scholarly journals Introducing double bouquet cells into a modular cortical associative memory model

2018 ◽  
Author(s):  
Nikolaos Chrysanthidis ◽  
Florian Fiebig ◽  
Anders Lansner
Keyword(s):  
Bayesian Learning ◽  
Hebbian Learning ◽  
Cell Model ◽  
Effective Connectivity ◽  
Learning Rule ◽  
Network Models ◽  
Pyramidal Cells ◽  
Learning Ability ◽  
Basic Operation ◽  
Learning Abilities

AbstractWe present an electrophysiological model of double bouquet cells and integrate them into an established cortical columnar microcircuit model that has previously been used as a spiking attractor model for memory. Learning in that model relies on a Bayesian-Hebbian learning rule to condition recurrent connectivity between pyramidal cells. We here demonstrate that the inclusion of a biophysically plausible double bouquet cell model can solve earlier concerns about learning rules that simultaneously learn excitation and inhibition and might thus violate Dale’s Principle.We show that learning ability and resulting effective connectivity between functional columns of previous network models is preserved when pyramidal synapses onto double-bouquet cells are plastic under the same Hebbian-Bayesian learning rule. The proposed architecture draws on experimental evidence on double bouquet cells and effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. We thus show that the resulting change to the microcircuit architecture improves the model’s biological plausibility without otherwise impacting the models spiking activity, basic operation, and learning abilities.

scholarly journals Introducing double bouquet cells into a modular cortical associative memory model

2019 ◽  
Vol 47 (2-3) ◽  
pp. 223-230 ◽  
Author(s):  
Nikolaos Chrysanthidis ◽  
Florian Fiebig ◽  
Anders Lansner
Keyword(s):  
Bayesian Learning ◽  
Cell Model ◽  
Effective Connectivity ◽  
Learning Rule ◽  
Network Models ◽  
Pyramidal Cells ◽  
Learning Ability ◽  
Basic Operation ◽  
Learning Abilities ◽  
Attractor Model

Abstract We present an electrophysiological model of double bouquet cells and integrate them into an established cortical columnar microcircuit model that has previously been used as a spiking attractor model for memory. Learning in that model relies on a Hebbian-Bayesian learning rule to condition recurrent connectivity between pyramidal cells. We here demonstrate that the inclusion of a biophysically plausible double bouquet cell model can solve earlier concerns about learning rules that simultaneously learn excitation and inhibition and might thus violate Dale’s principle. We show that learning ability and resulting effective connectivity between functional columns of previous network models is preserved when pyramidal synapses onto double bouquet cells are plastic under the same Hebbian-Bayesian learning rule. The proposed architecture draws on experimental evidence on double bouquet cells and effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. We thus show that the resulting change to the microcircuit architecture improves the model’s biological plausibility without otherwise impacting the model’s spiking activity, basic operation, and learning abilities.

scholarly journals Self-organization of a doubly asynchronous irregular network state for spikes and bursts

2021 ◽  
Author(s):  
Filip Vercruysse ◽  
Richard Naud ◽  
Henning Sprekeler
Keyword(s):  
Learning Rule ◽  
Network Models ◽  
Pyramidal Cells ◽  
Fine Tuning ◽  
Rate Theory ◽  
Special Role ◽  
Amount Of Information ◽  
Network State ◽  
Inhibitory Plasticity

Cortical pyramidal cells (PCs) have a specialized dendritic mechanism for the generation of bursts, suggesting that these events play a special role in cortical information processing. In vivo, bursts occur at a low, but consistent rate. Theory suggests that this network state increases the amount of information they convey. However, because burst activity relies on a threshold mechanism, it is rather sensitive to dendritic input levels. In spiking network models, network states in which bursts occur rarely are therefore typically not robust, but require fine-tuning. Here, we show that this issue can be solved by a homeostatic inhibitory plasticity rule in dendrite-targeting interneurons that is consistent with experimental data. The suggested learning rule can be combined with other forms of inhibitory plasticity to self-organize a network state in which both spikes and bursts occur asynchronously and irregularly at low rate. Finally, we show that this network state creates the network conditions for a recently suggested multiplexed code and thereby indeed increases the amount of information encoded in bursts.

scholarly journals The predictive brain: temporal coincidence and temporal order in synaptic learning mechanisms.

