create a minimal, biologically plausible model of the hippocampus functioning as a cognitive map; 2) to test, and minimally modify if necessary, this model using other paradigmatic behaviors that require the hippocampus; and 3) to begin development of the simplest possible biological model that can sensibly predict the patterns of hippocampal cell firing for behavioral situations relevant to hippocampal function. Using simplified models of the hippocampus, we will demonstrate that the archetypal hippocampal anatomy and associated physiologies can reproduce the functions ascribed to the hippocampus, including context formation, spatial mapping (i.e., cognitive mapping), and flexible memory representations (Eichenbaum et al., '92). We will discover which functional properties of the hippocampus arise from the sparse recurrent connectivity of CA3 and which properties rely on other aspects of hippocampal anatomy and physiology. Technically, we will use the methods of computer simulation to model hippocampal cell firing and cognitive behavior. By studying reduced models which are gradually made more complex, we will achieve a fundamental understanding of the critical anatomy and the critical physiology for the functions which we study. Because our laboratory is involved in basic neurophysiological and anatomical studies of the hippocampal formation, we are strongly motivated to produce models with the greatest possible biological plausibility and relevance.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH048161-06
Application #
2675017
Study Section
Cognitive Functional Neuroscience Review Committee (CFN)
Project Start
1991-04-01
Project End
1999-05-31
Budget Start
1998-04-10
Budget End
1999-05-31
Support Year
6
Fiscal Year
1998
Total Cost
Indirect Cost
Name
University of Virginia
Department
Neurosurgery
Type
Schools of Medicine
DUNS #
001910777
City
Charlottesville
State
VA
Country
United States
Zip Code
22904
Sullivan, D W; Levy, W B (2004) Quantal synaptic failures enhance performance in a minimal hippocampal model. Network 15:45-67
Rodriguez, Paul; Levy, William B (2004) Configural representations in transverse patterning with a hippocampal model. Neural Netw 17:175-90
Levy, William B; Baxter, Robert A (2002) Energy-efficient neuronal computation via quantal synaptic failures. J Neurosci 22:4746-55
Shon, A P; Wu, X B; Sullivan, D W et al. (2002) Initial state randomness improves sequence learning in a model hippocampal network. Phys Rev E Stat Nonlin Soft Matter Phys 65:031914
Rodriguez, P; Levy, W B (2001) A model of hippocampal activity in trace conditioning: where's the trace? Behav Neurosci 115:1224-38
Smith, A C; Wu, X B; Levy, W B (2000) Controlling activity fluctuations in large, sparsely connected random networks. Network 11:63-81
Greene, A J; Prepscius, C; Levy, W B (2000) Primacy versus recency in a quantitative model: activity is the critical distinction. Learn Mem 7:48-57
August, D A; Levy, W B (1999) Temporal sequence compression by an integrate-and-fire model of hippocampal area CA3. J Comput Neurosci 6:71-90
Levy, W B; Delic, H; Adelsberger-Mangan, D M (1999) The statistical relationship between connectivity and neural activity in fractionally connected feed-forward networks. Biol Cybern 80:131-9
Wu, X; Tyrcha, J; Levy, W B (1998) A neural network solution to the transverse patterning problem depends on repetition of the input code. Biol Cybern 79:203-13

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