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Robust spatial memory maps encoded by networks with transient connections.

ABSTRACT: The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space-a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map is transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable representation of space using a network that constantly changes its architecture? We address this question using a computational framework that allows evaluating the effect produced by the decaying connections between simulated hippocampal neurons on the properties of the cognitive map. Using novel Algebraic Topology techniques, we demonstrate that emergence of stable cognitive maps produced by networks with transient architectures is a generic phenomenon. The model also points out that deterioration of the cognitive map caused by weakening or lost connections between neurons may be compensated by simulating the neuronal activity. Lastly, the model explicates the importance of the complementary learning systems for processing spatial information at different levels of spatiotemporal granularity.

SUBMITTER: Babichev A

PROVIDER: S-EPMC6161922 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Robust spatial memory maps encoded by networks with transient connections.

Babichev Andrey A Morozov Dmitriy D Dabaghian Yuri Y

PLoS computational biology 20180918 9

The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space-a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map is transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable ...[more]

PMID: 30226836

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Project description:Our ability to recognize others by their facial features is at the core of human social interaction, yet this ability varies widely within the general population, ranging from developmental prosopagnosia to "super-recognizers". Previous work has focused mainly on the contribution of neural activity within the well described face network to this variance. However, given the nature of face memory in everyday life, and the social context in which it takes place, we were interested in exploring how the collaboration between different networks outside the face network in humans (measured through resting state connectivity) affects face memory performance. Fifty participants (men and women) were scanned with fMRI. Our data revealed that although the nodes of the face-processing network were tightly coupled at rest, the strength of these connections did not predict face memory performance. Instead, face recognition memory was dependent on multiple connections between these face patches and regions of the medial temporal lobe memory system (including the hippocampus), and the social processing system. Moreover, this network was selective for memory for faces, and did not predict memory for other visual objects (cars). These findings suggest that in the general population, variability in face memory is dependent on how well the face processing system interacts with other processing networks, with interaction among the face patches themselves accounting for little of the variance in memory ability.SIGNIFICANCE STATEMENT Our ability to recognize and remember faces is one of the pillars of human social interaction. Face recognition however is a very complex skill, requiring specialized neural resources in visual cortex, as well as memory, identity, and social processing, all of which are inherent in our real-world experience of faces. Yet in the general population, people vary greatly in their face memory abilities. Here we show that in the neural domain this variability is underpinned by the integration of visual, memory and social circuits, with the strength of the connections between these circuits directly linked to face recognition ability.

Dataset Information

Robust spatial memory maps encoded by networks with transient connections.

Publications

Robust spatial memory maps encoded by networks with transient connections.

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