scholarly journals A Neural Network Model Can Explain Ventriloquism Aftereffect and Its Generalization across Sound Frequencies

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Elisa Magosso ◽  
Filippo Cona ◽  
Mauro Ursino
Keyword(s):  
Neural Network ◽  
Visual Stimulus ◽  
Hebbian Learning ◽  
Experimental Studies ◽  
Auditory Stimuli ◽  
Spectral Features ◽  
Auditory Neurons ◽  
Learning Rules ◽  
Perceptual Shift ◽  
Ventriloquism Aftereffect

Exposure to synchronous but spatially disparate auditory and visual stimuli produces a perceptual shift of sound location towards the visual stimulus (ventriloquism effect). After adaptation to a ventriloquism situation, enduring sound shift is observed in the absence of the visual stimulus (ventriloquism aftereffect). Experimental studies report opposing results as to aftereffect generalization across sound frequencies varying from aftereffect being confined to the frequency used during adaptation to aftereffect generalizing across some octaves. Here, we present an extension of a model of visual-auditory interaction we previously developed. The new model is able to simulate the ventriloquism effect and, via Hebbian learning rules, the ventriloquism aftereffect and can be used to investigate aftereffect generalization across frequencies. The model includes auditory neurons coding both for the spatial and spectral features of the auditory stimuli and mimicking properties of biological auditory neurons. The model suggests that different extent of aftereffect generalization across frequencies can be obtained by changing the intensity of the auditory stimulus that induces different amounts of activation in the auditory layer. The model provides a coherent theoretical framework to explain the apparently contradictory results found in the literature. Model mechanisms and hypotheses are discussed in relation to neurophysiological and psychophysical data.

scholarly journals Attractor dynamics in networks with learning rules inferred from in vivo data

2017 ◽  
Author(s):  
Ulises Pereira ◽  
Nicolas Brunel
Keyword(s):  
Neural Network ◽  
Hebbian Learning ◽  
Temporal Cortex ◽  
Memory Storage ◽  
Inferior Temporal Cortex ◽  
Association Cortex ◽  
Visual Responses ◽  
Neural Network Models ◽  
Firing Rates ◽  
Learning Rules

AbstractThe attractor neural network scenario is a popular scenario for memory storage in association cortex, but there is still a large gap between models based on this scenario and experimental data. We study a recurrent network model in which both learning rules and distribution of stored patterns are inferred from distributions of visual responses for novel and familiar images in inferior temporal cortex (ITC). Unlike classical attractor neural network models, our model exhibits graded activity in retrieval states, with distributions of firing rates that are close to lognormal. Inferred learning rules are close to maximizing the number of stored patterns within a family of unsupervised Hebbian learning rules, suggesting learning rules in ITC are optimized to store a large number of attractor states. Finally, we show that there exists two types of retrieval states: one in which firing rates are constant in time, another in which firing rates fluctuate chaotically.

scholarly journals Evolving Hebbian Learning Rules in Voxel-based Soft Robots

2021 ◽  
Author(s):  
Andrea Ferigo ◽  
Giovanni Iacca ◽  
Eric Medvet ◽  
Federico Pigozzi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Hebbian Learning ◽  
Artificial Agents ◽  
Soft Robots ◽  
Multiple Timescales ◽  
Learning Rules ◽  
Artificial Neural Network Ann ◽  
Better Than

<div>According to Hebbian theory, synaptic plasticity is the ability of neurons to strengthen or weaken the synapses among them in response to stimuli. It plays a fundamental role in the processes of learning and memory of biological neural networks. With plasticity, biological agents can adapt on multiple timescales and outclass artificial agents, the majority of which still rely on static Artificial Neural Network (ANN) controllers. In this work, we focus on Voxel-based Soft Robots (VSRs), a class of simulated artificial agents, composed as aggregations of elastic cubic blocks. We propose a Hebbian ANN controller where every synapse is associated with a Hebbian rule that controls the way the weight is adapted during the VSR lifetime. For a given task and morphology, we optimize the controller for the task of locomotion by evolving, rather than the weights, the parameters of the Hebbian rules. Our results show that the Hebbian controller is comparable, often better than a non-Hebbian baseline and that it is more adaptable to unforeseen damages. We also provide novel insights into the inner workings of plasticity and demonstrate that "true" learning does take place, as the evolved controllers improve over the lifetime and generalize well.</div>

Competitive STDP Learning of Overlapping Spatial Patterns

2015 ◽  
Vol 27 (8) ◽  
pp. 1673-1685 ◽  
Author(s):  
Dalius Krunglevicius
Keyword(s):  
Neural Network ◽  
Euclidean Distance ◽  
Hebbian Learning ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Size Dependent ◽  
Learning Rules ◽  
Pattern Size ◽  
Dependent Parameter ◽  
Simple Neural Network

