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Modulation of a Single Neuron Has State-Dependent Actions on Circuit Dynamics(,.)

Gutierrez GJ, Marder E - eNeuro (2014)

Bottom Line: We determined the effects of varying ḡCa , ḡK , and ḡh on the frequency, amplitude, and duty cycle of a single neuron oscillator.For a different set of network parameters, circuit behavior varied with the maximal conductances of the hub neuron.This demonstrates that neuromodulation of a single target neuron may dramatically alter the performance of an entire network when the network is in one state, but have almost no effect when the circuit is in a different state.

View Article: PubMed Central - HTML - PubMed

Affiliation: Volen Center for Complex Systems and Biology Department, Brandeis University, Waltham, Massachusetts 02454.

ABSTRACT

When does neuromodulation of a single neuron influence the output of the entire network? We constructed a five-cell circuit in which a neuron is at the center of the circuit and the remaining neurons form two distinct oscillatory subnetworks. All neurons were modeled as modified Morris-Lecar models with a hyperpolarization-activated conductance (ḡh ) in addition to calcium (ḡCa ), potassium (ḡK ), and leak conductances. We determined the effects of varying ḡCa , ḡK , and ḡh on the frequency, amplitude, and duty cycle of a single neuron oscillator. The frequency of the single neuron was highest when the ḡK and ḡh conductances were high and ḡCa was moderate whereas, in the traditional Morris-Lecar model, the highest frequencies occur when both ḡK and ḡCa are high. We randomly sampled parameter space to find 143 hub oscillators with nearly identical frequencies but with disparate maximal conductance, duty cycles, and burst amplitudes, and then embedded each of these hub neurons into networks with different sets of synaptic parameters. For one set of network parameters, circuit behavior was virtually identical regardless of the underlying conductances of the hub neuron. For a different set of network parameters, circuit behavior varied with the maximal conductances of the hub neuron. This demonstrates that neuromodulation of a single target neuron may dramatically alter the performance of an entire network when the network is in one state, but have almost no effect when the circuit is in a different state.

No MeSH data available.


Circuit behavior with different hub neurons. Each diamond represents a circuit that contains an hn candidate with intrinsic conductances at that particular point in ionic conductance space. The circuit behavior that results is encoded according to the color key on the right. A, ḡsyn1 = 6 nS, ḡel = 2 nS; most of the hn candidate-containing circuits with these synaptic parameters display a behavior in which hn is active with the slow oscillators while the fast oscillators are active at their own frequency. B, ḡsyn1 = 2 nS, ḡel = 6 nS; with these synaptic parameters, there is much more variability among the output from hn candidate-containing circuits. ḡsynHC is 5 nS for both panels.
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Figure 6: Circuit behavior with different hub neurons. Each diamond represents a circuit that contains an hn candidate with intrinsic conductances at that particular point in ionic conductance space. The circuit behavior that results is encoded according to the color key on the right. A, ḡsyn1 = 6 nS, ḡel = 2 nS; most of the hn candidate-containing circuits with these synaptic parameters display a behavior in which hn is active with the slow oscillators while the fast oscillators are active at their own frequency. B, ḡsyn1 = 2 nS, ḡel = 6 nS; with these synaptic parameters, there is much more variability among the output from hn candidate-containing circuits. ḡsynHC is 5 nS for both panels.

Mentions: In Figure 6, the resulting circuit output is shown for the two different circuit configurations. With high ḡsyn1 and low ḡel (Fig. 6A), almost all of the hn candidate-containing circuits produced qualitatively similar behavior. This behavior is characterized by hn being active with the slow half-center oscillators while the fast half-centers oscillate at their own frequency (output pattern indicated in the legend by dark blue color). Although the network pattern is largely the same for this synaptic configuration, these networks do not necessarily have identical frequencies. The networks with higher overall frequencies generally had hns with lower Ca2+ and K+ conductances and higher h-conductances (data not shown). With the synaptic conductance values inverted so that ḡsyn1 is low and ḡel is high, a number of qualitatively different circuit behaviors are seen for the different hn neurons (Fig. 6B). Several of them produce the behavior represented by dark blue in which hn is active with the slow oscillators, especially when hn has a relatively high h-conductance. Unlike in Figure 6A, Figure 6B displays a variety of other circuit output patterns. Clustered around low values of ḡh are circuits in which hn joins the fast oscillators (represented by red). Also for low values of ḡh but spanning the range of ḡCa, there are circuits in which all but the s1 neuron are part of the fast oscillator (purple). There are also a few circuits in which all five neurons are active at the same frequency (green). These tend to have low values of all three ionic conductances. Finally, there are circuits (white diamonds) that were sufficiently irregular to defy simple classification. Such circuits tend to lie at the boundaries between clusters of circuits with classifiable behavior.


