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Electrosensory Midbrain Neurons Display Feature Invariant Responses to Natural Communication Stimuli.

Aumentado-Armstrong T, Metzen MG, Sproule MK, Chacron MJ - PLoS Comput. Biol. (2015)

Bottom Line: Such invariant responses were not seen in hindbrain electrosensory neurons providing afferent input to these midbrain neurons, suggesting that response invariance results from nonlinear integration of such input.We found that multiple combinations of parameter values could give rise to invariant responses matching those seen experimentally.Our model thus shows that there are multiple solutions towards achieving invariant responses and reveals how subthreshold membrane conductances help promote robust and invariant firing in response to heterogeneous stimulus waveforms associated with behaviorally relevant stimuli.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, McGill University, Montreal, Quebec, Canada.

ABSTRACT
Neurons that respond selectively but in an invariant manner to a given feature of natural stimuli have been observed across species and systems. Such responses emerge in higher brain areas, thereby suggesting that they occur by integrating afferent input. However, the mechanisms by which such integration occurs are poorly understood. Here we show that midbrain electrosensory neurons can respond selectively and in an invariant manner to heterogeneity in behaviorally relevant stimulus waveforms. Such invariant responses were not seen in hindbrain electrosensory neurons providing afferent input to these midbrain neurons, suggesting that response invariance results from nonlinear integration of such input. To test this hypothesis, we built a model based on the Hodgkin-Huxley formalism that received realistic afferent input. We found that multiple combinations of parameter values could give rise to invariant responses matching those seen experimentally. Our model thus shows that there are multiple solutions towards achieving invariant responses and reveals how subthreshold membrane conductances help promote robust and invariant firing in response to heterogeneous stimulus waveforms associated with behaviorally relevant stimuli. We discuss the implications of our findings for the electrosensory and other systems.

No MeSH data available.


Subthreshold membrane conductances increase the set of parameter values for which feature invariance is observed.A) Invariance as a function of Ibias and gsyn with (left) and without (right) the subthreshold membrane conductances gT and gh. Robustness (i.e. the % of pixels for which the feature invariance score FI was greater of equal to 0.7, see Methods) was 20% (left) vs. 4% (right). B) Invariance as a function of Ibias and σB with (left) and without (right) the subthreshold membrane conductances gT and gh. Robustness was 7.8% (left) vs. 0.5% (right). C) Invariance as a function of gsyn and σB with (left) and without (right) the subthreshold membrane conductances gT and gh. Robustness was 5.9% (left) vs. 0.3% (right). For the panels on the left, we had gT = 0.236 μS and gh = 2.1 μS whereas for those on the right, we had gT = gh = 0 μS. Other parameter values were gsyn = 0.063, 0.128 μS; Ibias = –9.39, –6.57 nA; and σB = 0.42, 0.41 for the panels on the left and right columns, respectively.
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pcbi.1004430.g007: Subthreshold membrane conductances increase the set of parameter values for which feature invariance is observed.A) Invariance as a function of Ibias and gsyn with (left) and without (right) the subthreshold membrane conductances gT and gh. Robustness (i.e. the % of pixels for which the feature invariance score FI was greater of equal to 0.7, see Methods) was 20% (left) vs. 4% (right). B) Invariance as a function of Ibias and σB with (left) and without (right) the subthreshold membrane conductances gT and gh. Robustness was 7.8% (left) vs. 0.5% (right). C) Invariance as a function of gsyn and σB with (left) and without (right) the subthreshold membrane conductances gT and gh. Robustness was 5.9% (left) vs. 0.3% (right). For the panels on the left, we had gT = 0.236 μS and gh = 2.1 μS whereas for those on the right, we had gT = gh = 0 μS. Other parameter values were gsyn = 0.063, 0.128 μS; Ibias = –9.39, –6.57 nA; and σB = 0.42, 0.41 for the panels on the left and right columns, respectively.

Mentions: We next investigated why different combinations of parameter values all gave rise to feature invariant responses to natural electrocommunication stimuli. To do so, we systematically varied model parameters. Specifically, we varied the bias current Ibias, the T-type calcium conductance gT, the h-type conductance gh, the maximum synaptic conductance gsyn, the noise intensity σnoise, and the fraction of ON-type input σB. We observed negatively sloped bands when varying any two parameters except σB within this set (Fig 6A and 6B and 6C as well as the left panels of Figs 7A and 8A and 8C), indicating that increases/decreases in one parameter could be compensated for by decreases/increases in the other parameter. In contrast, we did not observe such negatively sloped bands when varying both σB and any of Ibias, gT, gh, gsyn, or σnoise (Fig 6D and left panels of Figs 7B and 7C and 8B), indicating that proper tuning of σB, which gives the relative balance of ON vs. OFF-type input received by the model neuron is necessary to achieve feature invariance as a change in this parameter cannot be compensated for by changing other parameters. We note that, by definition, parameter regions with high feature invariance correspond to regions with high CSI and low VPD values (S1 and S2 Figs). The implications of these findings are discussed below.


