A network linking scene perception and spatial memory systems in posterior cerebral cortex

AbstractThe neural systems supporting scene-perception and spatial-memory systems of the human brain are well-described. But how do these neural systems interact? Here, using fine-grained individual-subject fMRI, we report three cortical areas of the human brain, each lying immediately anterior to a region of the scene perception network in posterior cerebral cortex, that selectively activate when recalling familiar real-world locations. Despite their close proximity to the scene-perception areas, network analyses show that these regions constitute a distinct functional network that interfaces with spatial memory systems during naturalistic scene understanding. These “place-memory areas” offer a new framework for understanding how the brain implements memory-guided visual behaviors, including navigation.

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A network linking perception and memory systems in posterior cerebral cortex

10.1101/2020.05.25.115147 ◽

2020 ◽

Cited By ~ 1

Author(s):

Adam Steel ◽

Madeleine M. Billings ◽

Edward H. Silson ◽

Caroline E. Robertson

Keyword(s):

Cerebral Cortex ◽

Human Brain ◽

Scene Perception ◽

Memory Systems ◽

Individual Subject ◽

Fine Grained ◽

Network Analyses ◽

Place Memory ◽

Visual Behaviors ◽

Close Proximity

AbstractHere, we report a network of brain areas bridging the spatial-memory and scene-perception systems of the human brain. Using fine-grained individual-subject fMRI, we reveal three cortical areas of the human brain, each lying immediately anterior to a region of the scene perception network in posterior cerebral cortex, that selectively activate when recalling familiar real-world locations. Despite their close proximity to the scene-perception areas, network analyses show that these regions constitute a distinct functional network that interfaces with memory systems during naturalistic scene understanding. These “place-memory areas” offer a new framework for understanding how the brain implements memory-guided visual behaviors, including navigation.

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Publisher Correction: A network linking scene perception and spatial memory systems in posterior cerebral cortex

Nature Communications ◽

10.1038/s41467-021-23781-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Adam Steel ◽

Madeleine M. Billings ◽

Edward H. Silson ◽

Caroline E. Robertson

Keyword(s):

Cerebral Cortex ◽

Spatial Memory ◽

Scene Perception ◽

Memory Systems

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Modulating the Use of Multiple Memory Systems in Value-based Decisions with Contextual Novelty

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01447 ◽

2019 ◽

Vol 31 (10) ◽

pp. 1455-1467 ◽

Cited By ~ 3

Author(s):

Katherine Duncan ◽

Annika Semmler ◽

Daphna Shohamy

Keyword(s):

Human Brain ◽

Incremental Learning ◽

Computational Models ◽

Memory Systems ◽

Neural Systems ◽

New Approach ◽

Multiple Memory Systems ◽

The Past ◽

Memory Modulation ◽

Episodic Memories

With multiple learning and memory systems at its disposal, the human brain can represent the past in many ways, from extracting regularities across similar experiences (incremental learning) to storing rich, idiosyncratic details of individual events (episodic memory). The unique information carried by these neurologically distinct forms of memory can bias our behavior in different directions, raising crucial questions about how these memory systems interact to guide choice and the factors that cause one to dominate. Here, we devised a new approach to estimate how decisions are independently influenced by episodic memories and incremental learning. Furthermore, we identified a biologically motivated factor that biases the use of different memory types—the detection of novelty versus familiarity. Consistent with computational models of cholinergic memory modulation, we find that choices are more influenced by episodic memories following the recognition of an unrelated familiar image but more influenced by incrementally learned values after the detection of a novel image. Together this work provides a new behavioral tool enabling the disambiguation of key memory behaviors thought to be supported by distinct neural systems while also identifying a theoretically important and broadly applicable manipulation to bias the arbitration between these two sources of memories.

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Language processing is not a race against time

Behavioral and Brain Sciences ◽

10.1017/s0140525x15000692 ◽

2016 ◽

Vol 39 ◽

Cited By ~ 1

Author(s):

Giosuè Baggio ◽

Carmelo M. Vicario

Keyword(s):

Information Processing ◽

Human Brain ◽

Language Processing ◽

Cognitive Functions ◽

Memory Systems ◽

Language System ◽

Moderating Factors

AbstractWe agree with Christiansen & Chater (C&C) that language processing and acquisition are tightly constrained by the limits of sensory and memory systems. However, the human brain supports a range of cognitive functions that mitigate the effects of information processing bottlenecks. The language system is partly organised around these moderating factors, not just around restrictions on storage and computation.

