Mario Negrello

Cerebellar output controls generalized spike-and-wave discharge occurrence

Disrupting thalamocortical activity patterns has proven to be a promising approach to stop generalized spike‐and‐wave discharges (GSWDs) characteristic of absence seizures. Here, we investigated to what extent modulation of neuronal firing in cerebellar nuclei (CN), which are anatomically in an advantageous position to disrupt cortical oscillations through their innervation of a wide variety of thalamic nuclei, is effective in controlling absence seizures.

Cerebellar control of gait and interlimb coordination

Synaptic and intrinsic processing in Purkinje cells, interneurons and granule cells of the cerebellar cortex have been shown to underlie various relatively simple, single-joint, reflex types of motor learning, including eyeblink conditioning and adaptation of the vestibulo-ocular reflex. However, to what extent these processes contribute to more complex, multi-joint motor behaviors, such as locomotion performance and adaptation during obstacle crossing, is not well understood. Here, we investigated these functions using the Erasmus Ladder in cell-specific mouse mutant lines that suffer from impaired Purkinje cell output (Pcd), Purkinje cell potentiation (L7-Pp2b), molecular layer interneuron output (L7-Δγ2), and granule cell output (α6-Cacna1a). We found that locomotion performance was severely impaired with small steps and long step times in Pcd and L7-Pp2b mice, whereas it was mildly altered in L7-Δγ2 and not significantly affected in α6-Cacna1a mice. Locomotion adaptation triggered by pairing obstacle appearances with preceding tones at fixed time intervals was impaired in all four mouse lines, in that they all showed inaccurate and inconsistent adaptive walking patterns. Furthermore, all mutants exhibited altered front–hind and left–right interlimb coordination during both performance and adaptation, and inconsistent walking stepping patterns while crossing obstacles. Instead, motivation and avoidance behavior were not compromised in any of the mutants during the Erasmus Ladder task. Our findings indicate that cell type-specific abnormalities in cerebellar microcircuitry can translate into pronounced impairments in locomotion performance and adaptation as well as interlimb coordination, highlighting the general role of the cerebellar cortex in spatiotemporal control of complex multi-joint movements.

Cerebellar Potentiation and Learning a Whisker-Based Object Localization Task with a Time Response Window

Whisker-based object localization requires activation and plasticity of somatosensory and motor cortex. These parts of the cerebral cortex receive strong projections from the cerebellum via the thalamus, but it is unclear whether and to what extent cerebellar processing may contribute to such a sensorimotor task. Here, we subjected knock-out mice, which suffer from impaired intrinsic plasticity in their Purkinje cells and long-term potentiation at their parallel fiber-to-Purkinje cell synapses (L7-PP2B), to an object localization task with a time response window (RW). Water-deprived animals had to learn to localize an object with their whiskers, and based upon this location they were trained to lick within a particular period (“go” trial) or refrain from licking (“no-go” trial). L7-PP2B mice were not ataxic and showed proper basic motor performance during whisking and licking, but were severely impaired in learning this task compared with wild-type littermates. Significantly fewer L7-PP2B mice were able to learn the task at long RWs. Those L7-PP2B mice that eventually learned the task made unstable progress, were significantly slower in learning, and showed deficiencies in temporal tuning. These differences became greater as the RW became narrower. Trained wild-type mice, but not L7-PP2B mice, showed a net increase in simple spikes and complex spikes of their Purkinje cells during the task. We conclude that cerebellar processing, and potentiation in particular, can contribute to learning a whisker-based object localization task when timing is relevant. This study points toward a relevant role of cerebellum–cerebrum interaction in a sophisticated cognitive task requiring strict temporal processing.

Multiplexed coding by cerebellar Purkinje neurons.

Purkinje cells (PC), the sole output neurons of the cerebellar cortex, encode sensorimotor information, but how they do it remains a matter of debate. Here we show that PCs use a multiplexed spike code. Synchrony/spike time and firing rate encode different information in behaving monkeys during saccadic eye motion tasks. Using the local field potential (LFP) as a probe of local network activity, we found that infrequent pause spikes, which initiated or terminated intermittent pauses in simple spike trains, provide a temporally reliable signal for eye motion onset, with strong phase-coupling to the β/γ band LFP. Concurrently, regularly firing, non-pause spikes were weakly correlated with the LFP, but were crucial to linear encoding of eye movement kinematics by firing rate. Therefore, PC spike trains can simultaneously convey information necessary to achieve precision in both timing and continuous control of motion. DOI: http://dx.doi.org/10.7554/eLife.13810.001

Duration of Purkinje cell complex spikes increases with their firing frequency

Climbing fiber (CF) triggered complex spikes (CS) are massive depolarization bursts in the cerebellar Purkinje cell (PC), showing several high frequency spikelet components (±600 Hz). Since its early observations, the CS is known to vary in shape. In this study we describe CS waveforms, extracellularly recorded in awake primates (Macaca mulatta) performing saccades. Every PC analyzed showed a range of CS shapes with profoundly different duration and number of spikelets. The initial part of the CS was rather constant but the later part differed greatly, with a pronounced jitter of the last spikelets causing a large variation in total CS duration. Waveforms did not effect the following pause duration in the simple spike (SS) train, nor were SS firing rates predictive of the waveform shapes or vice versa. The waveforms did not differ between experimental conditions nor was there a preferred sequential order of CS shapes throughout the recordings. Instead, part of their variability, the timing jitter of the CS’s last spikelets, strongly correlated with interval length to the preceding CS: shorter CS intervals resulted in later appearance of the last spikelets in the CS burst, and vice versa. A similar phenomenon was observed in rat PCs recorded in vitro upon repeated extracellular stimulation of CFs at different frequencies in slice experiments. All together these results strongly suggest that the variability in the timing of the last spikelet is due to CS frequency dependent changes in PC excitability.