1994 ◽  
Vol 1 (1) ◽  
pp. 1-33
Author(s):  
P R Montague ◽  
T J Sejnowski
Keyword(s):  
Synaptic Plasticity ◽  
Temporal Order ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Network Models ◽  
Learning Mechanisms ◽  
Neural Network Models ◽  
Spatial And Temporal Scales ◽  
Synaptic Inputs ◽  
Learning Rules

Some forms of synaptic plasticity depend on the temporal coincidence of presynaptic activity and postsynaptic response. This requirement is consistent with the Hebbian, or correlational, type of learning rule used in many neural network models. Recent evidence suggests that synaptic plasticity may depend in part on the production of a membrane permeant-diffusible signal so that spatial volume may also be involved in correlational learning rules. This latter form of synaptic change has been called volume learning. In both Hebbian and volume learning rules, interaction among synaptic inputs depends on the degree of coincidence of the inputs and is otherwise insensitive to their exact temporal order. Conditioning experiments and psychophysical studies have shown, however, that most animals are highly sensitive to the temporal order of the sensory inputs. Although these experiments assay the behavior of the entire animal or perceptual system, they raise the possibility that nervous systems may be sensitive to temporally ordered events at many spatial and temporal scales. We suggest here the existence of a new class of learning rule, called a predictive Hebbian learning rule, that is sensitive to the temporal ordering of synaptic inputs. We show how this predictive learning rule could act at single synaptic connections and through diffuse neuromodulatory systems.

scholarly journals Self-organization of a doubly asynchronous irregular network state for spikes and bursts

2021 ◽  
Vol 17 (11) ◽  
pp. e1009478
Author(s):  
Filip Vercruysse ◽  
Richard Naud ◽  
Henning Sprekeler
Keyword(s):  
Learning Rule ◽  
Network Models ◽  
Pyramidal Cells ◽  
Fine Tuning ◽  
Rate Theory ◽  
Special Role ◽  
Amount Of Information ◽  
Network State ◽  
Inhibitory Plasticity

Cortical pyramidal cells (PCs) have a specialized dendritic mechanism for the generation of bursts, suggesting that these events play a special role in cortical information processing. In vivo, bursts occur at a low, but consistent rate. Theory suggests that this network state increases the amount of information they convey. However, because burst activity relies on a threshold mechanism, it is rather sensitive to dendritic input levels. In spiking network models, network states in which bursts occur rarely are therefore typically not robust, but require fine-tuning. Here, we show that this issue can be solved by a homeostatic inhibitory plasticity rule in dendrite-targeting interneurons that is consistent with experimental data. The suggested learning rule can be combined with other forms of inhibitory plasticity to self-organize a network state in which both spikes and bursts occur asynchronously and irregularly at low rate. Finally, we show that this network state creates the network conditions for a recently suggested multiplexed code and thereby indeed increases the amount of information encoded in bursts.

Using problem-based learning in a Chinese character class design: A case study of Korean learners of Chinese

2019 ◽  
Vol 3 (2) ◽  
pp. 129-138
Author(s):  
Eun Young Jang ◽  
Heung Soo Park ◽  
Yeon Sil Jeong
Keyword(s):  
Character Education ◽  
Chinese Character ◽  
Chinese Language ◽  
Problem Based Learning ◽  
Learning Ability ◽  
Self Directed Learning ◽  
Learning Abilities ◽  
Learning Attitudes ◽  
Teaching Learning ◽  
Korean Learners

This study attempted to try out Chinese-character education centering on experience and learners away from existing lecture-centered, teacher-centered education. For this purpose, problem-based learning (PBL) was proposed as one of the Chinese-language ability-enhancement measures for Korean learners of the Chinese language, and in order to examine the effect, we attempt to use the PBL tasks in the ‘Chinese-language reading’ class at a university for basic Chinese-language learners and analyze the results. PBL is a teaching-learning method in which learners focus on learning by using problems. In this study, we attempted to use PBL for the group work format. In this way, we can confirm that the class using the PBL has many advantages, such as improving learning ability and problem-solving ability, and strengthening cooperation. In addition, it was found that PBL is worthwhile to try because it is effective in inducing learning motivation, improving attention and interest in Chinese-character learning, improving learning attitudes of learners, and developing self-directed learning abilities.

scholarly journals Sparse Coding with a Somato-Dendritic Rule

2018 ◽  
Author(s):  
Damien Drix ◽  
Verena V. Hafner ◽  
Michael Schmuker
Keyword(s):  
Sparse Coding ◽  
Cortical Neurons ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Homeostatic Plasticity ◽  
Hybrid Network ◽  
Neuron Models ◽  
Functional Interpretation ◽  
Inhibitory Plasticity ◽  
Somatic Inhibition

AbstractCortical neurons are silent most of the time. This sparse activity is energy efficient, and the resulting neural code has favourable properties for associative learning. Most neural models of sparse coding use some form of homeostasis to ensure that each neuron fires infrequently. But homeostatic plasticity acting on a fast timescale may not be biologically plausible, and could lead to catastrophic forgetting in embodied agents that learn continuously. We set out to explore whether inhibitory plasticity could play that role instead, regulating both the population sparseness and the average firing rates. We put the idea to the test in a hybrid network where rate-based dendritic compartments integrate the feedforward input, while spiking somas compete through recurrent inhibition. A somato-dendritic learning rule allows somatic inhibition to modulate nonlinear Hebbian learning in the dendrites. Trained on MNIST digits and natural images, the network discovers independent components that form a sparse encoding of the input and support linear decoding. These findings con-firm that intrinsic plasticity is not strictly required for regulating sparseness: inhibitory plasticity can have the same effect, although that mechanism comes with its own stability-plasticity dilemma. Going beyond point neuron models, the network illustrates how a learning rule can make use of dendrites and compartmentalised inputs; it also suggests a functional interpretation for clustered somatic inhibition in cortical neurons.