Modulation of a Single Neuron Has State-Dependent Actions on Circuit Dynamics(,.)

Gutierrez GJ, Marder E - eNeuro (2014)

Circuit behavior with different hub neurons. Each diamond represents a circuit that contains an hn candidate with intrinsic conductances at that particular point in ionic conductance space. The circuit behavior that results is encoded according to the color key on the right. A, ḡsyn1 = 6 nS, ḡel = 2 nS; most of the hn candidate-containing circuits with these synaptic parameters display a behavior in which hn is active with the slow oscillators while the fast oscillators are active at their own frequency. B, ḡsyn1 = 2 nS, ḡel = 6 nS; with these synaptic parameters, there is much more variability among the output from hn candidate-containing circuits. ḡsynHC is 5 nS for both panels.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4596081&req=5

Figure 6: Circuit behavior with different hub neurons. Each diamond represents a circuit that contains an hn candidate with intrinsic conductances at that particular point in ionic conductance space. The circuit behavior that results is encoded according to the color key on the right. A, ḡsyn1 = 6 nS, ḡel = 2 nS; most of the hn candidate-containing circuits with these synaptic parameters display a behavior in which hn is active with the slow oscillators while the fast oscillators are active at their own frequency. B, ḡsyn1 = 2 nS, ḡel = 6 nS; with these synaptic parameters, there is much more variability among the output from hn candidate-containing circuits. ḡsynHC is 5 nS for both panels.
Mentions: In Figure 6, the resulting circuit output is shown for the two different circuit configurations. With high ḡsyn1 and low ḡel (Fig. 6A), almost all of the hn candidate-containing circuits produced qualitatively similar behavior. This behavior is characterized by hn being active with the slow half-center oscillators while the fast half-centers oscillate at their own frequency (output pattern indicated in the legend by dark blue color). Although the network pattern is largely the same for this synaptic configuration, these networks do not necessarily have identical frequencies. The networks with higher overall frequencies generally had hns with lower Ca2+ and K+ conductances and higher h-conductances (data not shown). With the synaptic conductance values inverted so that ḡsyn1 is low and ḡel is high, a number of qualitatively different circuit behaviors are seen for the different hn neurons (Fig. 6B). Several of them produce the behavior represented by dark blue in which hn is active with the slow oscillators, especially when hn has a relatively high h-conductance. Unlike in Figure 6A, Figure 6B displays a variety of other circuit output patterns. Clustered around low values of ḡh are circuits in which hn joins the fast oscillators (represented by red). Also for low values of ḡh but spanning the range of ḡCa, there are circuits in which all but the s1 neuron are part of the fast oscillator (purple). There are also a few circuits in which all five neurons are active at the same frequency (green). These tend to have low values of all three ionic conductances. Finally, there are circuits (white diamonds) that were sufficiently irregular to defy simple classification. Such circuits tend to lie at the boundaries between clusters of circuits with classifiable behavior.

Bottom Line: We determined the effects of varying ḡCa , ḡK , and ḡh on the frequency, amplitude, and duty cycle of a single neuron oscillator.For a different set of network parameters, circuit behavior varied with the maximal conductances of the hub neuron.This demonstrates that neuromodulation of a single target neuron may dramatically alter the performance of an entire network when the network is in one state, but have almost no effect when the circuit is in a different state.

View Article: PubMed Central - HTML - PubMed

Affiliation: Volen Center for Complex Systems and Biology Department, Brandeis University, Waltham, Massachusetts 02454.

ABSTRACT

When does neuromodulation of a single neuron influence the output of the entire network? We constructed a five-cell circuit in which a neuron is at the center of the circuit and the remaining neurons form two distinct oscillatory subnetworks. All neurons were modeled as modified Morris-Lecar models with a hyperpolarization-activated conductance (ḡh ) in addition to calcium (ḡCa ), potassium (ḡK ), and leak conductances. We determined the effects of varying ḡCa , ḡK , and ḡh on the frequency, amplitude, and duty cycle of a single neuron oscillator. The frequency of the single neuron was highest when the ḡK and ḡh conductances were high and ḡCa was moderate whereas, in the traditional Morris-Lecar model, the highest frequencies occur when both ḡK and ḡCa are high. We randomly sampled parameter space to find 143 hub oscillators with nearly identical frequencies but with disparate maximal conductance, duty cycles, and burst amplitudes, and then embedded each of these hub neurons into networks with different sets of synaptic parameters. For one set of network parameters, circuit behavior was virtually identical regardless of the underlying conductances of the hub neuron. For a different set of network parameters, circuit behavior varied with the maximal conductances of the hub neuron. This demonstrates that neuromodulation of a single target neuron may dramatically alter the performance of an entire network when the network is in one state, but have almost no effect when the circuit is in a different state.

No MeSH data available.