DOI: 10.25549/usctheses-c127-666680
title: Two-layer synaptic integration in pyramidal neurons

description: Until recently, the accepted view of synaptic integration in most CNS neurons, including pyramidal neurons of the neocortex, has involved the vague notion that neurons ""sum"" their inputs from across their entire dendritic trees in some simple (e.g. linear) way, and fire action potentials when the axo-somatic spike threshold is crossed. ❧ However, evidence accumulating since the 1990's shows that active mechanisms in dendrites can produce local spikes that do not actively propagate to other parts of the cell, providing an additional 'layer' of nonlinear processing between synaptic inputs and the spike generation stage at the soma. But the recognition that dendrites can themselves act as separately-thresholded integrative subunits brought with it the same vague notion that dendrites also 'sum' their inputs in a simple way before generating local spikes once a local spike threshold is crossed. ❧ This idea of simple summation, though vague, has led to useful abstractions of synaptic integration. In the first instance they led to the point neuron abstraction for a neuron in which all synaptic potentials combine at the soma through a simple mathematical function (e.g. linear summation), and the resulting value then feeds into a global output nonlinearity essentially representing the cell's f-I curve. In the second instance it led to the 2-layer model developed previously in our lab, which extended the point neuron abstraction to two layers: synaptic inputs to a dendrite combine additively to drive a sigmoidal dendritic nonlinearity, and then all the dendritic 'outflows' combine linearly at the soma before feeding into the cell's global output nonlinearity. ❧ The 2-layer abstraction, while providing a more accurate description of a neuron's input-output behavior than the point neuron model, makes two key assumptions: 1) a dendrite's response depends only on the weighted sum of its own synaptic inputs, ignoring any spatial or other interactions between synapses that might exist within a subunit, and 2) dendrites operate independently of one another, that is, the output of one dendrite depends only on its own synaptic inputs, and is invariant to the ongoing activity within all the other dendrites, and the neuron as a whole. ❧ The first assumption raises an important unanswered question: whether integration within a dendrite depends, and if so how, on the absolute and relative locations of its activated synapses. The second assumption of subunit independence is also questionable, given that dendritic responses necessarily depend to some extent on local voltage-sensitive mechanisms, and voltage signals, such as back-propagating action potentials, do enter a dendrite carrying information from other parts of the cell. ❧ We address the first assumption through the use of both simple and complex compartmental models of a neocortical pyramidal neuron, whose results are compared to brain slice data gather in experiments conducted in Jackie Schiller's lab at the Technion in Haifa, Israel. The compartmental modeling experiments explored the effects of varying the absolute and relative locations of excitatory inputs targeting a dendrite, and simple models were used to explain the results. We found that a dendrite is sensitive to the degree of spatial clustering (vs. dispersion) of synaptic inputs driven by any single pathway, and shows asymmetric nonlinear interactions between pathways when more than one input pathway is separated along the proximal-distal axis of a thin dendrite (where proximal-distal refers to distance from the soma). In particular, relative to the baseline sigmoidal input-output curve for a dendrite, we found that distal synapses, when activated, simply lower the threshold of the sigmoidal input-output curve driven by a proximal set of synapses, whereas proximal inputs have a large gain-boosting effect on distally-driven responses. These findings lead us to propose an augmented 2-layer model for the neuron that takes into account these spatial interaction effects. ❧ We address the second assumption about compartmentalization again using a combination of simple and complex models. We find that an abstract, rate-based, 'location-aware' 2-layer model can predict the mean firing rate of a highly detailed compartmental model with remarkable fidelity (R² &gt; 0.999, NRMSE &lt; 1%), suggesting that compartmentalized 2-layer processing is a 'natural' feature of pyramidal neuron dendrites. In an analysis of the conductance, voltage, and current waveforms at various locations inside the cell during stimulation, we discover why the voltage interactions between dendrites referred to above leave functional compartmentalization essentially intact.