Neural Simulation
Neural Simulation - Related Videos
10:34
Designing and Implementing Nervous System Simulations on LEGO Robots
0 Views •
Cited by 7 •
2013
An approach to neural network modeling on the LEGO Mindstorms robotics platform is presented. The method provides a simulation tool for invertebrate neuroscience research in both the research lab and the classroom. This technique enables the investigation of biomimetic robot control principles.
10:32
Enumeration of Neural Stem Cells Using Clonal Assays
0 Views •
Cited by 5 •
2016
Neural stem cells (NSCs) refer to cells which can self-renew and differentiate into the three neural lineages. Here, we describe a protocol to determine NSC frequency in a given cell population using neurosphere formation and differentiation under clonal conditions.
09:46
MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
0 Views •
Cited by 48 •
2012
An efficient way to gain insight into how humans navigate themselves in three dimensions is described. The method takes advantage of a motion simulator capable of moving observers in ways unattainable by traditional simulators. Results confirm that movement in the horizontal plane is underestimated, while vertical movement is overestimated.
08:45
Study of Siphon Breaker Experiment and Simulation for a Research Reactor
0 Views •
Cited by 2 •
2017
The siphon breaking phenomenon was investigated experimentally and a theoretical model was proposed. A simulation program based on the theoretical model was developed and the results of the simulation program were compared with experimental results. It was concluded that the results of the simulation program matched the experimental results well.
13:44
Simulation, Fabrication and Characterization of THz Metamaterial Absorbers
0 Views •
Cited by 6 •
2012
This protocol outlines the simulation, fabrication and characterization of THz metamaterial absorbers. Such absorbers, when coupled with an appropriate sensor, have applications in THz imaging and spectroscopy.
FAQs
Models represent membrane-potential changes and synaptic communication as interacting processes that shape electrical activity across neurons or circuits. Mathematical equations or software-based networks can reproduce how signals are generated, transmitted, and processed under selected conditions. This makes it possible to examine how local cellular events contribute to larger patterns of neural activity.
Connectivity and cellular properties are major determinants of simulated neural behavior. Altering how neurons are connected can change signal flow and circuit-level activity, while changing cellular properties can affect how individual neurons generate or transmit signals. Comparing these conditions helps researchers identify which features influence information processing and circuit responses.
Defined conditions establish the setting in which a model’s electrical activity or circuit behavior is examined. Keeping those conditions specified allows researchers to compare alternative circuit configurations, test hypotheses consistently, and determine how particular changes affect the outcome. Without a defined framework, differences in simulated activity would be harder to attribute to specific model features.
A study can begin by representing relevant nervous-system structures or activities with mathematical equations or a software-based network. Researchers then examine the model under defined conditions, compare circuit behaviors, and alter connectivity or cellular properties to test predictions. The resulting comparisons can guide experimental design and help evaluate hypotheses about brain function.
Researchers can use this approach to test hypotheses about brain function, compare circuit behaviors, and explore how specific changes affect information processing. It is also useful when a study needs predictive models of neural dynamics or guidance for experimental design. Simulated comparisons provide a way to examine consequences of defined changes before or alongside laboratory investigation.
The method provides models in which changes to connectivity or cellular properties can be examined for their effects on neural activity and information processing. That capability supports studies of neurological disorders and the development of predictive models. It also contributes to brain-inspired computing by using simulated neural dynamics to inform systems modeled on nervous-system function.