Mechanisms underlying subunit independence in pyramidal neuron dendrites

Abstract

Pyramidal neuron (PN) dendrites compartmentalize voltage signals and can generate local spikes, which has led to the proposal that their dendrites act as independent computational subunits within a multilayered processing scheme. However, when a PN is strongly activated, back-propagating action potentials (bAPs) sweeping outward from the soma synchronize dendritic membrane potentials many times per second. How PN dendrites maintain the independence of their voltage-dependent computations, despite these repeated voltage resets, remains unknown. Using a detailed compartmental model of a layer 5 PN, and an improved method for quantifying subunit independence that incorporates a more accurate model of dendritic integration, we first established that the output of each dendrite can be almost perfectly predicted by the intensity and spatial configuration of its own synaptic inputs, and is nearly invariant to the rate of bAP-mediated "cross-talk" from other dendrites over a 100-fold range. Then, through an analysis of conductance, voltage, and current waveforms within the model cell, we identify three biophysical mechanisms that together help make independent dendritic computation possible in a firing neuron, suggesting that a major subtype of neocortical neuron has been optimized for layered, compartmentalized processing under in-vivo-like spiking conditions.O n the path to understanding the diverse information processing functions of the brain, it is essential to develop simplified models of individual neurons. The classical view that a neuron collects excitatory and inhibitory influences from across its dendritic arbor and passively funnels them to a single spikegenerating zone near the soma has been challenged by the finding that dendrites generate local spikes (1-15) and can support a variety of compartmentalized computations (16-28). We previously showed that the input-output (i/o) behavior of a dendritic subtree, whose branches emanate from a main trunk or soma, can be described by a two-layer model (2LM), where the first layer consists of multiple independent dendritic "subunits" with stereotyped nonlinear i/o functions. In the second layer, corresponding to the soma, the dendritic outputs are summed and fed through the cell's somatic firing rate-current (f-I) curve (8,21,22,29) (Fig. 1A). The core assumption of the 2LM is that each dendrite's output depends only on its synaptic inputs, and is independent of the activity of other dendrites or the cell as a whole. The 2LM was previously tested in both experimental and modeling studies, outperforming passive dendrite (one-layer) models in predicting pyramidal neuron (PN) responses both to brief inputs leading to subthreshold somatic responses (8,29) and to high-frequency stimulation that produced output trains lasting hundreds of milliseconds (21,22). Notwithstanding the improved description of PN responses provided by two-layer models, a substantial fraction of the response variance remained unexplained by 2LM predictions in those previous studies (21, 22), leav...

An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites

“…In contrast to the LN subunit assumption, which is clearly in need of revision, the other key assumption of the 2LM—that thin dendrites act as independent subunits [74], [83], [95], [96]—has received further support in recent years [1], [84], [140], [155]. Thus, our current working model of a PN subtree retains its two-layer structure, but includes an upgraded definition of the subunit function $$d_j=g(E\left(x\right),I\left(x\right))$$ where E ( x ) and I ( x ) are the spatial patterns of excitation and inhibition, respectively, impinging on a dendrite.…”

“…It is obvious that few real dendritic trees conform faithfully to the assumption that all of their dendrites are terminal, unbranched, and emanate directly from a central node, i.e., the morphology that would tend to maximize dendritic independence. Instead, thin dendrites within a typical subtree are electrically coupled to varying degrees, such that branches that are close, particularly sister branches joined at a branch point, show nontrivial levels of subthreshold voltage interaction that leads to a partial breakdown of their functional independence [1], [84], [159]. One interesting possibility is that the sharing of subthreshold voltage signals between nearby subunits is a feature rather than a bug.…”

“…Lacking these controls, it was not possible to decide whether the substantial fraction (33%) of unexplained firing rate variance seen in this study was due to either a fundamental breakdown of the 2LM’s core assumption of subunit independence, versus an inadequate representation of the subunit and somatic nonlinearities. In a recent follow-up study, we found that when the nonlinear dendritic I–O functions are properly characterized in terms of the steady state currents they produce, and the somatic F–I curve is properly taken into account, the two core assumptions of the 2LM—that 1) synaptic integration occurs independently in different subunits, and 2) subunit outputs combine linearly at the soma—are upheld with remarkable accuracy [84]. …”

“…A basic prediction of the 2LM is that the responses to two inputs delivered to the same dendritic branch should combine as if the inputs are summed by an LN subunit with a sigmoidal nonlinearity, whereas the responses to two inputs delivered to two different branches should combine linearly at the soma—as long as the cell remains subthreshold for somatic spiking [95], [96], or the somatic F–I curve happens to be linear [84]. These predictions were confirmed in experiments in brain slices [74], with a caveat discussed in Section VII.…”

“…3(b) and (c)]. Other parameters can influence the I–O curve as well, including the spatial spread of the excitation about its center [61], [67], [92]–[94], [160], and the spine neck resistance [84], [141], [161], both of which alter the sigmoidal nonlinearity. Thus, depending on its center, spread, and other parameters that may depend on location, each excitatory input to a dendrite can choose from a spectrum of different sigmoidal I–O curves—a fact that is irreconcilable with the locationless LN subunit assumption.…”