Biomaterial Database

Modeling frequency modulated responses of midbrain auditory neurons based on trigger features and artificial neural networks. 2012

T R Chang, and T W Chiu, and X Sun, and Paul W F Poon

Dept. of Computer Sciences and Information Engineering, Southern Taiwan University, Tainan, Taiwan. trchang@mail.stut.edu.tw

Frequency modulation (FM) is an important building block of communication signals for animals and human. Attempts to predict the response of central neurons to FM sounds have not been very successful, though achieving successful results could bring insights regarding the underlying neural mechanisms. Here we proposed a new method to predict responses of FM-sensitive neurons in the auditory midbrain. First we recorded single unit responses in anesthetized rats using a random FM tone to construct their spectro-temporal receptive fields (STRFs). Training of neurons in the artificial neural network to respond to a second random FM tone was based on the temporal information derived from the STRF. Specifically, the time window covered by the presumed trigger feature and its delay time to spike occurrence were used to train a finite impulse response neural network (FIRNN) to respond to this random FM. Finally we tested the model performance in predicting the response to another similar FM stimuli (third random FM tone). We found good performance in predicting the time of responses if not also the response magnitudes. Furthermore, the weighting function of the FIRNN showed temporal 'bumps' suggesting temporal integration of synaptic inputs from different frequency laminae. This article is part of a Special Issue entitled: Neural Coding.

UI	MeSH Term	Description	Entries
D008636	Mesencephalon	The middle of the three primitive cerebral vesicles of the embryonic brain. Without further subdivision, midbrain develops into a short, constricted portion connecting the PONS and the DIENCEPHALON. Midbrain contains two major parts, the dorsal TECTUM MESENCEPHALI and the ventral TEGMENTUM MESENCEPHALI, housing components of auditory, visual, and other sensorimoter systems.	Midbrain,Mesencephalons,Midbrains
D008959	Models, Neurological	Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.	Neurologic Models,Model, Neurological,Neurologic Model,Neurological Model,Neurological Models,Model, Neurologic,Models, Neurologic
D009474	Neurons	The basic cellular units of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the NERVOUS SYSTEM.	Nerve Cells,Cell, Nerve,Cells, Nerve,Nerve Cell,Neuron
D011930	Reaction Time	The time from the onset of a stimulus until a response is observed.	Response Latency,Response Speed,Response Time,Latency, Response,Reaction Times,Response Latencies,Response Times,Speed, Response,Speeds, Response
D000161	Acoustic Stimulation	Use of sound to elicit a response in the nervous system.	Auditory Stimulation,Stimulation, Acoustic,Stimulation, Auditory
D000200	Action Potentials	Abrupt changes in the membrane potential that sweep along the CELL MEMBRANE of excitable cells in response to excitation stimuli.	Spike Potentials,Nerve Impulses,Action Potential,Impulse, Nerve,Impulses, Nerve,Nerve Impulse,Potential, Action,Potential, Spike,Potentials, Action,Potentials, Spike,Spike Potential
D000818	Animals	Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA.	Animal,Metazoa,Animalia
D001307	Auditory Perception	The process whereby auditory stimuli are selected, organized, and interpreted by the organism.	Auditory Processing,Perception, Auditory,Processing, Auditory
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron
D017207	Rats, Sprague-Dawley	A strain of albino rat used widely for experimental purposes because of its calmness and ease of handling. It was developed by the Sprague-Dawley Animal Company.	Holtzman Rat,Rats, Holtzman,Sprague-Dawley Rat,Rats, Sprague Dawley,Holtzman Rats,Rat, Holtzman,Rat, Sprague-Dawley,Sprague Dawley Rat,Sprague Dawley Rats,Sprague-Dawley Rats