Limits...
A neural mechanism for background information-gated learning based on axonal-dendritic overlaps.

Mainetti M, Ascoli GA - PLoS Comput. Biol. (2015)

Bottom Line: The simplest instantiation encodes each concept by single neurons.Results are then generalized to cell assemblies.The proposed mechanism results in learning real associations better than spurious co-occurrences, providing definitive cognitive advantages.

View Article: PubMed Central - PubMed

Affiliation: Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, United States of America.

ABSTRACT
Experiencing certain events triggers the acquisition of new memories. Although necessary, however, actual experience is not sufficient for memory formation. One-trial learning is also gated by knowledge of appropriate background information to make sense of the experienced occurrence. Strong neurobiological evidence suggests that long-term memory storage involves formation of new synapses. On the short time scale, this form of structural plasticity requires that the axon of the pre-synaptic neuron be physically proximal to the dendrite of the post-synaptic neuron. We surmise that such "axonal-dendritic overlap" (ADO) constitutes the neural correlate of background information-gated (BIG) learning. The hypothesis is based on a fundamental neuroanatomical constraint: an axon must pass close to the dendrites that are near other neurons it contacts. The topographic organization of the mammalian cortex ensures that nearby neurons encode related information. Using neural network simulations, we demonstrate that ADO is a suitable mechanism for BIG learning. We model knowledge as associations between terms, concepts or indivisible units of thought via directed graphs. The simplest instantiation encodes each concept by single neurons. Results are then generalized to cell assemblies. The proposed mechanism results in learning real associations better than spurious co-occurrences, providing definitive cognitive advantages.

Show MeSH
Instantiation of background information-gated (BIG) learning by the neuroanatomical mechanism of axonal-dendrite overlap (ADO).A. Cognitive model: Previously acquired background information, reflected in the structure of the association network, provides a gating mechanism for the formation of novel associations. The ability to acquire the new piece of information (associating the buzz to the beetle) depends on prior knowledge of relevant facts: in this example, that other buzzing animals (e.g. wasps) fly erratically. The green fonts a, b, c, and d refer to the proximity formula (also in green), fully described in the Materials and Methods. B. Neural correlate: In this simplified (“grandmother” cells) model, each concept of panel A is represented by a single neuron, with axonal and dendritic trees drawn respectively in red and blue. The axon of the “Buzzing” neuron has a synaptic contact with the dendrite of the “Wasp” neuron. Thus, it must pass close to the dendrites of other nearby neurons. Neurons are likely to be near each other if they receive synapses from the same axons. Here, “Beetle” is near “Wasp” as they both receive synapses from the axon of the “Erratic Flight” neuron. Thus, prior knowledge of relevant background information, instantiated by the three existing synapses, provides proper conditions to learn the new association, i.e. forming the “Buzzing”-“Beetle” synapse.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4359104&req=5

pcbi.1004155.g001: Instantiation of background information-gated (BIG) learning by the neuroanatomical mechanism of axonal-dendrite overlap (ADO).A. Cognitive model: Previously acquired background information, reflected in the structure of the association network, provides a gating mechanism for the formation of novel associations. The ability to acquire the new piece of information (associating the buzz to the beetle) depends on prior knowledge of relevant facts: in this example, that other buzzing animals (e.g. wasps) fly erratically. The green fonts a, b, c, and d refer to the proximity formula (also in green), fully described in the Materials and Methods. B. Neural correlate: In this simplified (“grandmother” cells) model, each concept of panel A is represented by a single neuron, with axonal and dendritic trees drawn respectively in red and blue. The axon of the “Buzzing” neuron has a synaptic contact with the dendrite of the “Wasp” neuron. Thus, it must pass close to the dendrites of other nearby neurons. Neurons are likely to be near each other if they receive synapses from the same axons. Here, “Beetle” is near “Wasp” as they both receive synapses from the axon of the “Erratic Flight” neuron. Thus, prior knowledge of relevant background information, instantiated by the three existing synapses, provides proper conditions to learn the new association, i.e. forming the “Buzzing”-“Beetle” synapse.

Mentions: Reading about a newly discovered insect species, an entomologist can rapidly learn various details of their development, communication, and mating. Studying the same material, it is much harder for someone with different expertise to learn the same facts. While it is commonsense that new information is easier to memorize if it relates to prior knowledge, the cognitive and neural mechanisms underlying this familiar phenomenon are not established. More specifically, one-trial learning of “neutral” events, as opposed to emotionally charged or surprising experiences [1], is gated by knowledge of appropriate background information to make sense of the experienced occurrence [2, 3]. Consider experiencing for the first time the co-occurrence of a buzzing sound with the sight of a beetle (Fig. 1A). Learning that “beetles can buzz” may depend on background information that renders the “buzzing beetle” association sensible. Prior knowledge might include that wasps, flies, and bees also buzz. Such facts are relevant because they involve related concepts: these insects share several common associations with beetles (e.g. small size, crawling, flying, erratic trajectories). The remainder of this paper refers to this cognitive phenomenon as “background information gating” or BIG learning.


