Abstract
Dendrites of pyramidal cells exhibit complex morphologies and contain a variety of ionic conductances, which generate non-trivial integrative properties. Basal and proximal apical dendrites have been shown to function as independent computational subunits within a two-layer feedforward processing scheme. The outputs of the subunits are linearly summed and passed through a final non-linearity. It is an open question whether this mathematical abstraction can be applied to apical tuft dendrites as well. Using a detailed compartmental model of CA1 pyramidal neurons and a novel theoretical framework based on iso-response methods, we first show that somatic sub-threshold responses to brief synaptic inputs cannot be described by a two-layer feedforward model. Then, we relax the core assumption of subunit independence and introduce non-linear feedback from the output layer to the subunit inputs. We find that additive feedback alone explains the somatic responses to synaptic inputs to most of the branches in the apical tuft. Individual dendritic branches bidirectionally modulate the thresholds of their input-output curves without significantly changing the gains. In contrast to these findings for precisely timed inputs, we show that neuronal computations based on firing rates can be accurately described by purely feedforward two-layer models. Our findings support the view that dendrites of pyramidal neurons possess non-linear analog processing capabilities that critically depend on the location of synaptic inputs. The iso-response framework proposed in this computational study is highly efficient and could be directly applied to biological neurons.
Highlights
A key objective of theoretical neuroscience is the development of simplified neuron models that incorporate relevant features of neuronal function while abstracting away nonessential complexity
Pyramidal neurons are the principal cell type in the cerebral cortex. Revealing how these cells operate is key to understanding the dynamics and computations of cortical circuits. It is still a matter of debate how pyramidal neurons transform their synaptic inputs into spike outputs
Recent studies have proposed that individual dendritic branches or subtrees may function as independent computational subunits
Summary
A key objective of theoretical neuroscience is the development of simplified neuron models that incorporate relevant features of neuronal function while abstracting away nonessential complexity. According to the classical point-neuron assumption, synaptic inputs to a neuron sum linearly at a single integrative node, the soma, where the resulting membrane potential is transformed non-linearly to generate the neuronal response [1, 2] This simplified description might approximate the dynamics of certain neuron types, it is challenged for pyramidal cells that exhibit complex morphologies and spatially modulated distributions of ion channels (for recent reviews see [3, 4]). These characteristics generate regenerative events, such as Na+ or NMDA (N-methyl-D-aspartate) spikes, which are localized in specific branches or subtrees so that the neuron can no longer be described by a single voltage compartment. Experimental and simulation studies consolidated the two-layer model as a suitable abstraction of synaptic integration in basal and proximal apical dendrites [8,9,10,11]
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