Scientific Understanding of Consciousness
Dendritic Spines nonlinear processing enhances neuronal computation
Nature, 491, 599–602 (22 November 2012)
Synaptic amplification by dendritic spines enhances input cooperativity
Mark T. Harnett & Judit K. Makara, with Nelson Spruston, Jeffrey C. Magee, William L. Kath
HHMI Janelia Farm Research Campus, Ashburn, Virginia 20147, USA
Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest 1083, Hungary
Departments of Applied Mathematics and Neurobiology, Northwestern University, Evanston, Illinois 60208, USA
Dendritic spines are the nearly ubiquitous site of excitatory synaptic input onto neurons and as such are critically positioned to influence diverse aspects of neuronal signalling. Decades of theoretical studies have proposed that spines may function as highly effective and modifiable chemical and electrical compartments that regulate synaptic efficacy, integration and plasticity. Experimental studies have confirmed activity-dependent structural dynamics and biochemical compartmentalization by spines. However, there is a longstanding debate over the influence of spines on the electrical aspects of synaptic transmission and dendritic operation. Here we measure the amplitude ratio of spine head to parent dendrite voltage across a range of dendritic compartments and calculate the associated spine neck resistance (Rneck) for spines at apical trunk dendrites in rat hippocampal CA1 pyramidal neurons. We find that Rneck is large enough (~500 MΩ) to amplify substantially the spine head depolarization associated with a unitary synaptic input by ~1.5- to ~45-fold, depending on parent dendritic impedance. A morphologically realistic compartmental model capable of reproducing the observed spatial profile of the amplitude ratio indicates that spines provide a consistently high-impedance input structure throughout the dendritic arborization. Finally, we demonstrate that the amplification produced by spines encourages electrical interaction among coactive inputs through an Rneck-dependent increase in spine head voltage-gated conductance activation. We conclude that the electrical properties of spines promote nonlinear dendritic processing and associated forms of plasticity and storage, thus fundamentally enhancing the computational capabilities of neurons.
To measure the ratio of spine-to-dendrite voltage amplitude and associated Rneck, we combined two-photon Ca2+ imaging and glutamate uncaging with dual dendritic patch-clamp current injection and voltage recording from hippocampal CA1 pyramidal neurons in acute slices from adult rats.
These data indicate that spines function as high-impedance input compartments that passively amplify synaptic depolarization locally within the spine head to well over what could be achieved by synapses directly onto dendrites. Thus, unitary synaptic inputs may effectively recruit spine voltage-dependent conductances such as N-methyl-d-aspartate receptors (NMDARs).
We next compared the electrical properties of spines across various dendritic compartments. The results show that presence of a large, yet modifiable, Rneck allows dendritic spines to function as consistent, yet adjustable, high-impedance input structures throughout the apical dendritic arborization of CA1 pyramidal neurons.
Of the parameters affecting dendritic impedance (axial resistance, membrane capacitance, membrane resistance) only membrane resistance is readily modulated. We therefore investigated the role of membrane resistance in spine electrical function by blocking a variety of voltage-dependent ion channels. The results illustrate that, in addition to Rneck, the most important determinants of passive spine voltage amplification are morphological factors that control the magnitude of axial current (that is, dendritic diameter as well as proximity to branch and end points). Because such factors are not easily modified, changes in Rneck would be the most tenable approach to altering the amplifying properties of spines.
The passive amplification capabilities of spines could potentially increase the recruitment of active voltage-dependent conductances at the site of input, thereby enhancing interactions among multiple synaptic inputs. To test this idea we used multi-site uncaging and simultaneous Ca2+ imaging with NMDARs intact to produce voltage and single-spine Ca2+ input–output curves at individual apical oblique branches. The simulations show that passive electrical amplification by spines promotes the recruitment of local active voltage-dependent conductances by multiple inputs, increasing the amount of above-linear summation.
The presence of high-impedance spines therefore inherently augments input cooperativity by promoting electrical cross-talk between coactive synaptic inputs, providing a mechanism whereby activity-dependent changes in Rneck can regulate synaptic efficacy and nonlinear dendritic processing among potentiated synapses.
Our results provide insight into how the intrinsic properties of dendritic spines allow them to fundamentally shape neuronal processing and storage. Spines exhibit a high neck resistance (varying around 500 MΩ) that passively amplifies local synaptic depolarization up to 50-fold. This amplification increases the activation of voltage-dependent processes within the spine head, enhances the interaction among coactive spines, and increases nonlinear dendritic integration. Furthermore, spines endow individual synapses with the ability to locally control the amount of passive (ohmic) and active (voltage-dependent conductance-based) amplification they experience through the regulation of Rneck. The amplifying and coordinating properties of dendritic spines we have described here will have a profound effect on neuronal input processing, and will also influence information storage by promoting the induction of clustered forms of synaptic and dendritic plasticity among coactive spines. Thus, spines enhance the ability of neurons to detect, uniquely respond to, and store distinct synaptic input patterns.
[end of paraphrase]
Return to — Dendritic Trees
Return to — Neural Network