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple distinct patterns. If patterns overlap significantly (i.e., patterns are mutually inclusive), however, competition would not preclude trained neuron’s responding to a new pattern and adjusting synaptic weights accordingly. This letter presents a simple neural network that combines vertical inhibition and Euclidean distance-dependent synaptic strength factor. This approach helps to solve the problem of pattern size-dependent parameter optimality and significantly reduces the probability of a neuron’s forgetting an already learned pattern. For demonstration purposes, the network was trained for the first ten letters of the Braille alphabet.

scholarly journals Evolving Hebbian Learning Rules in Voxel-based Soft Robots

2021 ◽  
Author(s):  
Andrea Ferigo ◽  
Giovanni Iacca ◽  
Eric Medvet ◽  
Federico Pigozzi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Hebbian Learning ◽  
Artificial Agents ◽  
Soft Robots ◽  
Multiple Timescales ◽  
Learning Rules ◽  
Artificial Neural Network Ann ◽  
Better Than

<div>According to Hebbian theory, synaptic plasticity is the ability of neurons to strengthen or weaken the synapses among them in response to stimuli. It plays a fundamental role in the processes of learning and memory of biological neural networks. With plasticity, biological agents can adapt on multiple timescales and outclass artificial agents, the majority of which still rely on static Artificial Neural Network (ANN) controllers. In this work, we focus on Voxel-based Soft Robots (VSRs), a class of simulated artificial agents, composed as aggregations of elastic cubic blocks. We propose a Hebbian ANN controller where every synapse is associated with a Hebbian rule that controls the way the weight is adapted during the VSR lifetime. For a given task and morphology, we optimize the controller for the task of locomotion by evolving, rather than the weights, the parameters of the Hebbian rules. Our results show that the Hebbian controller is comparable, often better than a non-Hebbian baseline and that it is more adaptable to unforeseen damages. We also provide novel insights into the inner workings of plasticity and demonstrate that "true" learning does take place, as the evolved controllers improve over the lifetime and generalize well.</div>

scholarly journals Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3389
Author(s):  
Marcin Kamiński ◽  
Krzysztof Szabat
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Dynamic Properties ◽  
Experimental Studies ◽  
Drive System ◽  
Particle Swarm Optimizer ◽  
The Neural Network ◽  
Control Concept ◽  
Constant Coefficients ◽  
Selection Of

This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

scholarly journals Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 742
Author(s):  
Canh Nguyen ◽  
Vasit Sagan ◽  
Matthew Maimaitiyiming ◽  
Maitiniyazi Maimaitijiang ◽  
Sourav Bhadra ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Viral Diseases ◽  
Support Vector ◽  
Spectral Features ◽  
Feature Spaces

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

Introducing Asymmetry into Interneuron Learning

1995 ◽  
Vol 7 (6) ◽  
pp. 1191-1205 ◽  
Author(s):  
Colin Fyfe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Negative Feedback ◽  
Principal Components ◽  
Network Architecture ◽  
Hebbian Learning ◽  
Principal Component ◽  
Neural Network Architecture ◽  
Weight Decay ◽  
Artificial Neural Network Architecture

A review is given of a new artificial neural network architecture in which the weights converge to the principal component subspace. The weights learn by only simple Hebbian learning yet require no clipping, normalization or weight decay. The net self-organizes using negative feedback of activation from a set of "interneurons" to the input neurons. By allowing this negative feedback from the interneurons to act on other interneurons we can introduce the necessary asymmetry to cause convergence to the actual principal components. Simulations and analysis confirm such convergence.

Reliability of an Attention-Assessing Technique

1984 ◽  
Vol 59 (1) ◽  
pp. 212-214
Author(s):  
H. W. Craver
Keyword(s):  
Auditory Stimulus ◽  
Visual Stimulus ◽  
Experimental Situation ◽  
Auditory Stimuli ◽  
Self Monitoring ◽  
Monitoring Techniques ◽  
Self Reports

The reliability of an attention-focusing technique was assessed for 12 subjects over 4 sessions. Subjects' thought intrusions were counted while they were focusing on either visual or auditory stimuli. Digital temperatures were recorded and an experimental-situation questionnaire was administered. This technique provides extremely reliable self-reports across the sessions. The total number of intrusions was higher for the auditory stimulus than for the visual stimulus. The study's relevance to assessing self-monitoring techniques such as meditation is discussed.

scholarly journals Logarithmic distributions prove that intrinsic learning is Hebbian

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1222 ◽  
Author(s):  
Gabriele Scheler
Keyword(s):  
Adaptive Learning ◽  
Hebbian Learning ◽  
Neural Model ◽  
Intrinsic Excitability ◽  
Logarithmic Scale ◽  
Brain Areas ◽  
Lognormal Distributions ◽  
Learning Rules ◽  
Heavy Tailed ◽  
The Difference

In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability.

Sign in / Sign up

Export Citation Format

Share Document