Cited by 91Memory and Neural MechanismsSpatial Cognition and NavigationNeural Networks and Applications

Purposive behavior and cognitive mapping: a neural network model

Schmajuk, N. A., Thieme, A. D.·1992·Biological Cybernetics

Abstract

This study presents a real-time, biologically plausible neural network approach to purposive behavior and cognitive mapping. The system is composed of (a) an action system, consisting of a goal-seeking neural mechanism controlled by a motivational system; and (b) a cognitive system, involving a neural cognitive map. The goal-seeking mechanism displays exploratory behavior until either (a) the goal is found or (b) an adequate prediction of the goal is generated. The cognitive map built by the network is a topological map, i.e., it represents only the adjacency, but not distances or directions, between places. The network has recurrent and non-recurrent properties that allow the reading of the cognitive map without modifying it. Two types of predictions are introduced: fast-time and real-time predictions. Fast-time predictions are produced in advance of what occurs in real time, when the information stored in the cognitive map is used to predict the remote future. Real-time predictions are generated simultaneously with the occurrence of environmental events, when the information stored in the cognitive map is being updated. Computer simulations show that the network successfully describes latent learning and detour behavior in rats. In addition, simulations demonstrate that the network can be applied to problem-solving paradigms such as the Tower of Hanoi puzzle.

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Crossref · 10.1007/bf00201023

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  • A number of neurobiologically inspired, fully mechanistic computational models of navigational behavior have been developed to account for the ability of animals relying on allocentric frameworks to perform path planning without resorting to mentalistic explanations.

    “A number of neurobiologically inspired, fully mechanistic computational models of navigational behavior have been developed to account for the ability of animals relying on allocentric frameworks to perform path planning, without the need to resort to “mentalistic” explanations”

    From Spatial Cognition in Teleost Fish: Strategies and Mechanisms (2021) · cites this paper as [93]

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Authors
Schmajuk, N. A., & Thieme, A. D.
Affiliations
Northwestern University (US)
Venue
Biological Cybernetics
Published
1992-06
Volume
67
Issue
2
Pages
165-174
ISSN
  • 0340-1200 (ISSN-L)
  • 1432-0770
OpenAlex
W1999124634
Cited by
91
References
25
Citations by year
2024: 1 · 2023: 2 · 2022: 1 · 2021: 2 · 2020: 3 · 2019: 19 · 2018: 2 · 2016: 1 · 2015: 1 · 2014: 1 · 2012: 1
FWCI
1.47
Type
journal article
Language
en
Open access
closed
Locations
  • Biological Cybernetics (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (2s07rr007028-26)
  • National Center for Research Resources (SO7 RR07028-22)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
  • Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords
purposive action, cognitive mapping, neural network model, motivation system, topological maps, recurrent neural networks, real-time prediction, latent learning, detour behavior, rats, Tower of Hanoi, problem solving, biologically plausible models
MeSH terms
Animals, Behavior, Animal, Cognition, Computer Simulation, Exploratory Behavior, Learning, Mathematics, Neural Networks, Computer

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