A neural mechanism for background information-gated learning based on axonal-dendritic overlaps.

Mainetti M, Ascoli GA - PLoS Comput. Biol. (2015)

Instantiation of background information-gated (BIG) learning by the neuroanatomical mechanism of axonal-dendrite overlap (ADO).A. Cognitive model: Previously acquired background information, reflected in the structure of the association network, provides a gating mechanism for the formation of novel associations. The ability to acquire the new piece of information (associating the buzz to the beetle) depends on prior knowledge of relevant facts: in this example, that other buzzing animals (e.g. wasps) fly erratically. The green fonts a, b, c, and d refer to the proximity formula (also in green), fully described in the Materials and Methods. B. Neural correlate: In this simplified (“grandmother” cells) model, each concept of panel A is represented by a single neuron, with axonal and dendritic trees drawn respectively in red and blue. The axon of the “Buzzing” neuron has a synaptic contact with the dendrite of the “Wasp” neuron. Thus, it must pass close to the dendrites of other nearby neurons. Neurons are likely to be near each other if they receive synapses from the same axons. Here, “Beetle” is near “Wasp” as they both receive synapses from the axon of the “Erratic Flight” neuron. Thus, prior knowledge of relevant background information, instantiated by the three existing synapses, provides proper conditions to learn the new association, i.e. forming the “Buzzing”-“Beetle” synapse.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4359104&req=5

pcbi.1004155.g001: Instantiation of background information-gated (BIG) learning by the neuroanatomical mechanism of axonal-dendrite overlap (ADO).A. Cognitive model: Previously acquired background information, reflected in the structure of the association network, provides a gating mechanism for the formation of novel associations. The ability to acquire the new piece of information (associating the buzz to the beetle) depends on prior knowledge of relevant facts: in this example, that other buzzing animals (e.g. wasps) fly erratically. The green fonts a, b, c, and d refer to the proximity formula (also in green), fully described in the Materials and Methods. B. Neural correlate: In this simplified (“grandmother” cells) model, each concept of panel A is represented by a single neuron, with axonal and dendritic trees drawn respectively in red and blue. The axon of the “Buzzing” neuron has a synaptic contact with the dendrite of the “Wasp” neuron. Thus, it must pass close to the dendrites of other nearby neurons. Neurons are likely to be near each other if they receive synapses from the same axons. Here, “Beetle” is near “Wasp” as they both receive synapses from the axon of the “Erratic Flight” neuron. Thus, prior knowledge of relevant background information, instantiated by the three existing synapses, provides proper conditions to learn the new association, i.e. forming the “Buzzing”-“Beetle” synapse.
Mentions: Reading about a newly discovered insect species, an entomologist can rapidly learn various details of their development, communication, and mating. Studying the same material, it is much harder for someone with different expertise to learn the same facts. While it is commonsense that new information is easier to memorize if it relates to prior knowledge, the cognitive and neural mechanisms underlying this familiar phenomenon are not established. More specifically, one-trial learning of “neutral” events, as opposed to emotionally charged or surprising experiences [1], is gated by knowledge of appropriate background information to make sense of the experienced occurrence [2, 3]. Consider experiencing for the first time the co-occurrence of a buzzing sound with the sight of a beetle (Fig. 1A). Learning that “beetles can buzz” may depend on background information that renders the “buzzing beetle” association sensible. Prior knowledge might include that wasps, flies, and bees also buzz. Such facts are relevant because they involve related concepts: these insects share several common associations with beetles (e.g. small size, crawling, flying, erratic trajectories). The remainder of this paper refers to this cognitive phenomenon as “background information gating” or BIG learning.

Bottom Line: The simplest instantiation encodes each concept by single neurons.Results are then generalized to cell assemblies.The proposed mechanism results in learning real associations better than spurious co-occurrences, providing definitive cognitive advantages.

View Article: PubMed Central - PubMed

Affiliation: Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, United States of America.

ABSTRACT
Experiencing certain events triggers the acquisition of new memories. Although necessary, however, actual experience is not sufficient for memory formation. One-trial learning is also gated by knowledge of appropriate background information to make sense of the experienced occurrence. Strong neurobiological evidence suggests that long-term memory storage involves formation of new synapses. On the short time scale, this form of structural plasticity requires that the axon of the pre-synaptic neuron be physically proximal to the dendrite of the post-synaptic neuron. We surmise that such "axonal-dendritic overlap" (ADO) constitutes the neural correlate of background information-gated (BIG) learning. The hypothesis is based on a fundamental neuroanatomical constraint: an axon must pass close to the dendrites that are near other neurons it contacts. The topographic organization of the mammalian cortex ensures that nearby neurons encode related information. Using neural network simulations, we demonstrate that ADO is a suitable mechanism for BIG learning. We model knowledge as associations between terms, concepts or indivisible units of thought via directed graphs. The simplest instantiation encodes each concept by single neurons. Results are then generalized to cell assemblies. The proposed mechanism results in learning real associations better than spurious co-occurrences, providing definitive cognitive advantages.

Show MeSH