Electrosensory Midbrain Neurons Display Feature Invariant Responses to Natural Communication Stimuli.

Aumentado-Armstrong T, Metzen MG, Sproule MK, Chacron MJ - PLoS Comput. Biol. (2015)

Subthreshold membrane conductances increase the set of parameter values for which feature invariance is observed.A) Invariance as a function of Ibias and gsyn with (left) and without (right) the subthreshold membrane conductances gT and gh. Robustness (i.e. the % of pixels for which the feature invariance score FI was greater of equal to 0.7, see Methods) was 20% (left) vs. 4% (right). B) Invariance as a function of Ibias and σB with (left) and without (right) the subthreshold membrane conductances gT and gh. Robustness was 7.8% (left) vs. 0.5% (right). C) Invariance as a function of gsyn and σB with (left) and without (right) the subthreshold membrane conductances gT and gh. Robustness was 5.9% (left) vs. 0.3% (right). For the panels on the left, we had gT = 0.236 μS and gh = 2.1 μS whereas for those on the right, we had gT = gh = 0 μS. Other parameter values were gsyn = 0.063, 0.128 μS; Ibias = –9.39, –6.57 nA; and σB = 0.42, 0.41 for the panels on the left and right columns, respectively.
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pcbi.1004430.g007: Subthreshold membrane conductances increase the set of parameter values for which feature invariance is observed.A) Invariance as a function of Ibias and gsyn with (left) and without (right) the subthreshold membrane conductances gT and gh. Robustness (i.e. the % of pixels for which the feature invariance score FI was greater of equal to 0.7, see Methods) was 20% (left) vs. 4% (right). B) Invariance as a function of Ibias and σB with (left) and without (right) the subthreshold membrane conductances gT and gh. Robustness was 7.8% (left) vs. 0.5% (right). C) Invariance as a function of gsyn and σB with (left) and without (right) the subthreshold membrane conductances gT and gh. Robustness was 5.9% (left) vs. 0.3% (right). For the panels on the left, we had gT = 0.236 μS and gh = 2.1 μS whereas for those on the right, we had gT = gh = 0 μS. Other parameter values were gsyn = 0.063, 0.128 μS; Ibias = –9.39, –6.57 nA; and σB = 0.42, 0.41 for the panels on the left and right columns, respectively.
Mentions: We next investigated why different combinations of parameter values all gave rise to feature invariant responses to natural electrocommunication stimuli. To do so, we systematically varied model parameters. Specifically, we varied the bias current Ibias, the T-type calcium conductance gT, the h-type conductance gh, the maximum synaptic conductance gsyn, the noise intensity σnoise, and the fraction of ON-type input σB. We observed negatively sloped bands when varying any two parameters except σB within this set (Fig 6A and 6B and 6C as well as the left panels of Figs 7A and 8A and 8C), indicating that increases/decreases in one parameter could be compensated for by decreases/increases in the other parameter. In contrast, we did not observe such negatively sloped bands when varying both σB and any of Ibias, gT, gh, gsyn, or σnoise (Fig 6D and left panels of Figs 7B and 7C and 8B), indicating that proper tuning of σB, which gives the relative balance of ON vs. OFF-type input received by the model neuron is necessary to achieve feature invariance as a change in this parameter cannot be compensated for by changing other parameters. We note that, by definition, parameter regions with high feature invariance correspond to regions with high CSI and low VPD values (S1 and S2 Figs). The implications of these findings are discussed below.

Bottom Line: Such invariant responses were not seen in hindbrain electrosensory neurons providing afferent input to these midbrain neurons, suggesting that response invariance results from nonlinear integration of such input.We found that multiple combinations of parameter values could give rise to invariant responses matching those seen experimentally.Our model thus shows that there are multiple solutions towards achieving invariant responses and reveals how subthreshold membrane conductances help promote robust and invariant firing in response to heterogeneous stimulus waveforms associated with behaviorally relevant stimuli.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, McGill University, Montreal, Quebec, Canada.

ABSTRACT
Neurons that respond selectively but in an invariant manner to a given feature of natural stimuli have been observed across species and systems. Such responses emerge in higher brain areas, thereby suggesting that they occur by integrating afferent input. However, the mechanisms by which such integration occurs are poorly understood. Here we show that midbrain electrosensory neurons can respond selectively and in an invariant manner to heterogeneity in behaviorally relevant stimulus waveforms. Such invariant responses were not seen in hindbrain electrosensory neurons providing afferent input to these midbrain neurons, suggesting that response invariance results from nonlinear integration of such input. To test this hypothesis, we built a model based on the Hodgkin-Huxley formalism that received realistic afferent input. We found that multiple combinations of parameter values could give rise to invariant responses matching those seen experimentally. Our model thus shows that there are multiple solutions towards achieving invariant responses and reveals how subthreshold membrane conductances help promote robust and invariant firing in response to heterogeneous stimulus waveforms associated with behaviorally relevant stimuli. We discuss the implications of our findings for the electrosensory and other systems.

No MeSH data available.