Modulation of Electrotonic Coupling in the Inferior Olive by Inhibitory and Excitatory Inputs: Integration in the Glomerulus

Dendritic spines in glomeruli of the inferior olive are coupled by gap junctions and receive both inhibitory and excitatory inputs. In this issue of Neuron, Lefler et al. (2014), Mathy et al. (2014), and Turecek et al. (2014) provide new insight into how these inputs modulate electrical coupling and oscillatory activity.

Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer

The granular layer, which mainly consists of granule and Golgi cells, is the first stage of the cerebellar cortex and processes spatiotemporal information transmitted by mossy fiber inputs with a wide variety of firing patterns. To study its dynamics at multiple time scales in response to inputs approximating real spatiotemporal patterns, we constructed a large-scale 3D network model of the granular layer. Patterned mossy fiber activity induces rhythmic Golgi cell activity that is synchronized by shared parallel fiber input and by gap junctions. This leads to long distance synchrony of Golgi cells along the transverse axis, powerfully regulating granule cell firing by imposing inhibition during a specific time window. The essential network mechanisms, including tunable Golgi cell oscillations, on-beam inhibition and NMDA receptors causing first winner keeps winning of granule cells, illustrate how fundamental properties of the granule layer operate in tandem to produce (1) well timed and spatially bound output, (2) a wide dynamic range of granule cell firing and (3) transient and coherent gating oscillations. These results substantially enrich our understanding of granule cell layer processing, which seems to promote spatial group selection of granule cell activity as a function of timing of mossy fiber input.

Boundary representation of neural architecture and connectivity

The general objective of this work is to develop a description language for constructive 3D boundary representation [5] of neuroanatomical structures and connectivity at various levels of granularity (from coarse-resolution solids to fine meshes). This approach is motivated by a desire to capture regularities in neural circuitry as revealed by neuro-architectonic studies [4], while at the same time to explore hypotheses about anatomic variability of various origins, such as experimental uncertainties, cross species scaling factors, individual differences, and assess them for their impact on connectivity, and ultimately on network dynamics. Boundary representation is a general approach to describe 3D objects solely by their surface. Boundary representations consist of topological objects and their concrete geometrical representations in terms of enclosing boundaries. Topological elements include vertices, edges and faces, with corresponding geometrical elements being points, curves, and surfaces. The relationships between topological elements in a structure are expressed by means of a graph of topological connections. The description language is being implemented on top of the GNU Triangulated Surface library [3], and provides the ability to: • Specify compound topological objects with parametric geometry; • Specify geometric parameters for the instantiation of topological objects, such as coordinates for placement, or probability distributions for random placement of a group of identical objects; • Specify individual coordinate systems for different cell populations; • Define categories of topological objects, such as stellate, basket and Golgi cells, which may be part of a morphological continuum; • Define rules for connectivity between different categories of objects. We present the anisotropic cerebellar circuitry as a case study, and define boundary representations of the arrangement of Purkinje and Golgi cells in the cerebellar cortex. We use the hexagonal grid pattern suggested by Palkovits et al. in [1,2], while allowing for small variability in the placement of cells within a hexagon. The implementation of the language instantiates the topological objects, computes the intersections of the resulting surfaces, and given connectivity rules for the different categories of objects, computes the potential synaptic connectivity (producing graph theoretical measures, as well as connectivity histograms), and ultimately aims to generate connectivity descriptions for the NEURON simulator.

Valentino Braitenberg: From neuroanatomy to behavior and back

This article compiles an expose of Valentino Braitenberg’s singular view on neuroanatomy and neuroscience. The review emphasizes his topologically informed work on neuroanatomy and his dialectics of brain-based explanations of motor behavior. Some of his early ideas on topologically informed neuroanatomy are presented, together with some of his more obscure work on the taxonomy of neural fiber bundles and synaptic arborizations. His functionally informed interpretations of neuroanatomy of the cerebellum, cortex, and hippocampus, are introduced. Finally, we will touch on his philosophical views and the inextricable role of function in the explanation of neural behavior.

Exploring the functional implications of brain architecture and connectivity: a multi-simulator framework for biophysical neuronal models

We introduce a framework for implementing networks of neuronal models with conductance-based mechanisms and morphology (where applicable) across multiple simulators. The framework extends the existing NINEML language [1] by adding two independent modules, NINEML-Conductance and NINEML-BREP [2], which allow the specification of conductance-based mechanisms and geometrically derived connectivity respectively. The PyNN API [3] is utilised to reproduce connectivity across multiple simulators, with adapters added where necessary to accommodate the proposed extensions to NINEML. PyNN was chosen to handle the multi-simulator connectivity because it offers translations to a wide range of neural simulators and provides a standardised Python interface for simulation control. It is also straightforward to load predefined connectivity into the PyNN-Connector API from a sparse-matrix-like format, allowing a general interface to NINEML-BREP. Neuronal mechanisms are precompiled into simulator-dependent formats from the NINEML-Conductance declaration, and are then integrated into PyNN via a novel “conductance standard model” class. Depending on whether the selected simulator supports multi-compartment neuronal models, cell morphology is optionally loaded from the NINEML-BREP description and incorporated into the conductance standard model, with flags set in the declarative model description to handle the required adjustments to mechanism parameters. By the meeting we aim to have completed the extensions to the NINEML language and the required interface between the extended NINEML language and PyNN for the NEURON [4] and NEST [5] simulators, and have a working network model of the cerebellar cortex within this framework. This will enable us to test the effect of varying the biophysical detail of neuronal models and different simulators on the proposed cerebellar cortex model. Figure 1