Neurons including hippocampal spatial view cells, and navigation in primates including humans

Rolls, Edmund T.

doi:10.1002/hipo.23324

Cited by 33 publications

(55 citation statements)

References 144 publications

(288 reference statements)

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“…Each neuron in a Layer (or cortical area in the hierarchy) receives from neurons in a small region of the preceding Layer. It is proposed that idiothetic update through this dorsal visual cortical stream is used for idiothetic update of hippocampal spatial view cells useful for navigation when the environment may not be visible for short periods (Rolls, 2020(Rolls, , 2021b. PCC, posterior cingulate cortex; RSC, retrosplenial cortex.…”

Section: Spatial View Cellsmentioning

confidence: 99%

“…This enables the space to be read out continuously and sequentially, as a bubble of neural activity traverses the space (Rolls, 2021a). These spatial view cells to be useful for memory of where an object or person is in the spatial environment, and for navigation, and for imagery, and for the Art of Memory (Rolls, 2017), need to be invariant with respect to the exact position on the retina, eye position, head direction, and place where the viewer is located [and they are, as described above and elsewhere (Georges-François et al, 1999;Rolls, 2021b)]. The mechanism just described with the primate fovea which provides a locally Gaussian spatial view of the world enables the appropriate spatial scene representations to be formed, which do not depend on where the viewer is etc.…”

Section: Spatial View Cellsmentioning

confidence: 99%

“…Scenes consisting of a set of 4 objects presented simultaneously in 4 quadrants of a scene resulted in neurons in the fifth Layer learning representations that required the components of the scene to be in the correct fixed spatial relationship to each other ( Rolls et al, 2008 ). This is one way in which it is proposed that spatial view cells, present in the hippocampus and parahippocampal gyrus ( Rolls et al, 1989b , 1997a , 1998 ; Feigenbaum and Rolls, 1991 ; Rolls and O’Mara, 1995 ; Robertson et al, 1998 ; Georges-François et al, 1999 ; Rolls and Xiang, 2006 ; Rolls and Wirth, 2018 ; Rolls, 2021b ) which receive from high order visual cortical areas ( Epstein and Baker, 2019 ; Huang et al, 2021 ), learn to respond to scenes and indeed to particular locations in a scene ( Rolls, 2021a , b ).…”

Section: Transform-invariant Representations Of Objects and Facesmentioning

confidence: 99%

“…This forms a continuous attractor network that is effectively a representation of a scene and is formed by the overlaps of the spatial fields of neurons ( Stringer et al, 2005 ; Rolls et al, 2008 ). This continuous attractor representation of a spatial scene facilitates navigation by enabling a trajectory through the continuous attractor of spatial view cells ( Rolls, 2021b ). Analogous mechanisms are proposed for place cell learning ( Samsonovich and McNaughton, 1997 ; Stringer et al, 2002 ).…”

Section: Transform-invariant Representations Of Objects and Facesmentioning

confidence: 99%

“…This paper also extends this slow learning approach to coordinate transforms in the dorsal visual system and parietal cortex that result in allocentric (world based) coordinates ( Rolls, 2020 ), and that allow hippocampal spatial view cells to be updated by self-motion for navigation when the view is temporarily obscured ( Rolls, 2021b ). This extension helps to show how the slow learning approach used in VisNet that uses statistics present from the environmental inputs may be useful in a number of different brain systems.…”

Section: Introductionmentioning

confidence: 97%

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Learning Invariant Object and Spatial View Representations in the Brain Using Slow Unsupervised Learning

Rolls

2021

Front. Comput. Neurosci.

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First, neurophysiological evidence for the learning of invariant representations in the inferior temporal visual cortex is described. This includes object and face representations with invariance for position, size, lighting, view and morphological transforms in the temporal lobe visual cortex; global object motion in the cortex in the superior temporal sulcus; and spatial view representations in the hippocampus that are invariant with respect to eye position, head direction, and place. Second, computational mechanisms that enable the brain to learn these invariant representations are proposed. For the ventral visual system, one key adaptation is the use of information available in the statistics of the environment in slow unsupervised learning to learn transform-invariant representations of objects. This contrasts with deep supervised learning in artificial neural networks, which uses training with thousands of exemplars forced into different categories by neuronal teachers. Similar slow learning principles apply to the learning of global object motion in the dorsal visual system leading to the cortex in the superior temporal sulcus. The learning rule that has been explored in VisNet is an associative rule with a short-term memory trace. The feed-forward architecture has four stages, with convergence from stage to stage. This type of slow learning is implemented in the brain in hierarchically organized competitive neuronal networks with convergence from stage to stage, with only 4-5 stages in the hierarchy. Slow learning is also shown to help the learning of coordinate transforms using gain modulation in the dorsal visual system extending into the parietal cortex and retrosplenial cortex. Representations are learned that are in allocentric spatial view coordinates of locations in the world and that are independent of eye position, head direction, and the place where the individual is located. This enables hippocampal spatial view cells to use idiothetic, self-motion, signals for navigation when the view details are obscured for short periods.

