Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dengue Infection in Pediatric Patients With Malignancies: A Single-center Retrospective Study.

Journal of pediatric hematology/oncology·2025
Same author

Clinical profile and outcome of pediatric synovial sarcoma - Eleven-year experience from a tertiary cancer center in LMIC.

Indian journal of cancer·2025
Same author

Survival and prognostic factors of childhood Ewing sarcoma - Experience from a cancer center in South India.

Indian journal of cancer·2025
Same author

Resource-adapted strategies in the management of paediatric Burkitt lymphoma in low- and middle-income country setting and outcomes: An Indian centre experience.

British journal of haematology·2025
Same author

Serum Immunoglobulin Levels in Children with Acute Lymphoblastic Leukemia During Maintenance Chemotherapy and its Association with Severe Febrile Illness.

Indian pediatrics·2025
Same author

Outcome of Reconstruction with Irradiated Tumour Bone in Paediatric Malignant Bone Tumours.

Indian journal of surgical oncology·2025
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026
See all related articles

Related Experiment Video

Updated: Nov 10, 2025

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.1K

Temporal constrained objects for modelling neuronal dynamics.

Manjusha Nair1,2, Jinesh Manchan Kannimoola3, Bharat Jayaraman4

  • 1Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India.

Peerj. Computer Science
|April 5, 2021
PubMed
Summary
This summary is machine-generated.

Temporal constrained objects offer a new way to model complex systems, especially neural networks, by combining object-oriented programming with temporal constraints for efficient simulation and analysis.

Keywords:
Constraint programmingDeclarative modellingNeuron modelsObject-oriented languagesTemporal constrained objects

More Related Videos

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.0K
Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.1K

Related Experiment Videos

Last Updated: Nov 10, 2025

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.1K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.0K
Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.1K

Area of Science:

  • Computational neuroscience
  • Computer science
  • Complex systems modeling

Background:

  • Modeling complex dynamical systems requires balancing model realism with computational tractability.
  • Existing programming languages and technologies present challenges in representing diverse levels of abstraction for system dynamics.

Purpose of the Study:

  • To introduce a novel programming paradigm, temporal constrained objects, for principled modeling of complex dynamical systems.
  • To facilitate the analysis and prediction of system dynamic behavior, particularly in neuronal systems.

Main Methods:

  • Temporal constrained objects extend constrained objects by incorporating temporal constraints for dynamic behavior analysis.
  • Neuronal system structures are represented using objects, while dynamics are modeled via declarative temporal constraints.
  • Computation involves constraint satisfaction within a time-based simulation environment.

Main Results:

  • Demonstrated feasibility of mapping neuron and synapse models to temporal constrained objects.
  • Successfully modeled simple neuronal networks by composing circuit components.
  • Achieved significant conciseness in model formulation and reduced code for simulation control.

Conclusions:

  • Temporal constrained objects offer powerful capabilities for modeling neural system structure and dynamics.
  • The paradigm's declarative nature and integration of object-oriented features make it suitable for complex systems.
  • The approach is well-suited for efficient execution on parallel architectures for large-scale brain circuit simulations.