Function of Cerebellar Microcircuitry within a Closed-loop System during Control and Adaptation

The human motor control is both robust and stable, despite large delays and highly complex motor systems with an abundance of actuators, sensors and degrees of freedom. The cerebellum is thought help accomplish this by compensating for external loads and internal limitations and disturbances through adaptation, creating inverse models of the motor system dynamics. The cerebellum does also exhibit a generic relatively well described modular microcircuitry, making it a suitable neural circuitry to study. This thesis models a small part of the cerebellum, using detailed bio-physical models in combination with rate-based models, and uses the constructed network model to improve control of a planar double joint arm.The individual neuron models were calibrated using data from in vivo experiments. The response from the models when they were introduced to recorded primary afferent spike trains, originating from tactile stimulation, was used to validate their behaviour. Subsets of the complete network was also constructed to investigate possible functions of the granule cells and inhibitory connection patterns between interneurons within the molecular layer

Spike generation estimated from stationary spike trains in a variety of neurons in vivo

To any model of brain function, the variability of neuronal spike firing is a problem that needs to be taken into account. Whereas the synaptic integration can be described in terms of the original Hodgkin-Huxley (H-H) formulations of conductance-based electrical signaling, the transformation of the resulting membrane potential into patterns of spike output is subjected to stochasticity that may not be captured with standard single neuron H-H models. The dynamics of the spike output is dependent on the normal background synaptic noise present in vivo, but the neuronal spike firing variability in vivo is not well studied. In the present study, we made long-term whole cell patch clamp recordings of stationary spike firing states across a range of membrane potentials from a variety of subcortical neurons in the non-anesthetized, decerebrated state in vivo. Based on the data, we formulated a simple, phenomenological model of the properties of the spike generation in each neuron that accurately captured the stationary spike firing statistics across all membrane potentials. The model consists of a parametric relationship between the mean and standard deviation of the inter-spike intervals, where the parameter is linearly related to the injected current over the membrane. This enabled it to generate accurate approximations of spike firing also under inhomogeneous conditions with input that varies over time. The parameters describing the spike firing statistics for different neuron types overlapped extensively, suggesting that the spike generation had similar properties across neurons

cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions

Abstract We implemented a functional neuronal network that was able to learn and discriminate haptic features from biomimetic tactile sensor inputs using a two-layer spiking neuron model and homeostatic synaptic learning mechanism. The first order neuron model was used to emulate biological tactile afferents and the second order neuron model was used to emulate biological cuneate neurons. We have evaluated 10 naturalistic textures using a passive touch protocol, under varying sensing conditions. Tactile sensor data acquired with five textures under five sensing conditions were used for a synaptic learning process, to tune the synaptic weights between tactile afferents and cuneate neurons. Using post-learning synaptic weights, we evaluated the individual and population cuneate neuron responses by decoding across 10 stimuli, under varying sensing conditions. This resulted in a high decoding performance. We further validated the decoding performance across stimuli, irrespective of sensing velocities using a set of 25 cuneate neuron responses. This resulted in a median decoding performance of 96% across the set of cuneate neurons. Being able to learn and perform generalized discrimination across tactile stimuli, makes this functional spiking tactile system effective and suitable for further robotic applications

Ubiquitous Neocortical Decoding of Tactile Input Patterns

Whereas functional localization historically has been a key concept in neuroscience, direct neuronal recordings show that input of a particular modality can be recorded well outside its primary receiving areas in the neocortex. Here, we wanted to explore if such spatially unbounded inputs potentially contain any information about the quality of the input received. We utilized a recently introduced approach to study the neuronal decoding capacity at a high resolution by delivering a set of electrical, highly reproducible spatiotemporal tactile afferent activation patterns to the skin of the contralateral second digit of the forepaw of the anesthetized rat. Surprisingly, we found that neurons in all areas recorded from, across all cortical depths tested, could decode the tactile input patterns, including neurons of the primary visual cortex. Within both somatosensory and visual cortical areas, the combined decoding accuracy of a population of neurons was higher than for the best performing single neuron within the respective area. Such cooperative decoding indicates that not only did individual neurons decode the input, they also did so by generating responses with different temporal profiles compared to other neurons, which suggests that each neuron could have unique contributions to the tactile information processing. These findings suggest that tactile processing in principle could be globally distributed in the neocortex, possibly for comparison with internal expectations and disambiguation processes relying on other modalities

Long-term exposure to transportation noise and risk of incident stroke:A pooled study of nine scandinavian cohorts

BACKGROUND: Transportation noise is increasingly acknowledged as a cardiovascular risk factor, but the evidence base for an association with stroke is sparse. OBJECTIVE: We aimed to investigate the association between transportation noise and stroke incidence in a large Scandinavian population. METHODS: We harmonized and pooled data from nine Scandinavian cohorts (seven Swedish, two Danish), totaling 135,951 participants. We identified residential address history and estimated road, railway, and aircraft noise for all addresses. Information on stroke incidence was acquired through link-age to national patient and mortality registries. We analyzed data using Cox proportional hazards models, including socioeconomic and lifestyle con-founders, and air pollution. RESULTS: During follow-up (median = 19:5 y), 11,056 stroke cases were identified. Road traffic noise (Lden ) was associated with risk of stroke, with a hazard ratio (HR) of 1.06 [95% confidence interval (CI): 1.03, 1.08] per 10-dB higher 5-y mean time-weighted exposure in analyses adjusted for indi-vidual-and area-level socioeconomic covariates. The association was approximately linear and persisted after adjustment for air pollution [particulate matter (PM) with an aerodynamic diameter of ≤2:5 lm (PM2:5 ) and NO2 ]. Stroke was associated with moderate levels of 5-y aircraft noise exposure (40–50 vs. ≤40 dB) (HR = 1:12; 95% CI: 0.99, 1.27), but not with higher exposure (≥50 dB, HR = 0:94; 95% CI: 0.79, 1.11). Railway noise was not associated with stroke. DISCUSSION: In this pooled study, road traffic noise was associated with a higher risk of stroke. This finding supports road traffic noise as an important cardiovascular risk factor that should be included when estimating the burden of disease due to traffic noise. https://doi.org/10.1289/EHP8949

