Mapping Function Onto Neuronal Morphology

Stiefel, Klaus M.; Sejnowski, Terrence J.

doi:10.1152/jn.00865.2006

Cited by 51 publications

(48 citation statements)

References 28 publications

(27 reference statements)

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“…Evidence suggests that sensitivities to other parameters, such as those describing passive cable properties, ion channel kinetics, and dendritic channel densities, may also be high [22,49]. Other firing rate and gain sensitivity trends may also exist along dimensions of Figures 3A and 4D.…”

Section: Sensitivities Compare Influences Of Different Classes Of Parmentioning

confidence: 96%

“…The most appropriate output measures for a particular study will depend on the type and function of the neuron being analyzed. In general, sensitivities of somatocentric functions such as firing rate are likely to be highest to channel kinetics [49] and dendritic channel density gradients and intercepts ( Figure S6), while sensitivities of dendritic functions such as AP backpropagation and propagation and integration of synaptic inputs are likely to be highest to passive parameters [22] and dendritic channel density gradients. Morphologic parameters are likely to influence both somatocentric and dendritic functions [11,13,22].…”

Section: Sensitivities Compare Influences Of Different Classes Of Parmentioning

confidence: 99%

“…The influence of morphology on neuronal processing is further enhanced by active ion channels distributed throughout the dendrites [14,[17][18][19]. Previous computational studies have identified conductance [2,3,6,20] and morphologic [11,21,22] parameters that drive general firing patterns, but have not quantified how these different parameters influence individual models across parameter space. Moreover, few studies have analyzed the contributions of intracellular calcium (Ca 2þ ) dynamics to firing patterns [21], despite the crucial role of intracellular Ca 2þ in shaping membrane potential, synaptic transmission, and signaling cascades [23].…”

Section: Introductionmentioning

confidence: 99%

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Neuronal Firing Sensitivity to Morphologic and Active Membrane Parameters

Weaver¹,

Wearne²

2005

PLoS Comput Biol

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Both the excitability of a neuron's membrane, driven by active ion channels, and dendritic morphology contribute to neuronal firing dynamics, but the relative importance and interactions between these features remain poorly understood. Recent modeling studies have shown that different combinations of active conductances can evoke similar firing patterns, but have neglected how morphology might contribute to homeostasis. Parameterizing the morphology of a cylindrical dendrite, we introduce a novel application of mathematical sensitivity analysis that quantifies how dendritic length, diameter, and surface area influence neuronal firing, and compares these effects directly against those of active parameters. The method was applied to a model of neurons from goldfish Area II. These neurons exhibit, and likely contribute to, persistent activity in eye velocity storage, a simple model of working memory. We introduce sensitivity landscapes, defined by local sensitivity analyses of firing rate and gain to each parameter, performed globally across the parameter space. Principal directions over which sensitivity to all parameters varied most revealed intrinsic currents that most controlled model output. We found domains where different groups of parameters had the highest sensitivities, suggesting that interactions within each group shaped firing behaviors within each specific domain. Application of our method, and its characterization of which models were sensitive to general morphologic features, will lead to advances in understanding how realistic morphology participates in functional homeostasis. Significantly, we can predict which active conductances, and how many of them, will compensate for a given age-or development-related structural change, or will offset a morphologic perturbation resulting from trauma or neurodegenerative disorder, to restore normal function. Our method can be adapted to analyze any computational model. Thus, sensitivity landscapes, and the quantitative predictions they provide, can give new insight into mechanisms of homeostasis in any biological system.

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Section: Sensitivities Compare Influences Of Different Classes Of Parmentioning

confidence: 96%

Section: Sensitivities Compare Influences Of Different Classes Of Parmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neuronal Firing Sensitivity to Morphologic and Active Membrane Parameters

Weaver¹,

Wearne²

2005

PLoS Comput Biol

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“…For example, shape can influence whether a pyramidal cell fires tonically or in bursts (van Elburg & van Ooyen 2010). Certain shapes can also make a neuron better at linear summation of EPSPs, as one might see in a sensory neuron, or at distinguishing the temporal order of EPSPs, as would be expected of a coincidence detector in a circuit that learns (Stiefel & Sejnowski 2007). Whether these shape parameters are common is unclear, although these studies cite several cases in which neurons have both the predicted shape and physiology.…”

Section: Why Does Shape Matter?mentioning

confidence: 99%

Development of Dendritic Form and Function

Lefebvre¹,

Sanes

Kay

2015

Annu. Rev. Cell Dev. Biol.

205

211

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The nervous system is populated by numerous types of neurons, each bearing a dendritic arbor with a characteristic morphology. These type-specific features influence many aspects of a neuron's function, including the number and identity of presynaptic inputs and how inputs are integrated to determine firing properties. Here, we review the mechanisms that regulate the construction of cell type-specific dendrite patterns during development. We focus on four aspects of dendrite patterning that are particularly important in determining the function of the mature neuron: (a) dendrite shape, including branching pattern and geometry of the arbor; (b) dendritic arbor size;

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“…For example, an optimization procedure was used by Stiefel and Sejnowski [25] to find neuronal morphological structures (with passive electrical membrane properties only) for two computational tasks: for linearly summing excitatory synaptic potentials (EPSPs), and to distinguish the temporal order of EPSPs. The solutions resembled the morphology of real neurons.…”

Section: Resultsmentioning

confidence: 99%

Neuronal Morphology: Shape Characteristics and Models

Schierwagen

2008

Neurophysiology

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This paper is focused on quantification (morphometry) and modeling of neuronal morphological complexity. First, computer-aided methods for reconstruction, processing, and analysis of raw morphological data are reviewed. Then, topological and metrical measures are touched upon. Fractal measures (together with the extension of multiscale fractal dimension) are presented more explicitly. Models of neuronal arborizations are differentiated between reconstruction models and growth models (stochastic or mechanistic). The growth model approach is discussed in more detail. The methods presented are applied to several types of neurons and shown to have considerable discriminative power. Recent developments stress the importance of these methods for optimizing virtual neuronal trees in view of functional characteristics of the neurons.

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Mapping Function Onto Neuronal Morphology

Cited by 51 publications

References 28 publications

Neuronal Firing Sensitivity to Morphologic and Active Membrane Parameters

Neuronal Firing Sensitivity to Morphologic and Active Membrane Parameters

Development of Dendritic Form and Function

Neuronal Morphology: Shape Characteristics and Models

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