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Models of Spatial Navigation

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Detour Finding. What has been accomplished so far in modeling spatial navigation: ... Detour finding. Obstacle avoidance. Cognitive map building. SODAS August ... – PowerPoint PPT presentation

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Title: Models of Spatial Navigation


1
Models of Spatial Navigation
  • Horatiu Voicu
  • Computer Science Division
  • The University of Memphis

2
Computational Models of Spatial Navigation
  • Verbal (Deutch, 1960)
  • Behavioral (Reid and Staddon, 1998)
  • Computational
  • Symbolic (Kuipers, 1978)
  • Connectionist (Schmajuk and Thieme,1992)
  • Attractor networks (Samsonovitch and McNaughton,
    1997)

3
What has been accomplished so far in modeling
spatial navigation
  • Path Integration

4
What has been accomplished so far in modeling
spatial navigation
  • Shortcut Finding

5
What has been accomplished so far in modeling
spatial navigation
  • Detour Finding

6
What has been accomplished so far in modeling
spatial navigation
  • Detour Finding

7
What has been accomplished so far in modeling
spatial navigation
  • Cognitive Map Building

8
What has been accomplished so far in modeling
spatial navigation
  • Latent Learning

9
What has been accomplished so far in modeling
spatial navigation
  • Navigation in Large Environments

10
What has been accomplished so far in modeling
spatial navigation
  • Area-Restricted Search

11
What has been accomplished so far in modeling
spatial navigation
  • Explanation of place cells and theta rhythm

12
(Adapted from Thinus-Blanc, 1996)
Temporal Cortex
Frontal Cortex
Parietal Cortex
Cingulate Cortex
Parahippocampal Cortex
Perirhinal Cortex
Presubicullum
Dentate Gyrus
Entorhinal Cortex
Subicullum
CA1
CA3
Parasubicullum
13
Navigation Systems
14
(No Transcript)
15
Key innovations in our approach
  • Integration of different navigation systems in
    one multi-purpose navigation system
  • Demonstration of chaotic mesoscopic space-time
    neurodynamics for perception and action in an
    autonomous agents

16
Accomplishments
Goal finding
Detour finding
Obstacle avoidance
Cognitive map building
17
Challenges
  • Use Chaotic Attractor Networks to
  • Store and retrieve information from cognitive
    maps in simulated and real environments
  • Perform information fusion of different types of
    sensory modalities
  • Perform action planning and selection

18
Environment
19
Environment
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