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 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 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 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 Flexible categorization in perceptual decision making

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Network Models ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Sensory Evidence ◽  
Winner Take All

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

scholarly journals Flexible categorization in perceptual decision making

2020 ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Large Body ◽  
Network Models ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Winner Take All ◽  
The Brain

AbstractPerceptual decisions require the brain to make categorical choices based on accumulated sensory evidence. The underlying computations have been studied using either phenomenological drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both classes of models can account for a large body of experimental data, it remains unclear to what extent their dynamics are qualitatively equivalent. Here we show that, unlike the drift diffusion model, the attractor model can operate in different integration regimes: an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision-states leading to a crossover between weighting mostly early evidence (primacy regime) to weighting late evidence (recency regime). Between these two limiting cases, we found a novel regime, which we name flexible categorization, in which fluctuations are strong enough to reverse initial categorizations, but only if they are incorrect. This asymmetry in the reversing probability results in a non-monotonic psychometric curve, a novel and distinctive feature of the attractor model. Finally, we show psychophysical evidence for the crossover between integration regimes predicted by the attractor model and for the relevance of this new regime. Our findings point to correcting transitions as an important yet overlooked feature of perceptual decision making.

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 Feedback-dependent generalization

2013 ◽  
Vol 109 (1) ◽  
pp. 202-215 ◽  
Author(s):  
Jordan A. Taylor ◽  
Laura L. Hieber ◽  
Richard B. Ivry
Keyword(s):  
Neural Network ◽  
Learning Rule ◽  
Network Models ◽  
Visuomotor Adaptation ◽  
Neural Representation ◽  
Neural Network Models ◽  
Feedback Processing ◽  
Feedback Information ◽  
Specific Effects ◽  
Sensorimotor Map

Generalization provides a window into the representational changes that occur during motor learning. Neural network models have been integral in revealing how the neural representation constrains the extent of generalization. Specifically, two key features are thought to define the pattern of generalization. First, generalization is constrained by the properties of the underlying neural units; with directionally tuned units, the extent of generalization is limited by the width of the tuning functions. Second, error signals are used to update a sensorimotor map to align the desired and actual output, with a gradient-descent learning rule ensuring that the error produces changes in those units responsible for the error. In prior studies, task-specific effects in generalization have been attributed to differences in neural tuning functions. Here we ask whether differences in generalization functions may arise from task-specific error signals. We systematically varied visual error information in a visuomotor adaptation task and found that this manipulation led to qualitative differences in generalization. A neural network model suggests that these differences are the result of error feedback processing operating on a homogeneous and invariant set of tuning functions. Consistent with novel predictions derived from the model, increasing the number of training directions led to specific distortions of the generalization function. Taken together, the behavioral and modeling results offer a parsimonious account of generalization that is based on the utilization of feedback information to update a sensorimotor map with stable tuning functions.

scholarly journals Novel Intelligent and Sensorless Proportional Valve Control with Self-Learning Ability

2016 ◽  
Vol 2016 ◽  
pp. 1-6
Author(s):  
Bayram Akdemir
Keyword(s):  
Pulse Width ◽  
Pulse Width Modulation ◽  
Control Method ◽  
Low Voltage ◽  
Control Unit ◽  
Control Signal ◽  
Learning Ability ◽  
Learning Abilities ◽  
Valve Control ◽  
Self Learning

Linear control is widely used for any fluid or air flows in many automobile, robotics, and hydraulics applications. According to signal level, valve can be controlled linearly. But, for many valves, hydraulics or air is not easy to control proportionally because of flows dynamics. As a conventional solution, electronic driver has up and down limits. After manually settling up and down limits, control unit has proportional blind behavior between two points. This study offers a novel valve control method merging pulse width and amplitude modulation in the same structure. Proposed method uses low voltage AC signal to understand the valve position and uses pulse width modulation for power transfer to coil. DC level leads to controlling the valve and AC signal gives feedback related to core moving. Any amplitude demodulator gives core position as voltage. Control unit makes reconstruction using start and end points to obtain linearization at zero control signal and maximum control signal matched to minimum demodulated amplitude level. Proposed method includes self-learning abilities to keep controlling in hard environmental conditions such as dust, temperature, and corrosion. Thus, self-learning helps to provide precision control for hard conditions.

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