Abstract
The respiratory rhythm generator is spectacular in its ability to support a wide range of activities and adapt to changing environmental conditions, yet its operating mechanisms remain elusive. We show how selective control of inspiration and expiration times can be achieved in a new representation of the neural system (called a Boolean network). The new framework enables us to predict the behavior of neural networks based on properties of neurons, not their values. Hence, it reveals the logic behind the neural mechanisms that control the breathing pattern. Our network mimics many features seen in the respiratory network such as the transition from a 3-phase to 2-phase to 1-phase rhythm, providing novel insights and new testable predictions.
Highlights
The respiratory rhythm generator is spectacular in its ability to support a wide range of activities and adapt to changing environmental conditions, yet its operating mechanisms remain elusive
In extreme conditions of hypoxia, and despite being embedded in the brainstem where it could potentially interact with other populations of neurons, only the pre Bötzinger Complex (preBötC) population remains active, generating a 1-phase pattern, similar to the pattern generated by the isolated preBötC population6,14
The Boolean network we present in this paper enables us to explore a key question for understanding control of breathing: how are the activation and quiescent times in a bursting signal changed selectively by varying the rate of tonic spikes in a control input signal? Such control of timing is crucial for supporting a wide range of activities involving breathing with diverse and dynamic combinations of inspiration and expiration times
Summary
The respiratory rhythm generator is spectacular in its ability to support a wide range of activities and adapt to changing environmental conditions, yet its operating mechanisms remain elusive. The new framework enables us to predict the behavior of neural networks based on properties of neurons, not their values It reveals the logic behind the neural mechanisms that control the breathing pattern. An isolated single PreBötC neuron could generate tonic spiking (a non-interrupted sequence of action potentials), bursting (a repeating pattern that consists of a sequence of action potentials followed by a time interval with no action potentials) or silence (no action potentials). An isolated single PreBötC neuron could generate tonic spiking (a non-interrupted sequence of action potentials), bursting (a repeating pattern that consists of a sequence of action potentials followed by a time interval with no action potentials) or silence (no action potentials)8 These signals are transmitted, through other populations of neurons, to spinal motor neurons that activate the respiratory muscle. The Boolean network we present in this paper enables us to explore a key question for understanding control of breathing: how are the activation and quiescent times in a bursting signal changed selectively by varying the rate of tonic spikes in a control input signal? Such control of timing is crucial for supporting a wide range of activities involving breathing with diverse and dynamic combinations of inspiration and expiration times
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