Orientation behavior of butterflyfishes (family Chaetodontidae) on coral reefs: spatial learning of route specific landmarks and cognitive maps

Sharp wave/ripple (SWR, 150–250 Hz) hippocampal events have long been postulated to be involved in memory consolidation. However, more recent work has investigated SWRs that occur during active waking behaviour: findings that suggest that SWRs may also play a role in cell assembly strengthening or spatial working memory. Do such theories of SWR function apply to animal learning? This review discusses how general theories linking SWRs to memory-related function may explain circuit mechanisms related to rodent spatial learning and to the associated stabilization of new cognitive maps.

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Mental Representation

How the Brain Makes Decisions ◽

10.1093/oso/9780198824367.003.0012 ◽

2020 ◽

pp. 84-88

Author(s):

Thomas Boraud

Keyword(s):

Spatial Learning ◽

Mental Representation ◽

Declarative Memory ◽

Cognitive Maps ◽

Procedural Memory ◽

Cognitive Map ◽

The Other ◽

Spatial Relationships ◽

American Psychologist

This chapter focuses on the neural substrate of mental representation and cognitive maps. In 1948, American psychologist Edward Chance Tolman postulated that spatial learning requires a representation of the environment in which a subject evolves. This concept has been popularized under the term ‘cognitive map’. These maps would retain information about the spatial relationships between different places, which supposes the existence of a coordinate system, or referential. The chapter then considers the role of the hippocampus in memory processes. According to psychologists, there are two types of memory: declarative and procedural. Procedural memory describes the ability to reproduce learned behaviour. On the other hand, declarative memory is based on very different processes. Whatever form it takes, it undoubtedly requires the construction of a mental representation. This mental representation is likened to the cognitive maps theorized by Tolman.

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From Topological Analyses to Functional Modeling: The Case of Hippocampus

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2020.593166 ◽

2021 ◽

Vol 14 ◽

Author(s):

Yuri Dabaghian

Keyword(s):

Spatial Learning ◽

Functional Model ◽

Cognitive Maps ◽

Functional Modeling ◽

Memory Processing ◽

Neuronal Ensembles ◽

Brain Waves ◽

Data Analyses ◽

Brain Part ◽

Different Levels

Topological data analyses are widely used for describing and conceptualizing large volumes of neurobiological data, e.g., for quantifying spiking outputs of large neuronal ensembles and thus understanding the functions of the corresponding networks. Below we discuss an approach in which convergent topological analyses produce insights into how information may be processed in mammalian hippocampus—a brain part that plays a key role in learning and memory. The resulting functional model provides a unifying framework for integrating spiking data at different timescales and following the course of spatial learning at different levels of spatiotemporal granularity. This approach allows accounting for contributions from various physiological phenomena into spatial cognition—the neuronal spiking statistics, the effects of spiking synchronization by different brain waves, the roles played by synaptic efficacies and so forth. In particular, it is possible to demonstrate that networks with plastic and transient synaptic architectures can encode stable cognitive maps, revealing the characteristic timescales of memory processing.

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