scholarly journals A neural circuit model for a contextual association task inspired by recommender systems

Hippocampus ◽  
2020 ◽  
Vol 30 (4) ◽  
pp. 384-395 ◽  
Author(s):  
Henghui Zhu ◽  
Ioannis Ch. Paschalidis ◽  
Allen Chang ◽  
Chantal E. Stern ◽  
Michael E. Hasselmo
Keyword(s):  
Recommender Systems ◽  
Neural Circuit ◽  
Circuit Model ◽  
Association Task ◽  
Contextual Association

A Dynamic Body Model of the NematodeC. eleganswith Neural Oscillators

2005 ◽  
Vol 17 (3) ◽  
pp. 318-326 ◽  
Author(s):  
Michiyo Suzuki ◽  
◽  
Takeshi Goto ◽  
Toshio Tsuji ◽  
Hisao Ohtake ◽  
...  
Keyword(s):  
Computer Simulation ◽  
Motor Control ◽  
Caenorhabditis Elegans ◽  
Neural Circuit ◽  
Rhythmic Movement ◽  
Circuit Model ◽  
Neural Oscillators ◽  
C Elegans ◽  
Body Model ◽  
Multicellular Organisms

The nematode <I>Caenorhabditis elegans (C. elegans)</I>, a relatively simple organism in structure, is one of the most well-studied multicellular organisms. We developed a <I>virtual C. elegans</I> based on the actual organism to analyze motor control. We propose a dynamic body model, including muscles, controlled by a neural circuit model based on the actual nematode. The model uses neural oscillators to generate rhythmic movement. Computer simulation confirmed that the <I>virtual C. elegans</I> realizes motor control similar qualitatively to that of the actual organism. Specified classes of neurons are killed in the neural circuit model corresponding to actual <I>unc</I> mutants, demonstrating that resulting movement of the <I>virtual C. elegans</I> resembles that of actual mutants.

Towards a neural circuit model of verbal humor processing: An fMRI study of the neural substrates of incongruity detection and resolution

NeuroImage ◽  
2013 ◽  
Vol 66 ◽  
pp. 169-176 ◽  
Author(s):  
Yu-Chen Chan ◽  
Tai-Li Chou ◽  
Hsueh-Chih Chen ◽  
Yu-Chu Yeh ◽  
Joseph P. Lavallee ◽  
...  
Keyword(s):  
Neural Circuit ◽  
Neural Substrates ◽  
Circuit Model ◽  
Fmri Study

scholarly journals A spiking neural circuit model for learning multi-sensory integration

2020 ◽  
Author(s):  
Deying Song ◽  
Xueyan Niu ◽  
Wen-Hao Zhang ◽  
Tai Sing Lee
Keyword(s):  
Information Integration ◽  
Sensory Integration ◽  
Neural Circuit ◽  
Hebbian Learning ◽  
Neural Model ◽  
Circuit Model ◽  
Cue Integration ◽  
Medial Superior Temporal ◽  
Vestibular Information ◽  
Sensory Modalities

AbstractNeurons in visual and vestibular information integration areas of macaque brain such as medial superior temporal (MSTd) and ventral intraparietal (VIP) have been classified into congruent neurons and opposite neurons, which prefer congruent inputs and opposite inputs from the two sensory modalities, respectively. In this work, we propose a mechanistic spiking neural model that can account for the emergence of congruent and opposite neurons and their interactions in a neural circuit for multi-sensory integration. The spiking neural circuit model is adopted from an established model for the circuits of the primary visual cortex with little changes in parameters. The network can learn, based on the basic Hebbian learning principle, the correct topological organization and behaviors of the congruent and opposite neurons that have been proposed to play a role in multi-sensory integration. This work explore the constraints and the conditions that lead to the development of a proposed neural circuit for cue integration. It also demonstrates that such neural circuit might indeed be a canonical circuit shared by computations in many cortical areas.

Sign in / Sign up

Export Citation Format

Share Document