Inhibitory Control of Linear and Supralinear Dendritic Excitation in CA1 Pyramidal Neurons

Abstract

The transformation of dendritic excitatory synaptic inputs to axonal action potential output is the fundamental computation performed by all principal neurons. We show that in the hippocampus this transformation is potently controlled by recurrent inhibitory microcircuits. However, excitatory input on highly excitable dendritic branches could resist inhibitory control by generating strong dendritic spikes and trigger precisely timed action potential output. Furthermore, we show that inhibition-sensitive branches can be transformed into inhibition-resistant, strongly spiking branches by intrinsic plasticity of branch excitability. In addition, we demonstrate that the inhibitory control of spatially defined dendritic excitation is strongly regulated by network activity patterns. Our findings suggest that dendritic spikes may serve to transform correlated branch input into reliable and temporally precise output even in the presence of inhibition.

A Unified Dynamic Model for Learning, Replay, and Sharp-Wave/Ripples

“…The ripple-frequency range is determined by experimentally measured characteristics of nonlinear dendrites (Ariav et al, 2003;Müller et al, 2012) and agrees with the experimentally found one. Slow dendritic spikes in the interneurons may also enable or contribute to ripplefrequency oscillations (Chiovini et al, 2014).…”

“…The resulting inhibitory feedback does not hinder the generation of dendritic spikes, but it decreases the probability that a somatic spike is initiated by hyperpolarization of the postsynaptic neurons (cf. Müller et al, 2012). As a consequence, for very large synchronous pulses, the inhibitory feedback overwhelms the excitation, the pulse does not spread further across the network, and the overall activity decays to the level of spon- frac of place cells that spike at least once during the replay events.…”

“…Recurrent pyramidal-pyramidal connections target basal dendrites in CA1 (Deuchars and Thomson, 1996;Klausberger and Somogyi, 2008) and basal and apical dendrites in CA3 (Li et al, 1994;Le Duigou et al, 2014). As required for the model, basal dendrites in CA1 (Ariav et al, 2003;Müller et al, 2012) and basal and apical dendrites in CA3 (Kim et al, 2012;Makara and Magee, 2013) exhibit fast dendritic spikes. We note that these had not been demonstrated in CA3 when the model was originally proposed; rather, it predicted their existence.…”

“…In particular, as discussed in Materials and Methods, the model explains the frequency of the SPW/Rs from anatomical knowledge on hippocampal recurrent excitatory connectivity (Knowles and Schwartzkroin, 1981;Christian and Dudek, 1988;Yang et al, 2014), from experimentally measured conduction delays (Andersen et al, 2000;Meeks and Mennerick, 2007), and from single-neuron properties, from the latency of dendritic spikes and their impact on the somatic membrane potential (Ariav et al, 2003;Kim et al, 2012;Müller et al, 2012;Makara and Magee, 2013). The model explains that ripples in CA3 have lower frequency and are less marked than those in CA1, by the larger average and broader distributed axonal conduction delay in the more globally connected CA3 network.…”

“…Such inhibition suppresses the impact of excitatory inputs that are not or only weakly dendritically amplified (Müller et al, 2012). In contrast, strong dendritic spikes that form the basis of the chosen model, and their triggering of action potentials, are robust against recurrent and feedforward inhibition (Kamondi et al, 1998;Müller et al, 2012). The presence of several dendrites can lead to a further increase in the effect of the supralinearity, because more than one dendrite can generate spikes (Breuer et al, 2014).…”