Dendritic spikes control synaptic plasticity and somatic output in cerebellar Purkinje cells.

Neurons receive the vast majority of their input onto their dendrites. Dendrites express a plethora of voltage-gated channels. Regenerative, local events in dendrites and their role in the information transformation in single neurons are, however, poorly understood. This thesis investigates the basic properties and functional roles of dendritic spikes in cerebellar Purkinje cells using whole-cell patch clamp recordings from the dendrites and soma of rat Purkinje cells in brain slices. I show that parallel fibre (PF) evoked dendritic spikes are mediated by calcium channels, depend on membrane potential and stimulus intensity and are highly localized to the spiny branches receiving the synaptic input. A determining factor in the localization and spread of dendritic calcium spikes is the activation of large-conductance, calcium dependent potassium (BK) channels. I provide a strong link between dendritic spikes and the endocannabinoid dependent short-term synaptic plasticity, depolarization-induced suppression of excitation (DSE). Gating the dendritic spikes using stimulus intensity or membrane potential, I show that the threshold of DSE is identical to that of the dendritic spikes and the extent of DSE depends on the number of dendritic spikes. Blocking BK channels increases the spatial spread of dendritic spikes and enables current injection or climbing fibre (CF) evoked dendritic spikes to suppress PF inputs via DSE. By monitoring dendritic spikes during strong PF stimulation-induced long-term depression (LTD), I also provide a link between long-term synaptic plasticity and dendritic excitability. By showing that blocking CB1 cannabinoid receptors reduces the intensity requirement for LTD, I provide a connection between the short- and long-term changes in PF strength triggered by dendritic spikes I also investigate the effect dendritic spikes have on somatic action potential output. Contrary to pyramidal cells, where dendritic spikes boost the output of the neuron, the average Purkinje cell output becomes independent from the output strength for inputs triggering dendritic spikes. However, the temporal pattern of the output is strongly affected by dendritic spikes. I show that this phenomenon depends on BK channel activation resulting in a pause in somatic firing following dendritic spikes. In summary, I present a description of PF evoked local dendritic spikes and demonstrate their functional role in controlling the synaptic input and action potential output of cerebellar Purkinje cells

Dendritic Spikes Veto Inhibition

How inhibition regulates dendritic excitability is critical to an understanding of the way neurons integrate the many thousands of synaptic inputs they receive. In this issue of Neuron, Müller et al. (2012) show that inhibition blocks the generation of weak dendritic spikes, leaving strong dendritic spikes intact

Statistical physics of neural systems with non-additive dendritic coupling

How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such non-additive dendritic processing on single neuron responses and the performance of associative memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality

Branch-specific plasticity enables self-organization of nonlinear computation in single neurons

It has been conjectured that nonlinear processing in dendritic branches endows individual neurons with the capability to perform complex computational operations that are needed in order to solve for example the binding problem. However, it is not clear how single neurons could acquire such functionality in a self-organized manner, since most theoretical studies of synaptic plasticity and learning concentrate on neuron models without nonlinear dendritic properties. In the meantime, a complex picture of information processing with dendritic spikes and a variety of plasticity mechanisms in single neurons has emerged from experiments. In particular, new experimental data on dendritic branch strength potentiation in rat hippocampus have not yet been incorporated into such models. In this article, we investigate how experimentally observed plasticity mechanisms, such as depolarization-dependent STDP and branch-strength potentiation could be integrated to self-organize nonlinear neural computations with dendritic spikes. We provide a mathematical proof that in a simplified setup these plasticity mechanisms induce a competition between dendritic branches, a novel concept in the analysis of single neuron adaptivity. We show via computer simulations that such dendritic competition enables a single neuron to become member of several neuronal ensembles, and to acquire nonlinear computational capabilities, such as for example the capability to bind multiple input features. Hence our results suggest that nonlinear neural computation may self-organize in single neurons through the interaction of local synaptic and dendritic plasticity mechanisms

Dendritic spike induction of postsynaptic cerebellar LTP

The architecture of parallel fiber (PF) axons contacting cerebellar Purkinje neurons (PNs) retains spatial information over long distances. PF synapses can trigger local dendritic calcium spikes, but whether and how this calcium signal leads to plastic changes that decode the PF input organization is unknown. By combining voltage and calcium imaging, we show that PF-elicited calcium signals, mediated by voltage-gated calcium channels, increase non-linearly during high-frequency bursts of electrically constant calcium spikes because they locally and transiently saturate the endogenous buffer. We demonstrate that these non-linear calcium signals, independently of NMDA or metabotropic glutamate receptor activation, can induce PF long-term potentiation (LTP). Two-photon imaging in coronal slices revealed that calcium signals inducing LTP can be observed by stimulating either the PF or the ascending fiber pathway. We propose that local dendritic calcium spikes, evoked by synaptic potentials, provide a unique mechanism to spatially decode PF signals into cerebellar circuitry changes

Reading out a spatiotemporal population code by imaging neighbouring parallel fibre axons in vivo.