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Cursed complexity. Computational properties of subcortical neuronal microcircuitry in sensorimotor control

One of the big obstacles for understanding the nervous system is its inherent complexity. It poses problems when interpreting both experimental and theoretical studies since we are currently forced to consider only reduced variants of the actual circuitry of the brain. Since there exist problems that do not appear until a system is sufficiently complex, there are no guarantees that the results stemming from such reduced studies can be extrapolated to actually apply to the real brain. The initial part of the thesis investigates the properties of the spinocerebellar circuitry of the nervous system, and its role in motor control. Especially the cerebellum has been shown to play an important role in the coordination of fast movements, such as reaching and pointing. Paper I uses theoretical reasoning based on previously found experimental studies to show that the cerebellar circuitry should not be studied in isolation if the aim is to explore cerebellar function. The inputs provided by the pre-cerebellar circuits in the spinal cord and brain stem can significantly reduce the complexity of the problem that the cerebellar circuitry needs to solve. Papers II, IV and V investigate the properties of the mossy fiber pathways. Both the spinal border cell neurons that ascend the ventral spinocerebellar tract with sensorimotor information related to locomotion and the neurons of the cuneate nucleus that process tactile information are studied using behavioral stimulation, either in vivo (Paper V) or through modeling (Paper IV). The results indicate both that the overall activity of the circuitry provides the cerebellum with an easy to interpret encoding, but the individual neurons can at the same time segregate underlying features and details of the stimulus. This result can be seen as a parallel to the found statistics of spike generation in Paper III. Even though the neurons have complex electrodynamic properties, their average activity, described by their firing statistics is surprisingly similar between neurons with vastly different morphology. Paper VI reviews the theoretical grounds for sparse coding, and compares them to recent experimental findings, both in the cerebellum and the neocortex. While there are beneficial properties of certain sparse codes, the experimental results rather indicate that the circuitry both in the cerebellum and the neocortex do not actively maintain a sparse population code

Processing of Multi-dimensional Sensorimotor Information in the Spinal and Cerebellar Neuronal Circuitry: A New Hypothesis.

Why are sensory signals and motor command signals combined in the neurons of origin of the spinocerebellar pathways and why are the granule cells that receive this input thresholded with respect to their spike output? In this paper, we synthesize a number of findings into a new hypothesis for how the spinocerebellar systems and the cerebellar cortex can interact to support coordination of our multi-segmented limbs and bodies. A central idea is that recombination of the signals available to the spinocerebellar neurons can be used to approximate a wide array of functions including the spatial and temporal dependencies between limb segments, i.e. information that is necessary in order to achieve coordination. We find that random recombination of sensory and motor signals is not a good strategy since, surprisingly, the number of granule cells severely limits the number of recombinations that can be represented within the cerebellum. Instead, we propose that the spinal circuitry provides useful recombinations, which can be described as linear projections through aspects of the multi-dimensional sensorimotor input space. Granule cells, potentially with the aid of differentiated thresholding from Golgi cells, enhance the utility of these projections by allowing the Purkinje cell to establish piecewise-linear approximations of non-linear functions. Our hypothesis provides a novel view on the function of the spinal circuitry and cerebellar granule layer, illustrating how the coordinating functions of the cerebellum can be crucially supported by the recombinations performed by the neurons of the spinocerebellar systems

Questioning the role of sparse coding in the brain.

Coding principles are central to understanding the organization of brain circuitry. Sparse coding offers several advantages, but a near-consensus has developed that it only has beneficial properties, and these are partially unique to sparse coding. We find that these advantages come at the cost of several trade-offs, with the lower capacity for generalization being especially problematic, and the value of sparse coding as a measure and its experimental support are both questionable. Furthermore, silent synapses and inhibitory interneurons can permit learning speed and memory capacity that was previously ascribed to sparse coding only. Combining these properties without exaggerated sparse coding improves the capacity for generalization and facilitates learning of models of a complex and high-dimensional reality

Approximation examples of basic non-linear functions.

<p>(A) Approximated surfaces using a single or two projections (left and middle columns, respectively) compared to the approximated or target function surface (right-hand column) (i.e RMSE = 0). The colors represent the height of the surface ranging from negative values (blue) to positive values (red), comparable to the surface in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002979#pcbi-1002979-g002" target="_blank">Figure 2C</a>. In each row, a different non-linear interaction retrieved from the terms within the inverse dynamics of the planar double joint arm in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002979#pcbi.1002979-Hollerbach1" target="_blank">[79]</a> is used. The illustrated projections had the lowest RMSE of 100 tested projections, each tested projection having a random direction. The actual RMSE values can be found in (B). The approximated surfaces also display the actual projections used as dashed lines above the surfaces. The value of the elbow angle variable, range between and , to capture an entire period of the sin function that is approximated. (B) RMSE of approximations of three two-dimensional non-linear terms in A. The approximations where constructed using random projection directions and a total of 60 GrCs. 100 approximations where constructed for each box. The mean RMSE is shown by the center line of the box, the boxes themselves extend to the 25th and 75th quartile and the whiskers extends to the most extreme RMSE not considered to be outliers, which are instead shown as black crosses. The red markers with an arrow from “raw signal” show the RMSE of approximations using the raw signals as projection directions, i.e. without recombination of inputs and those with an arrow from “in A” show the RMSE of the approximations shown in (A).</p