CNS*2011 workshop, Stockholm, July 27
Dendrite function and wiring: experiments and theory
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speakers
Jaap van Pelt (Vrije Universiteit, Amsterdam) keynote

Tiago Branco (University College London)
Hermann Cuntz (Goethe University, Frankfurt)
Albert Gidon (Hebrew University, Jerusalem)
Anja Matthiä, née Noerenberg (Berlin)
Matthew Nolan (Edinburgh University)
Panayiota Poirazi (Foundation for Research and Technology, Heraklion)
Arnd Roth (University College London)
Nathan Schultheiss (Boston University)
Klaus Stiefel (Okinawa Institute of Science and Technology, Japan)

abstracts

Jaap van Pelt: From growth cones to functional networks - a model-based approach to understanding
The morphology of neuronal branching patterns (axons and dendrites) has at least a two-fold impact on neural function. First, the integration of postsynaptic potentials strongly depends on the details of the branching geometries, and second, the overlap of axonal and dendritic arborizations in 3D space critically determines the connectivity in a network of neurons. It is therefore important to understand (i) how neurons attain their characteristic morphologies, (ii) what impact these morphologies have on signal integration, (iii) how network connectivity depends on the details of axonal and dendritic branching patterns, and (iv) how activity dynamics in neuronal networks depends on network connectivity. Model-based approaches will be discussed in attempts to answer these questions.

Tiago Branco: Dendritic computations in cortical neurons
Cortical pyramidal neurons receive thousands of synaptic inputs, arriving at different dendritic locations with varying degrees of temporal synchrony and in different sequences. In this work we have asked if different regions along single cortical dendrites integrate excitatory inputs in different ways, and if they are sensitive to the sequence of synaptic activation. Applying two-photon glutamate uncaging, calcium imaging and compartmental modeling, we found a gradient of non-linear synaptic integration in basal and apical oblique dendrites. Proximal inputs sum linearly and require precise temporal coincidence for effective summation, whereas distal inputs are amplified with high gain and integrated over broader time windows. The mechanism involves dendritic impedance gradients and non-linear synaptic NMDA receptor activation, and also confers high sensitivity to the temporal input sequence. Pyramidal cell dendrites can thus exhibit different computational strategies, and act processing compartments for detection of synaptic sequences, implementing a fundamental cortical computation.

Hermann Cuntz: A power law for dendritic wiring
How dendrites sample their inputs is a crucial determinant of the wiring of neural circuits. Yet the precise link between dendrite shape and its synaptic contacts remains a mystery. Studying maturing neurons in which the number of synapses increases provides a unique window on this problem. We combined morphological analysis and modelling of newborn neurons in the adult olfactory glomerulus during neurogenesis in vivo. We reveal a power law between synapse number and dendrite length that is consistent with an optimal space-filling of a given volume. Furthermore, we show that this same principle leads to a specific ratio between the number of branch points in the dendritic tree and the number of synapses. This in turn allows us to generalize our power law to dendritic trees for which the synapse locations are unknown, and show that it holds for a wide variety of neuronal dendritic trees.

Albert Gidon: Principles governing the operation of synaptic inhibition in dendrites
Work together with Idan Segev
Synaptic inhibition plays a key role in shaping network dynamics, selecting cell assemblies and controlling precision of learning and more. In the neocortex and hippocampus about 20% of the neurons are inhibitory; their axons typically contact particular dendritic subdomains of their target neuron, where each axon often makes 10-20 synapses. Furthermore, dendrites are covered with a mixture of voltage-dependent membrane conductances that trigger local regenerative currents as well as dendritic spikes and other plasticity-inducing signals. However, traditionally the impact of inhibition has been characterized (both theoretically and experimentally) only at the soma and the axon, neglecting the impact of inhibition locally at the dendrites. Our “dendro-centric” theoretical study fills in this gap; it highlights three new principles for inhibition in branched dendrites: (i) with multiple synaptic contacts inhibition operates globally, spreading hundreds of micrometers away from the inhibitory synapses; (ii) inhibition in central dendritic regions lacking inhibitory synapses may exceed that at the synaptic sites themselves; (iii) distal inhibition effectively dampens more proximal excitable dendritic “hotspots”, thus powerfully controlling the neuron’s output. These results suggest a major reevaluation in our understanding of synaptic inhibition in neurons.