The spatiotemporal pattern of synaptic inputs to the dendritic tree is crucial for synaptic integration and plasticity. However, it is not known if input patterns driven by sensory stimuli are structured or random. Here we investigate the spatial patterning of synaptic inputs by directly monitoring presynaptic activity in the intact mouse brain on the micron scale. Using in vivo calcium imaging of multiple neighbouring cerebellar parallel fibre axons, we find evidence for clustered patterns of axonal activity during sensory processing. The clustered parallel fibre input we observe is ideally suited for driving dendritic spikes, postsynaptic calcium signalling, and synaptic plasticity in downstream Purkinje cells, and is thus likely to be a major feature of cerebellar function during sensory processing

Dendritic integration of synaptic inputs in the stellate cells of the medial entorhinal cortex

Grid cells fire action potentials at regular intervals in space, giving rise to a spectacularly regular and stable hexagonal arrangement of firing fields (Hafting et al., 2005). For this reason they have been proposed to represent a neural code for path integration (McNaughton et al., 2006). Grid cells have primarily been found in layer II of the medial entorhinal cortex (MEC) (Hafting et al., 2005). In this thesis I explore the dendritic properties of putative grid cells in MEC layer II and how they may contribute to generating the grid cell firing pattern. To assess the spatial and temporal dynamics of dendritic integration I have used patterned two-photon glutamate uncaging in vitro in combination with somatic whole cell recordings. My findings suggest that the principal neurons of MEC are highly excitable, exhibiting supralinear summation of near-simultaneous inputs and fast and slow dendritic spikes. Supralinear summation is timing-dependent and inputs are summated in a linear manner if separated by 8 ms time intervals. In order to understand the biophysical mechanisms of supralinear summation I blocked NMDA receptors and voltage-gated sodium channels (VGSCs) with D-AP5 and TTX respectively. Both supralinearity and dendritic spikes were abolished in the presence of both blockers, while TTX alone reduced supralinearity and abolished fast but not slow dendritic spikes. This suggests that fast dendritic spikes are largely mediated by VGSCs and slow dendritic spikes by NMDA receptors. Furthermore, I have assessed dendritic integration in physiologically relevant conditions by injecting current waveform to produce in vivo-like membrane potential dynamics, recorded when an animal was crossing a firing field of a MEC II principal neuron in a virtual environment (Schmidt-Hieber & Häusser, 2013). In vivo-like membrane potential dynamics increased supralinearity of the integral of EPSPs and probability of dendritic spikes. These findings have been integrated in a continuous attractor network model of grid cell firing by Christoph Schmidt-Hieber, to assess their relevance for the grid cell rate and temporal code, that revealed that supralinear dendritic integration increases grid cell rate code robustness and fast dendritic sodium spikes increase the precision of the temporal code (phase precession) of grid cells. To conclude, in this thesis I demonstrated that dendrites of principal neurons of MEC layer II integrate synaptic inputs in a highly supralinear manner, mediated by the VGSCs and NMDARs and boosted by putative dendritic spikes. Both supralinearity and proportion of dendritic spikes are increased under in vivo-like membrane potential dynamics. These findings suggest the hypothesis for the intracellular mechanisms that mediate the robustness of grid cell firing

Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields

A fundamental question in understanding neuronal computations is how dendritic events influence the output of the neuron. Different forms of integration of neighbouring and distributed synaptic inputs, isolated dendritic spikes and local regulation of synaptic efficacy suggest that individual dendritic branches may function as independent computational subunits. In the present paper, we study how these local computations influence the output of the neuron. Using a simple cascade model, we demonstrate that triggering somatic firing by a relatively small dendritic branch requires the amplification of local events by dendritic spiking and synaptic plasticity. The moderately branching dendritic tree of granule cells seems optimal for this computation since larger dendritic trees favor local plasticity by isolating dendritic compartments, while reliable detection of individual dendritic spikes in the soma requires a low branch number. Finally, we demonstrate that these parallel dendritic computations could contribute to the generation of multiple independent place fields of hippocampal granule cells

Location-Dependent Effects of Inhibition on Local Spiking in Pyramidal Neuron Dendrites

Cortical computations are critically dependent on interactions between pyramidal neurons (PNs) and a menagerie of inhibitory interneuron types. A key feature distinguishing interneuron types is the spatial distribution of their synaptic contacts onto PNs, but the location-dependent effects of inhibition are mostly unknown, especially under conditions involving active dendritic responses. We studied the effect of somatic vs. dendritic inhibition on local spike generation in basal dendrites of layer 5 PNs both in neocortical slices and in simple and detailed compartmental models, with equivalent results: somatic inhibition divisively suppressed the amplitude of dendritic spikes recorded at the soma while minimally affecting dendritic spike thresholds. In contrast, distal dendritic inhibition raised dendritic spike thresholds while minimally affecting their amplitudes. On-the-path dendritic inhibition modulated both the gain and threshold of dendritic spikes depending on its distance from the spike initiation zone. Our findings suggest that cortical circuits could assign different mixtures of gain vs. threshold inhibition to different neural pathways, and thus tailor their local computations, by managing their relative activation of soma- vs. dendrite-targeting interneurons