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Single neuron model with recurrent excitation: response to slow periodic modulation
K Pakdaman1, F Alvarez, O Diez-Martínez
1B3E, INSERM U 444, ISARS, Faculté de Médecine Saint-Antoine, Paris, France. pakdaman@b3e.jussieu.fr
Bio Systems
|January 1, 1997
Summary
Recurrent excitation in neuron models creates hysteresis, where output depends on input direction. This effect widens with modulation frequency, transmission delay, and connection strength.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- Neuronal responses to periodic stimuli are crucial for information processing.
- Hysteresis, a dependence on past states, can occur in neural systems.
- Recurrent connections are fundamental to neural circuit function.
Purpose of the Study:
- To analyze the impact of recurrent excitatory connections on neuron model responses to periodic input.
- To investigate hysteresis in different neuron models under periodic modulation.
- To quantify how recurrent excitation modulates hysteresis width.
Main Methods:
- Analysis of three neuron models: graded response, threshold with adaptation, and threshold without adaptation.
- Application of slow periodic modulation to the input signal.
- Utilizing Lissajous displays to visualize output (discharge rate) versus input value.
Main Results:
- All three neuron models exhibited hysteresis in their response to periodic modulation.
- Recurrent excitation was found to increase the width of the hysteresis loop.
- Hysteresis width increased with modulation frequency, transmission delay, and recurrent connection strength.
Conclusions:
- Recurrent excitatory connections significantly influence neuronal dynamics by inducing and widening hysteresis.
- The observed hysteresis suggests a history-dependent processing capability in these models.
- Understanding hysteresis in neuronal networks is key to deciphering complex neural computations.