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Active dendrites enhance neuronal dynamic range.

Gollo LL, Kinouchi O, Copelli M - PLoS Comput. Biol. (2009)

Bottom Line: Here we show that, owing to very general properties of excitable media, the average output of a model of an active dendritic tree is a highly non-linear function of its afferent rate, attaining extremely large dynamic ranges (above 50 dB).Moreover, the model yields double-sigmoid response functions as experimentally observed in retinal ganglion cells.We predict that neurons with larger dendritic trees should have larger dynamic range and that blocking of active conductances should lead to a decrease in dynamic range.

View Article: PubMed Central - PubMed

Affiliation: Laboratório de Física Teórica e Computacional, Departamento de Física, Universidade Federal de Pernambuco, Recife, Brazil. leonardo@ifisc.uib-csic.es

ABSTRACT
Since the first experimental evidences of active conductances in dendrites, most neurons have been shown to exhibit dendritic excitability through the expression of a variety of voltage-gated ion channels. However, despite experimental and theoretical efforts undertaken in the past decades, the role of this excitability for some kind of dendritic computation has remained elusive. Here we show that, owing to very general properties of excitable media, the average output of a model of an active dendritic tree is a highly non-linear function of its afferent rate, attaining extremely large dynamic ranges (above 50 dB). Moreover, the model yields double-sigmoid response functions as experimentally observed in retinal ganglion cells. We claim that enhancement of dynamic range is the primary functional role of active dendritic conductances. We predict that neurons with larger dendritic trees should have larger dynamic range and that blocking of active conductances should lead to a decrease in dynamic range.

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Related in: MedlinePlus

Effect of heterogeneous tree activation.(A) Response functions  for the exponential activation distribution  where  refers to the branchlet generation and  controls the exponential shape. More distal branchlets (larger ) are more activated than the apical site. For large values of parameter  the sensitivity of the response function is greatly increased while the saturation remains almost the same. All curves have ,  and . (B) Dynamic range for the previous case with  activation, with an amazing enlargement of the dynamic range for  and . All cases refer to tree sizes of . (C) Response functions  for the disordered branchlet activation model with coefficient of variation , recovery probability , symmetric propagation () and . From bottom to top, . (D) The dynamic range remains the same for this disordered scenario in the tree (same parameters of panel (C)). Note that a coefficient of variation  corresponds already to a highly heterogeneous case.
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pcbi-1000402-g006: Effect of heterogeneous tree activation.(A) Response functions for the exponential activation distribution where refers to the branchlet generation and controls the exponential shape. More distal branchlets (larger ) are more activated than the apical site. For large values of parameter the sensitivity of the response function is greatly increased while the saturation remains almost the same. All curves have , and . (B) Dynamic range for the previous case with activation, with an amazing enlargement of the dynamic range for and . All cases refer to tree sizes of . (C) Response functions for the disordered branchlet activation model with coefficient of variation , recovery probability , symmetric propagation () and . From bottom to top, . (D) The dynamic range remains the same for this disordered scenario in the tree (same parameters of panel (C)). Note that a coefficient of variation corresponds already to a highly heterogeneous case.

Mentions: In Fig. 6A we show the response curves for several values of the parameter . In Fig. 6B we show that the enhancement of dynamic range obtained from the response curves (for different values of ) is robust. Surprisingly, the signal amplification and dynamic range is indeed much more efficient than the homogeneous case , attaining 80 dB (notwithstanding the poor coding for intermediate values of , where the size of the plateau increases with and ). This result suggests that this case of peripheral branchlets being more excitable could optimize the signal processing, specially for neurons with poor propagation of dendritic spikes (small , see Fig. 6B).


Active dendrites enhance neuronal dynamic range.

Gollo LL, Kinouchi O, Copelli M - PLoS Comput. Biol. (2009)

Effect of heterogeneous tree activation.(A) Response functions  for the exponential activation distribution  where  refers to the branchlet generation and  controls the exponential shape. More distal branchlets (larger ) are more activated than the apical site. For large values of parameter  the sensitivity of the response function is greatly increased while the saturation remains almost the same. All curves have ,  and . (B) Dynamic range for the previous case with  activation, with an amazing enlargement of the dynamic range for  and . All cases refer to tree sizes of . (C) Response functions  for the disordered branchlet activation model with coefficient of variation , recovery probability , symmetric propagation () and . From bottom to top, . (D) The dynamic range remains the same for this disordered scenario in the tree (same parameters of panel (C)). Note that a coefficient of variation  corresponds already to a highly heterogeneous case.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2690843&req=5

pcbi-1000402-g006: Effect of heterogeneous tree activation.(A) Response functions for the exponential activation distribution where refers to the branchlet generation and controls the exponential shape. More distal branchlets (larger ) are more activated than the apical site. For large values of parameter the sensitivity of the response function is greatly increased while the saturation remains almost the same. All curves have , and . (B) Dynamic range for the previous case with activation, with an amazing enlargement of the dynamic range for and . All cases refer to tree sizes of . (C) Response functions for the disordered branchlet activation model with coefficient of variation , recovery probability , symmetric propagation () and . From bottom to top, . (D) The dynamic range remains the same for this disordered scenario in the tree (same parameters of panel (C)). Note that a coefficient of variation corresponds already to a highly heterogeneous case.
Mentions: In Fig. 6A we show the response curves for several values of the parameter . In Fig. 6B we show that the enhancement of dynamic range obtained from the response curves (for different values of ) is robust. Surprisingly, the signal amplification and dynamic range is indeed much more efficient than the homogeneous case , attaining 80 dB (notwithstanding the poor coding for intermediate values of , where the size of the plateau increases with and ). This result suggests that this case of peripheral branchlets being more excitable could optimize the signal processing, specially for neurons with poor propagation of dendritic spikes (small , see Fig. 6B).

Bottom Line: Here we show that, owing to very general properties of excitable media, the average output of a model of an active dendritic tree is a highly non-linear function of its afferent rate, attaining extremely large dynamic ranges (above 50 dB).Moreover, the model yields double-sigmoid response functions as experimentally observed in retinal ganglion cells.We predict that neurons with larger dendritic trees should have larger dynamic range and that blocking of active conductances should lead to a decrease in dynamic range.

View Article: PubMed Central - PubMed

Affiliation: Laboratório de Física Teórica e Computacional, Departamento de Física, Universidade Federal de Pernambuco, Recife, Brazil. leonardo@ifisc.uib-csic.es

ABSTRACT
Since the first experimental evidences of active conductances in dendrites, most neurons have been shown to exhibit dendritic excitability through the expression of a variety of voltage-gated ion channels. However, despite experimental and theoretical efforts undertaken in the past decades, the role of this excitability for some kind of dendritic computation has remained elusive. Here we show that, owing to very general properties of excitable media, the average output of a model of an active dendritic tree is a highly non-linear function of its afferent rate, attaining extremely large dynamic ranges (above 50 dB). Moreover, the model yields double-sigmoid response functions as experimentally observed in retinal ganglion cells. We claim that enhancement of dynamic range is the primary functional role of active dendritic conductances. We predict that neurons with larger dendritic trees should have larger dynamic range and that blocking of active conductances should lead to a decrease in dynamic range.

Show MeSH
Related in: MedlinePlus