Anja Matthiä: Properties of the dendritic cable in fast-spiking GABAergic interneurons
Fast-spiking, parvalbumin (PV)-expressing basket cells (BCs) play a key role in feedforward and feedback inhibition in cortical neuronal networks. However, the dendritic mechanisms underlying the rapid activation of these inhibitory cells remain unclear.
To obtain a quantitative picture of the generation, propagation, and integration of excitatory synaptic potentials in BCs, detailed passive cable models of these neurons were developed on the basis of electrophysiological and detailed morphological data. Voltage responses to short and long current pulses were recorded in a dual somatic or somatodendritic configuration in the presence of the Ih channel blocker ZD 7288. Cells were filled with biocytin during recording for subsequent morphological analysis. Soma, dendrites, and the axon were completely reconstructed and cable parameters were obtained by direct fitting of the experimentally recorded voltage transients. Combined analysis of short- and long-pulse responses revealed that models with non-uniform specific membrane resistance (Rm) described the experimental data significantly better than uniform models. Interestingly, in comparison hippocampal principal neurons the gradient of dendritic Rm is the opposite with low Rm in the proximal somatodendritic region (Rm = 7.6 ± 1.7 kΩ cm2), intermediate Rm in the distal dendrites (Rm = 74.3 ± 56.1 kΩ cm2), and highest Rm in the axon (Rm = 281.6 ± 108.2 kΩ cm2). In contrast, the specific membrane capacitance and the intracellular resistivity were similar to those in hippocampal principal cells, with Cm = 0.93 ± 0.04 µF cm-2 and Ri = 172.1 ± 18.5 Ω cm (6 fully reconstructed cells, 30 - 34°C).
Further computational analysis revealed that these unique cable properties accelerate the time course of synaptic potentials at the soma in response to fast inputs, while boosting the efficacy of slow distal inputs. These properties will facilitate rapid phasic activation of BCs by proximal inputs from both feedforward and feedback pathways, and efficient tonic activation of the cells by slower distal dendrites mainly from the feedforward pathway in hippocampal microcircuits.

Matthew Nolan: Stochastic gating of dendritic voltage-gated ion channels modifies integration of distributed synaptic input
Voltage-gated ion channels powerfully modify synaptic responses as they propagate from dendrites to the soma. While it is well established that ion channels gate stochastically, most models of synaptic integration are deterministic and the consequences of stochastic ion channel gating for dendritic integration of synaptic input have received relatively little attention. This may in part be because simulation of electrical activity of a typical dendritic neuron, which may express > 10^6 ion channels, is computationally demanding. To begin to address this issue we have developed and validated new computational tools that enable efficient simulation of stochastic ion channel gating in compartmental models with arbitrary neuronal morphology. We find that the functional consequences of stochastic gating of ion channels are likely to depend on dendritic morphology and to differ between neuron types. We show that stochastic gating of dendritic ion channels causes neurons to respond probabilistically to distributed dendritic synaptic input.

Panayiota Poirazi: Dendrites and information processing: insights from compartmental models
The goal of this presentation is to provide a set of predictions generated by detailed compartmental models regarding the ways in which information may be encoded by single cells and/or neural assemblies and the role of dendrites in this process. Towards this goal, I will present modelling studies from our lab that investigate how single pyramidal neurons and small neural networks in different brain regions process incoming signals that are associated with learning and memory. I will first briefly discuss the computational capabilities of individual pyramidal neurons in the hippocampus [1-3] and how these properties may allow a single cell to discriminate between familiar versus novel memories [4]. I will then present biophysical models of prefrontal layer V neurons and small networks that exhibit Up and Down states or sustained activity under realistic synaptic stimulation and discuss their potential role in working memory [5-7].
1. Poirazi, P. Brannon, T. & Mel, B.W. “Arithmetic of Subthreshold Synaptic Summation in a Model CA1 Pyramidal Cell.” Neuron, vol 37, pg. 977-987, March 2003.
2. Poirazi, P. Brannon, T. & Mel, B.W. “Pyramidal Neuron as 2-Layer Neural Network.” Neuron, vol 37, pg. 989-999, March 2003.
3. Poirazi, P. and Mel, B.W. “Impact of Active Dendritic Processing and Structural Plasticity on Learning and Memory.” Neuron, vol 29, pg. 779-796, March 2001.
4. Pissadaki, E.K., Sidiropoulou K., Reczko M., and Poirazi, P. “Encoding of spatio-temporal input characteristics by a single CA1 pyramidal neuron model” PLoS Comp. Biology, 2010 Dec;6(12): e1001038.
5. Sidiropoulou, K. and Poirazi, P. “Persistent activity in a single neuron model: differential properties between regular spiking and intrinsic bursting neuron models” (submitted)
6. Papoutsi, A., Sidiropoulou, K., and Poirazi, P. “Synaptic Versus Intrinsic Modulation of Persistent Activity in a PFC Microcircuit Model.” (manuscript in preparation)
7. Krioneriti, D, Papoutsi, A, Poirazi, P “Mechanisms underlying the emergence of Up and Down states in a model PFC microcircuit” (submitted to CNS 2011).