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The organization of neural systems in the primate cerebral cortex

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.1993.0040 ◽

1993 ◽

Vol 252 (1333) ◽

pp. 13-18 ◽

Cited By ~ 181

Keyword(s):

Cerebral Cortex ◽

Neural Systems ◽

Primate Cerebral Cortex

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Vast Topological Learning and Sentient AGI

Journal of Artificial Intelligence and Consciousness ◽

10.1142/s2705078521500053 ◽

2021 ◽

Vol 08 (01) ◽

pp. 81-111

Author(s):

Stephen L. Thaler

Keyword(s):

Human Brain ◽

Cortical Neurons ◽

Neural Nets ◽

Neural Systems ◽

General Intelligence ◽

Associative Memories ◽

Direct Experience ◽

Environmental Input ◽

New Concepts ◽

Random Disturbances

A novel form of neurocomputing allows machines to generate new concepts along with their anticipated consequences, all encoded as chained associative memories. Knowledge is accumulated by the system through direct experience as network chaining topologies form in response to various environmental input patterns. Thereafter, random disturbances to the connections joining these nets promote the formation of alternative chaining topologies representing novel concepts. The resulting ideational chains are then reinforced or weakened as they incorporate nets containing memories of impactful events or things. Such encodings of entities, actions, and relationships as geometric forms composed of artificial neural nets may well suggest how the human brain summarizes and appraises the states of nearly a hundred billion cortical neurons. It may also be the paradigm that allows the scaling of synthetic neural systems to brain-like proportions to achieve sentient artificial general intelligence (SAGI).

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Social data collection and analyses

10.1093/oso/9780198838609.003.0003 ◽

2021 ◽

pp. 53-76

Author(s):

Marie J. E. Charpentier ◽

Marie Pelé ◽

Julien P. Renoult ◽

Cédric Sueur

Keyword(s):

Social Relationships ◽

Information Diffusion ◽

Social Systems ◽

Large Data ◽

Natural Environments ◽

Data Sets ◽

Large Array ◽

Fine Grained ◽

Network Analyses ◽

Analytical Tools

Sampling accurate and quantitative behavioural data requires the description of fine-grained patterns of social relationships and/or spatial associations, which is highly challenging, especially in natural environments. Although behavioural ecologists have tackled systematic studies on animals’ societies since the nineteenth century, new biologging technologies have the potential to revolutionise the sampling of animals’ social relationships. However, the tremendous quantity of data sampled and the diversity of biologgers (such as proximity loggers) currently available that allow the sampling of a large array of biological and physiological data bring new analytical challenges. The high spatiotemporal resolution of data needed when studying social processes, such as disease or information diffusion, requires new analytical tools, such as social network analyses, developed to analyse large data sets. The quantity and quality of the data now available on a large array of social systems bring undiscovered outputs, consistently opening new and exciting research avenues.

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Magnetic and electrical responses of the human brain to texture-defined form and to textons

Journal of Neurophysiology ◽

10.1152/jn.1995.74.3.1167 ◽

1995 ◽

Vol 74 (3) ◽

pp. 1167-1178 ◽

Cited By ~ 5

Author(s):

D. Regan ◽

P. He

Keyword(s):

Human Brain ◽

Neural Systems ◽

Magnetic Response ◽

Evoked Responses ◽

Local Orientation ◽

Brain Response ◽

Check Size ◽

Texture Discrimination ◽

Texture Pattern ◽

Brain Responses

1. We searched for a neurophysical correlate of preattentive texture discrimination by recording magnetic and electric evoked responses from the human brain during the first few hundred milliseconds following the presentation of texture-defined (TD) checkerboard form. The only two textons that changed when the TD checkerboard appeared or disappeared were the local orientation and line termination textons. (Textons are conspicuous local features within a texture pattern). 2. Our evidence that the magnetic response to TD form cannot be explained in terms of responses to the two associated textons is as follows: 1) by dissociating the two responses we showed that the magnetic response to TD form is almost entirely independent of the magnetic response to the local orientation texton; 2) a further distinction between the two responses is that their distributions over the head are different; and 3) the magnetic response to TD form differs from the magnetic response to the line termination texton in both distribution over the head and waveform. We conclude that this evidence identifies the existence of a brain response correlate of preattentive texture discrimination. 3. We also recorded brain responses to luminance-defined (LD) checkerboard form. Our grounds for concluding that magnetic brain responses to the onset of checkerboard form are generated by different and independent neural systems for TD and LD form are as follows: 1) magnetic responses to the onset of TD form and LD form had different distributions over the skull, had different waveforms, and depended differently on check size; and 2) the waveform of the response to superimposed TD and LD checks closely approximated the linear sum of responses to TD checks and LD checks alone. 4. One possible explanation for the observed differences between the magnetic and electric evoked responses is that responses to both onset and offset of TD form predominantly involve neurons aligned parallel to the skull, whereas that is not the case for responses to LD form.

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