Single neuron model with recurrent excitation: response to slow periodic modulation. 1997

K Pakdaman, and F Alvarez, and O Diez-Martínez, and J F Vibert
B3E, INSERM U 444, ISARS, Faculté de Médecine Saint-Antoine, Paris, France. pakdaman@b3e.jussieu.fr

The influence of a recurrent excitatory connection on the response of three neuron models to slow periodic modulation is analyzed. The models are the graded response model and the threshold model with and without adaptation. Lissajous displays of the system's output (discharge rate) as a function of the instantaneous input value show hysteresis in all three models. Hence, the outputs are different depending on whether the input is increasing or decreasing. Recurrent excitation increases the width of the hysteresis with (i) the frequency of the periodic modulation, (ii) the transmission delay of the recurrent connection, and (iii) the connection strength.

UI MeSH Term Description Entries
D008954 Models, Biological Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment. Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
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
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
D000222 Adaptation, Physiological The non-genetic biological changes of an organism in response to challenges in its ENVIRONMENT. Adaptation, Physiologic,Adaptations, Physiologic,Adaptations, Physiological,Adaptive Plasticity,Phenotypic Plasticity,Physiological Adaptation,Physiologic Adaptation,Physiologic Adaptations,Physiological Adaptations,Plasticity, Adaptive,Plasticity, Phenotypic

Related Publications

K Pakdaman, and F Alvarez, and O Diez-Martínez, and J F Vibert
July 1996, Neural networks : the official journal of the International Neural Network Society,
K Pakdaman, and F Alvarez, and O Diez-Martínez, and J F Vibert
January 1974, Kybernetik,
K Pakdaman, and F Alvarez, and O Diez-Martínez, and J F Vibert
December 1992, Mathematical biosciences,
K Pakdaman, and F Alvarez, and O Diez-Martínez, and J F Vibert
August 2019, Scientific reports,
K Pakdaman, and F Alvarez, and O Diez-Martínez, and J F Vibert
July 2007, European biophysics journal : EBJ,
K Pakdaman, and F Alvarez, and O Diez-Martínez, and J F Vibert
July 2003, The Journal of the Acoustical Society of America,
K Pakdaman, and F Alvarez, and O Diez-Martínez, and J F Vibert
January 1964, Comptes rendus des seances de la Societe de biologie et de ses filiales,
K Pakdaman, and F Alvarez, and O Diez-Martínez, and J F Vibert
March 2018, The Journal of chemical physics,
K Pakdaman, and F Alvarez, and O Diez-Martínez, and J F Vibert
January 1987, Biological cybernetics,
K Pakdaman, and F Alvarez, and O Diez-Martínez, and J F Vibert
May 2003, Physical review. E, Statistical, nonlinear, and soft matter physics,
Copied contents to your clipboard!