Michiel Remme: Encoding of sound source location by auditory brainstem cells: role of cell morphology and membrane properties

Arnd Roth: Structure-function relations in dendrites
How does dendritic morphology shape the functional architecture of different types of neurons? Using compartmental models of reconstructed neurons endowed with the same distribution of active conductances we isolate morphology as the only variable. We show that the spread of subthreshold synaptic potentials, the forward- and backpropagation of action potentials in dendrites, the conditions for initiation of local dendritic spikes as well as the interaction of somatic and dendritic action potential initiation sites depend on the dendritic branching pattern of the neuron.

Nathan Schultheiss: Phase response curve analysis of a morphological-reconstructed neuron model reveals distinct somatic and dendritic modes of synaptic integration
Phase response curves (PRCs) are a compact device relating the timing of synaptic inputs within a neuron’s spike cycle to the timing of output spike initiation. Experimental and theoretical investigations of neuronal phase response properties have demonstrated the utility of PRCs for prediction of synchronization phenomena in connected networks of neurons and populations of neurons receiving correlated inputs. Generally, these approaches represent a reductionist perspective of neurons and networks where only the essential dynamics are preserved. In network models, for example, the complexity of individual neurons is typically reduced to a minimal set of dynamic variables and heterogeneity between neurons is sometimes neglected. Experimental estimations of neuronal PRCs are typically limited to somatic recordings and stimulation protocols and are typically carried out in slice preparations which may not fully represent neuronal dynamics under in vivo conditions. Furthermore, connectivity between neurons is often assumed to be very weak such that the effects of individual inputs on spike timing sum linearly. In many cases such reductions of neuronal or network complexity are necessary to allow neuronal PRCs to be analytically related to network dynamics. However, excluding these complexities eliminates higher order interactions between neuronal morphology, spatially distributed active conductances, and complex network connectivity patterns that may be important features of neural computation. In this talk I will describe a nearly opposite approach to PRC analysis wherein, instead of minimizing neuronal complexity, a great degree of physiological realism is incorporated into a single neuron model. The model is then treated as if it were a real neuron for which perfect stimulus control and complete access to all sources of data are available. Dendritic PRCs obtained in this way for the GENESIS model of a globus pallidus (GP) neuron developed in the lab of Dr. Dieter Jaeger, are qualitatively different than somatic PRCs, in that they contain a pronounced negative region for inputs delivered early within the spike cycle. This negative region reflects that dendritic excitation can paradoxically delay spike timing for some inputs, and PRCs containing such a negative region are classified as type II distinguishing them from purely positive PRCs (type I). This talk will focus on the dendritic conductances that generate type II PRCs in the GP model, and I will discuss the implications of ongoing, spatially-distributed stochastic conductance inputs, characteristic of the in vivo synaptic environment, for phase-dependent effects of dendritic inputs on output spike timing.

Klaus Stiefel: An inverse approach for elucidating dendritic function
We outline an inverse approach for investigating dendritic function-structure relationships by optimizing dendritic trees for a-priori chosen computational functions. The inverse approach can be applied in two different ways. First, we can use it as a ‘hypothesis pump’ in which we optimize dendrites for a function of general interest. The optimization yields an artificial dendrite that is subsequently compared to real neurons. This comparison potentially allows us to propose hypotheses about the function of real neurons. In this way, we investigated dendrites that optimally perform input-order detection. Second, we can use it as a ‘function confirmation’ by optimizing dendrites for functions hypothesized to be performed by classes of neurons. If the optimized, artificial, dendrites resemble the dendrites of real neurons the artificial dendrites corroborate the hypothesized function of the real neuron. Moreover, properties of the artificial dendrites can lead to predictions about yet unmeasured properties. In this way, we investigated wide-field motion integration performed by the VS cells of the fly visual system. In outlining the inverse approach and two applications, we also elaborate on the nature of dendritic function. We furthermore discuss the role of optimality in assigning functions to dendrites and point out interesting future directions.