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Section: Spatial View Cellsmentioning

confidence: 99%

Section: Spatial View Cellsmentioning

confidence: 99%

Section: Transform-invariant Representations Of Objects and Facesmentioning

confidence: 99%

Section: Transform-invariant Representations Of Objects and Facesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Learning Invariant Object and Spatial View Representations in the Brain Using Slow Unsupervised Learning

Rolls

2021

Front. Comput. Neurosci.

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The human posterior cingulate, retrosplenial, and medial parietal cortex effective connectome, and implications for memory and navigation

Rolls

Wirth

Deco

et al. 2022

Human Brain Mapping

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The human posterior cingulate, retrosplenial, and medial parietal cortex are involved in memory and navigation. The functional anatomy underlying these cognitive functions was investigated by measuring the effective connectivity of these Posterior Cingulate Division (PCD) regions in the Human Connectome Project-MMP1 atlas in 171 HCP participants, and complemented with functional connectivity and diffusion tractography. First, the postero-ventral parts of the PCD (31pd, 31pv, 7m, d23ab, and v23ab) have effective connectivity with the temporal pole, inferior temporal visual cortex, cortex in the superior temporal sulcus implicated in auditory and semantic processing, with the reward-related vmPFC and pregenual anterior cingulate cortex, with the inferior parietal cortex, and with the hippocampal system. This connectivity implicates it in hippocampal episodic memory, providing routes for "what," reward and semantic schema-related information to access the hippocampus. Second, the antero-dorsal parts of the PCD (especially 31a and 23d, PCV, and also RSC) have connectivity with early visual cortical areas including those that represent spatial scenes, with the superior parietal cortex, with the pregenual anterior cingulate cortex, and with the hippocampal system. This connectivity implicates it in the "where" component for hippocampal episodic memory and for spatial navigation. The dorsal-

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Selective Brain Activations and Connectivities Related to the Storage and Recall of Human Object‐Location, Reward‐Location, and Word‐Pair Episodic Memories

Rolls,

Zhang,

Deco

et al. 2024

Human Brain Mapping

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Different cortical systems to the hippocampus were activated using fMRI during different types of episodic memory task. For object with scene location episodic memory, the activations were high in cortical systems involved in spatial processing, including the ventromedial visual and medial parahippocampal system. These activations for the medial parahippocampal system were higher in the right hemisphere. The activations in the face and object processing ventrolateral visual cortical stream regions FFC, PIT, V8 and TE2p were higher in the object‐location in scene task than the reward‐location task, and were higher in the right hemisphere. For reward‐location in scene episodic memory, activations were also high in the ventromedial visual cortical spatial stream to the hippocampus, but were also selectively high in storage in key reward cortical regions (ventromedial prefrontal 10r, 10v, 10d; pregenual anterior cingulate d32, p24, p32, s32; and medial orbitofrontal cortex reward‐related pOFC, 11l, OFC). For word‐pair episodic memory, activations were lower in the ventromedial visual and medial parahippocampal spatial cortical stream, and were higher in language‐related regions in Broca's area (44, 45, 47l), and were higher in the left hemisphere for these regions and for the many highly connected inferior frontal gyrus regions in the left hemisphere. Further, effective connectivity analyses during the episodic memory tasks showed that the direction of connectivity for these systems was from early visual cortical regions V2–V4 to the ventromedial visual cortical regions VMV1–3 and VVC for spatial scene processing; was from the pregenual anterior cingulate and orbitofrontal cortex reward systems to the hippocampal system; and was from the FFC/V8/PIT system to TE2p in the visual inferior temporal visual cortex, which has connectivity to lateral parahippocampal TF, which in turn has forward effective connectivity to the hippocampus.

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Neurons including hippocampal spatial view cells, and navigation in primates including humans

Cited by 33 publications

References 144 publications

Learning Invariant Object and Spatial View Representations in the Brain Using Slow Unsupervised Learning

Learning Invariant Object and Spatial View Representations in the Brain Using Slow Unsupervised Learning

The human posterior cingulate, retrosplenial, and medial parietal cortex effective connectome, and implications for memory and navigation

Selective Brain Activations and Connectivities Related to the Storage and Recall of Human Object‐Location, Reward‐Location, and Word‐Pair Episodic Memories

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