Dendritic Ventriloquism: Inhibitory Synapses Throw Their Voices

In a theoretical study in this issue of Neuron, Gidon and Segev (2012) identify several new principles governing how inhibition interacts with excitation in active dendrites. They show that inhibitory synapses can interact with excitability at a distance, effectively “throwing their voices” in the dendritic tree, such that distributed inhibitory synapses can act synergistically to provide a global veto of dendritic excitability

Structured Connectivity in Cerebellar Inhibitory Networks

SummaryDefining the rules governing synaptic connectivity is key to formulating theories of neural circuit function. Interneurons can be connected by both electrical and chemical synapses, but the organization and interaction of these two complementary microcircuits is unknown. By recording from multiple molecular layer interneurons in the cerebellar cortex, we reveal specific, nonrandom connectivity patterns in both GABAergic chemical and electrical interneuron networks. Both networks contain clustered motifs and show specific overlap between them. Chemical connections exhibit a preference for transitive patterns, such as feedforward triplet motifs. This structured connectivity is supported by a characteristic spatial organization: transitivity of chemical connectivity is directed vertically in the sagittal plane, and electrical synapses appear strictly confined to the sagittal plane. The specific, highly structured connectivity rules suggest that these motifs are essential for the function of the cerebellar network

New biophysical methods for the characterization of signal transfer in neurons

Many neurons have extensive dendritic trees, and therefore somatic voltage clamp of dendritic synapses is often associated with substantial distortion and attenuation of the synaptic currents. A new method is presented which permits faithful extraction of the decay time constant of the synaptic conductance independent of dendritic geometry and the electrotonic location of the synapse. The decay time course of the synaptic conductance was recovered with high accuracy in all the tested geometries, even with high series resistances, low membrane resistances, and electrotonically remote, distributed synapses. The method also provides the time course of the voltage change at the synapse in response to a somatic voltage clamp step, and thus will be useful for constraining compartmental models and estimating the relative electrotonic distance of synapses. Action potential propagation in dendrites links information processing in different regions of the dendritic tree. In simulations using compartmental models with identical complements of voltage-gated channels, different dendritic branching patterns caused a range of backpropagation efficacies, similar to that observed experimentally. Dendritic geometry also determines the extent to which modulation of channel densities can affect propagation. Forward propagation of dendritically initiated action potentials is influenced by geometry in a similar manner. By determining the spatial pattern of action potential signalling, dendritic geometry thus helps to define the size and interdependence of functional compartments in the neuron

The quantitative single-neuron modeling competition

As large-scale, detailed network modeling projects are flourishing in the field of computational neuroscience, it is more and more important to design single neuron models that not only capture qualitative features of real neurons but are quantitatively accurate in silico representations of those. Recent years have seen substantial effort being put in the development of algorithms for the systematic evaluation and optimization of neuron models with respect to electrophysiological data. It is however difficult to compare these methods because of the lack of appropriate benchmark tests. Here, we describe one such effort of providing the community with a standardized set of tests to quantify the performances of single neuron models. Our effort takes the form of a yearly challenge similar to the ones which have been present in the machine learning community for some time. This paper gives an account of the first two challenges which took place in 2007 and 2008 and discusses future directions. The results of the competition suggest that best performance on data obtained from single or double electrode current or conductance injection is achieved by models that combine features of standard leaky integrate-and-fire models with a second variable reflecting adaptation, refractoriness, or a dynamic threshol

The quantitative single-neuron modeling competition

As large-scale, detailed network modeling projects are flourishing in the field of computational neuroscience, it is more and more important to design single neuron models that not only capture qualitative features of real neurons but are quantitatively accurate in silico representations of those. Recent years have seen substantial effort being put in the development of algorithms for the systematic evaluation and optimization of neuron models with respect to electrophysiological data. It is however difficult to compare these methods because of the lack of appropriate benchmark tests. Here, we describe one such effort of providing the community with a standardized set of tests to quantify the performances of single neuron models. Our effort takes the form of a yearly challenge similar to the ones which have been present in the machine learning community for some time. This paper gives an account of the first two challenges which took place in 2007 and 2008 and discusses future directions. The results of the competition suggest that best performance on data obtained from single or double electrode current or conductance injection is achieved by models that combine features of standard leaky integrate-and-fire models with a second variable reflecting adaptation, refractoriness, or a dynamic threshold

Active dendritic integration as a mechanism for robust and precise grid cell firing

Understanding how active dendrites are exploited for behaviorally relevant computations is a fundamental challenge in neuroscience. Grid cells in medial entorhinal cortex are an attractive model system for addressing this question, as the computation they perform is clear: they convert synaptic inputs into spatially modulated, periodic firing. Whether active dendrites contribute to the generation of the dual temporal and rate codes characteristic of grid cell output is unknown. We show that dendrites of medial entorhinal cortex neurons are highly excitable and exhibit a supralinear input–output function in vitro, while in vivo recordings reveal membrane potential signatures consistent with recruitment of active dendritic conductances. By incorporating these nonlinear dynamics into grid cell models, we show that they can sharpen the precision of the temporal code and enhance the robustness of the rate code, thereby supporting a stable, accurate representation of space under varying environmental conditions. Our results suggest that active dendrites may therefore constitute a key cellular mechanism for ensuring reliable spatial navigation

Initiation of simple and complex spikes in cerebellar Purkinje cells

Cerebellar Purkinje cells produce two distinct forms of action potential output: simple and complex spikes. Simple spikes occur spontaneously or are driven by parallel fibre input, while complex spikes are activated by climbing fibre input. Previous studies indicate that both simple and complex spikes originate in the axon of Purkinje cells, but the precise location where they are initiated is unclear. Here we address where in the axon of cerebellar Purkinje cells simple and complex spikes are generated. Using extracellular recording and voltage-sensitive dye imaging in rat and mouse Purkinje cells, we show that both simple and complex spikes are generated in the proximal axon, ∼15–20 μm from the soma. Once initiated, simple and complex spikes propagate both down the axon and back into the soma. The speed of backpropagation into the soma was significantly faster for complex compared to simple spikes, presumably due to charging of the somatodendritic membrane capacitance during the climbing fibre synaptic conductance. In conclusion, we show using two independent methods that the final integration site of simple and complex spikes is in the proximal axon of cerebellar Purkinje cells, at a location corresponding to the distal end of the axon initial segment