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Intrinsic dynamics in neuronal networks. II. experiment
P E Latham1, B J Richmond, S Nirenberg
1Department of Neurobiology, University of California at Los Angeles, Los Angeles, California 90095, USA.
Journal of Neurophysiology
|February 11, 2000
Summary
The fraction of endogenously active neurons controls central nervous system (CNS) firing patterns. Reducing this fraction transitions steady firing to bursting, while eliminating these cells leads to silence.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cellular Neuroscience
Background:
- Neurons in the mammalian central nervous system (CNS) exhibit spontaneous activity, including steady firing and rhythmic bursting.
- The mechanisms maintaining these firing patterns and the transitions between them, particularly under recurrent excitation, are not fully understood.
Purpose of the Study:
- To experimentally investigate the role of endogenously active cells in maintaining spontaneous neuronal firing patterns in the CNS.
- To validate theoretical predictions regarding the relationship between the fraction of endogenously active cells and network firing states.
Main Methods:
- Experimental manipulation of neuronal cultures to alter the fraction of endogenously active cells.
- Observation and analysis of resulting changes in network firing patterns (steady firing vs. bursting).
Main Results:
- All spontaneously, steadily firing neuronal cultures contained endogenously active cells.
- Reducing the proportion of endogenously active cells in these cultures induced a transition from steady firing to bursting.
- These experimental findings corroborate previous theoretical models on firing pattern control.
Conclusions:
- The fraction of endogenously active cells is a critical determinant of spontaneous neuronal firing patterns in the CNS.
- A sufficient proportion of endogenously active cells is necessary to maintain steady firing; below a threshold, bursting occurs, and at zero, silence ensues.
- Experimental results strongly support the theoretical framework linking endogenous activity to network dynamics.