The Ecology of Social Learning in Animals and its Link with Intelligence

2016 ◽  
Vol 19 ◽  
Author(s):  
Carel van Schaik ◽  
Sereina Graber ◽  
Caroline Schuppli ◽  
Judith Burkart
Keyword(s):  
Social Learning ◽  
Skill Acquisition ◽  
Behavioral Ecology ◽  
Cultural Intelligence ◽  
Learning Ability ◽  
Learning Abilities ◽  
The Social ◽  
In The Wild ◽  
Broad Variety

AbstractClassical ethology and behavioral ecology did not pay much attention to learning. However, studies of social learning in nature reviewed here reveal the near-ubiquity of reliance on social information for skill acquisition by developing birds and mammals. This conclusion strengthens the plausibility of the cultural intelligence hypothesis for the evolution of intelligence, which assumes that selection on social learning abilities automatically improves individual learning ability. Thus, intelligent species will generally be cultural species. Direct tests of the cultural intelligence hypothesis require good estimates of the amount and kind of social learning taking place in nature in a broad variety of species. These estimates are lacking so far. Here, we start the process of developing a functional classification of social learning, in the form of the social learning spectrum, which should help to predict the mechanisms of social learning involved. Once validated, the categories can be used to estimate the cognitive demands of social learning in the wild.

Long-term memory predictors of adult language learning at the interface between syntactic form and meaning

2021 ◽  
Author(s):  
Diana Pili-Moss
Keyword(s):  
Language Learning ◽  
Sentence Comprehension ◽  
Procedural Memory ◽  
Procedural Learning ◽  
Learning Ability ◽  
Thematic Role ◽  
Positive Interaction ◽  
Learning Abilities ◽  
Early Stages

Recent neurocognitive models of second language learning have posited specific roles for declarative and procedural memory in the processing of novel linguistic stimuli. Pursuing this line of investigation, the present study examined the role of declarative and procedural memory abilities in the early stages of adult comprehension of sentences in a miniature language with natural language characteristics (BrocantoJ). Thirty-six native Italian young adults were aurally exposed to BrocantoJ in the context of a computer game over three sessions on consecutive days. Following vocabulary training and passive exposure, participants were asked to perform game moves described by aural sentences in the language. Game trials differed with respect to the information the visual context offered. In part of the trials processing of relationships between grammatical properties of the language (word order and morphological case marking) and noun semantics (thematic role) was necessary in order reach an accurate outcome, whereas in others nongrammatical contextual cues were sufficient. Declarative and procedural learning abilities were respectively indexed by visual and verbal declarative memory measures and by a measure of visual implicit sequence learning. Overall, the results indicated a substantial role of declarative learning ability in the early stages of sentence comprehension, thus confirming theoretical predictions and the findings of previous similar studies in miniature artificial language paradigms. However, for trials that specifically probed the learning of relationships between morphosyntax and semantics, a positive interaction between declarative and procedural learning ability also emerged, indicating the cooperative engagement of both types of learning abilities in the processing of relationships between ruled-based grammar and interpretation in the early stages of exposure to a new language in adults.

scholarly journals Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning

2020 ◽  
Vol 117 (47) ◽  
pp. 29948-29958
Author(s):  
Maxwell Gillett ◽  
Ulises Pereira ◽  
Nicolas Brunel
Keyword(s):  
Storage Capacity ◽  
Synaptic Weight ◽  
Hebbian Learning ◽  
Mean Field Theory ◽  
Mean Field ◽  
Learning Rule ◽  
Network Activity ◽  
Synaptic Connectivity ◽  
Random Input ◽  
Low Dimensional

Sequential activity has been observed in multiple neuronal circuits across species, neural structures, and behaviors. It has been hypothesized that sequences could arise from learning processes. However, it is still unclear whether biologically plausible synaptic plasticity rules can organize neuronal activity to form sequences whose statistics match experimental observations. Here, we investigate temporally asymmetric Hebbian rules in sparsely connected recurrent rate networks and develop a theory of the transient sequential activity observed after learning. These rules transform a sequence of random input patterns into synaptic weight updates. After learning, recalled sequential activity is reflected in the transient correlation of network activity with each of the stored input patterns. Using mean-field theory, we derive a low-dimensional description of the network dynamics and compute the storage capacity of these networks. Multiple temporal characteristics of the recalled sequential activity are consistent with experimental observations. We find that the degree of sparseness of the recalled sequences can be controlled by nonlinearities in the learning rule. Furthermore, sequences maintain robust decoding, but display highly labile dynamics, when synaptic connectivity is continuously modified due to noise or storage of other patterns, similar to recent observations in hippocampus and parietal cortex. Finally, we demonstrate that our results also hold in recurrent networks of spiking neurons with separate excitatory and inhibitory populations.

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