Self-organization of multiple spatial and context memories in the hippocampus

Stella, Federico; Cerasti, Erika; Si, Bailu; Ježek, Karel; Treves, Alessandro

doi:10.1016/j.neubiorev.2011.12.002

Cited by 47 publications

(31 citation statements)

References 172 publications

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“…As we have discussed elsewhere (Stella et al, 2012), the specific contribution of the CA3 network can be argued to be in the formation of memories for specific spatio-temporal contexts, more than in retaining spatial information about a single context. So far we have quantified the amount of spatial information in one chart, either the only one or one among several stored concurrently, but one can also ask how much information the network can produce about which chart best matches the current environment, irrespective of exact position within it.…”

Section: Resultsmentioning

confidence: 86%

The spatial representations acquired in CA3 by self-organizing recurrent connections

Cerasti¹,

Treves²

2013

Front. Cell. Neurosci.

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Neural computation models have hypothesized that the dentate gyrus (DG) drives the storage in the CA3 network of new memories including, e.g., in rodents, spatial memories. Can recurrent CA3 connections self-organize, during storage, and form what have been called continuous attractors, or charts—so that they express spatial information later, when aside from a partial cue the information may not be available in the inputs? We use a simplified mathematical network model to contrast the properties of spatial representations self-organized through simulated Hebbian plasticity with those of charts pre-wired in the synaptic matrix, a control case closer to the ideal notion of continuous attractors. Both models form granular quasi-attractors, characterized by drift, which approach continuous ones only in the limit of an infinitely large network. The two models are comparable in terms of precision, but not of accuracy: with self-organized connections, the metric of space remains distorted, ill-adequate for accurate path integration, even when scaled up to the real hippocampus. While prolonged self-organization makes charts somewhat more informative about position in the environment, some positional information is surprisingly present also about environments never learned, borrowed, as it were, from unrelated charts. In contrast, context discrimination decreases with more learning, as different charts tend to collapse onto each other. These observations challenge the feasibility of the idealized CA3 continuous chart concept, and are consistent with a CA3 specialization for episodic memory rather than path integration.

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Section: Resultsmentioning

confidence: 86%

The spatial representations acquired in CA3 by self-organizing recurrent connections

Cerasti¹,

Treves²

2013

Front. Cell. Neurosci.

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“…For example, since the Rat's environment is made up of tunnels, corridors and mazes, its hippocampus would have evolved for this type of environment while that of the Pigeon would be more suited for free range flying environments. This is hypothesis is supported by [9,11] that discuss how the hippocampus is functionally similar between the avian and mammalian hippocampus but different in what information is stored as well as how it is stored and encoded.…”

Section: Support Of the Hippocampus In Map Buildingmentioning

confidence: 91%

“…There is evidence to suggest that the more exploration of an environment an organism does, the greater the volume of its hippocampus [7,11,12]. In [7] for example, black cab taxi drivers were observed to have larger hippocampus than the average human.…”

Section: Support Of the Hippocampus In Map Buildingmentioning

confidence: 99%

“…These two SLAM techniques are based on the mammalian hippocampus. Even though Stringer et al [17] also used it in studying various models of the hippocampus, Stella et al argued that attractor dynamics is not only unique to the hippocampus but can also be found in other areas of the brain [11]. In this work, a self-organising network is proposed to be used as a tool to simulate the hippocampus.…”

Section: Developing a Computational Model Of The Pigeon Hippocampus Fmentioning

confidence: 99%

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A vision-based terrain morphology estimation model inspired by the avian hippocampus

Oyekan

2015

Digital Communications and Networks

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Homing pigeons are known for their ability to return home after being released from a location spanning up to hundreds of miles. They make use of detected visual features in the environment, the earth's magnetic field as well as using the hippocampus region of the brain to construct spatial maps of the environment. This is unlike present day UAVs that rely on GPS and radio/satellite communications with a ground station, both of which might not be available during a major disaster scenario such as a solar flare. In this paper, we take inspiration from the avian hippocampus and develop a preliminary model for estimating a terrain's morphology using visually detected features on the terrain. This could then be used to localise a portable micro-UAV during a demining task for humanitarian purposes in third world countries affected by buried land mines from previous wars. Our goal is that in future, the presented model and algorithm in this work would enable effective coverage of an affected area using the visual information obtained from the environment.

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“…Inputs from the other senses (olfactory, gustatory, touch/pain) are also communicated to the hippocampus. Learning and long-term memories of such experiences require the hippocampus in humans, monkeys, rodents, and birds (Stella et al, 2012). For example, birds that store food in caches are able to remember, in a hippocampus-dependent manner, not only the locations of the caches, but also what they placed in the cache and when they put it there.…”

Section: Neural Substrates Of Superior Pattern Processing: a Conservementioning

confidence: 99%

Superior pattern processing is the essence of the evolved human brain

Mattson

2014

Front. Neurosci.

110

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Humans have long pondered the nature of their mind/brain and, particularly why its capacities for reasoning, communication and abstract thought are far superior to other species, including closely related anthropoids. This article considers superior pattern processing (SPP) as the fundamental basis of most, if not all, unique features of the human brain including intelligence, language, imagination, invention, and the belief in imaginary entities such as ghosts and gods. SPP involves the electrochemical, neuronal network-based, encoding, integration, and transfer to other individuals of perceived or mentally-fabricated patterns. During human evolution, pattern processing capabilities became increasingly sophisticated as the result of expansion of the cerebral cortex, particularly the prefrontal cortex and regions involved in processing of images. Specific patterns, real or imagined, are reinforced by emotional experiences, indoctrination and even psychedelic drugs. Impaired or dysregulated SPP is fundamental to cognitive and psychiatric disorders. A broader understanding of SPP mechanisms, and their roles in normal and abnormal function of the human brain, may enable the development of interventions that reduce irrational decisions and destructive behaviors.

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Self-organization of multiple spatial and context memories in the hippocampus

Cited by 47 publications

References 172 publications

The spatial representations acquired in CA3 by self-organizing recurrent connections

The spatial representations acquired in CA3 by self-organizing recurrent connections

A vision-based terrain morphology estimation model inspired by the avian hippocampus

Superior pattern processing is the essence of the evolved human brain

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