: Spatial relationships provide the context for most adaptive behaviors and the framework for episodic memory. Their internal representation involves a transformation from the egocentric coordinates of raw sensory inputs into an allocentric framework, and ultimately into motor outputs that are also represented in terms of the spatial coordinates of the goal. Mechanisms of spatial coding are highly conserved across mammals, and their study provides insight into the neural basis of higher cognitive processes and factors that contribute to abnormal function in early development, aging, brain injury, disease, and substance abuse. The research is guided by an evolving theory in which landmarks and events are mapped onto 2-dimensional metric frameworks or """"""""charts"""""""" that are used to connect representations of brain structures. Their coordinates are signaled by the activity of groups of hippocampal """"""""place"""""""" cells. The metric is based on a network architecture that links chart coordinates through linear and angular self-motion information (""""""""path-integration""""""""). Charts are preconfigured in the synaptic connections, independently of external input, but they become associated with landmark information through exploration. This enables appropriate framework selection, correction for the drift error that is inherent in any path integration system and the use of efficient, vector-like operations to compute trajectories to goals, although the latter computations may be carried out not in hippocampus, but in its neocortical targets. The theory is explored using simultaneous recordings from large groups of neurons (50-150) and the interpretation of neural population codes for spatial experiences and behaviors. Proposed technological developments should increase these numbers sufficiently for accurate reading of population codes on the short time-scales necessary to study computational mechanisms and the interaction of neural ensembles between multiple brain regions. The current research questions are: 1) How are environmental structure and the spatial context of events encoded in hippocampal activity? 2) Are either navigational goals or memories of recently visited locations explicitly represented in the hippocampus or neocortex and are the former encoded in a manner that could subserve vector-like computations of shortest routes? 3) What is the role of the fascia dentata, a major hippocampal subfield that is required for spatial learning, but not for place specific firing in hippocampal pyramidal cells? 4) Are hippocampal plasticity mechanisms that presumably underlie spatial learning modulated by the suppression of interneuron firing that occurs in novel environments? The data will be considered in relation to numerical simulations of the proposed mechanisms using simplified, spiking-neuron models.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS020331-14
Application #
2431133
Study Section
Neurology B Subcommittee 2 (NEUB)
Program Officer
Broman, Sarah H
Project Start
1984-03-01
Project End
2001-05-31
Budget Start
1997-06-01
Budget End
1998-05-31
Support Year
14
Fiscal Year
1997
Total Cost
Indirect Cost
Name
University of Arizona
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
City
Tucson
State
AZ
Country
United States
Zip Code
85721
Maurer, Andrew P; Burke, Sara N; Lipa, Peter et al. (2012) Greater running speeds result in altered hippocampal phase sequence dynamics. Hippocampus 22:737-47
Navratilova, Zaneta; Giocomo, Lisa M; Fellous, Jean-Marc et al. (2012) Phase precession and variable spatial scaling in a periodic attractor map model of medial entorhinal grid cells with realistic after-spike dynamics. Hippocampus 22:772-89
Euston, David R; Gruber, Aaron J; McNaughton, Bruce L (2012) The role of medial prefrontal cortex in memory and decision making. Neuron 76:1057-70
Johnson, Lise A; Euston, David R; Tatsuno, Masami et al. (2010) Stored-trace reactivation in rat prefrontal cortex is correlated with down-to-up state fluctuation density. J Neurosci 30:2650-61
Colgin, Laura L; Leutgeb, Stefan; Jezek, Karel et al. (2010) Attractor-map versus autoassociation based attractor dynamics in the hippocampal network. J Neurophysiol 104:35-50
Takehara-Nishiuchi, Kaori; McNaughton, Bruce L (2008) Spontaneous changes of neocortical code for associative memory during consolidation. Science 322:960-3
Euston, David R; Tatsuno, Masami; McNaughton, Bruce L (2007) Fast-forward playback of recent memory sequences in prefrontal cortex during sleep. Science 318:1147-50
Cowen, Stephen L; McNaughton, Bruce L (2007) Selective delay activity in the medial prefrontal cortex of the rat: contribution of sensorimotor information and contingency. J Neurophysiol 98:303-16
Maurer, Andrew P; McNaughton, Bruce L (2007) Network and intrinsic cellular mechanisms underlying theta phase precession of hippocampal neurons. Trends Neurosci 30:325-33
Lansink, Carien S; Bakker, Mattijs; Buster, Wietze et al. (2007) A split microdrive for simultaneous multi-electrode recordings from two brain areas in awake small animals. J Neurosci Methods 162:129-38

Showing the most recent 10 out of 60 publications