| 1. |
- Bengtsson, Fredrik, et al.
(author)
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Ketamine and xylazine depress sensory-evoked parallel fiber and climbing fiber responses.
- 2007
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In: Journal of Neurophysiology. - : American Physiological Society. - 0022-3077 .- 1522-1598. ; 98:3, s. 705-1697
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Journal article (peer-reviewed)abstract
- Abstract The last few years have seen an increase in the variety of in vivo experiments used for studying cerebellar physiological mechanisms. A combination of ketamine and xylazine has become a particularly popular form of anesthesia. However, because nonanesthetized control conditions are lacking in these experiments, so far there has been no evaluation of the effects of these drugs on the physiological activity in the cerebellar neuronal network. In the present study, we used the mossy fiber, parallel fiber, and climbing fiber field potentials evoked in the nonanesthetized, decerebrated rat to serve as a control condition against which the effects of intravenous drug injections could be compared. All anesthetics were applied at doses required for normal maintenance of anesthesia. We found that ketamine substantially depressed the evoked N3 field potential, which is an indicator of the activity in the parallel fiber synapses (-40%), and nearly completely abolished evoked climbing fiber field potentials (-90%). Xylazine severely depressed the N3 field (-75%) and completely abolished the climbing fiber field (-100%). In a combination commonly used for general anesthesia (20:1), ketamine-xylazine injections also severely depressed the N3 field (-75%) and nearly completely abolished the climbing fiber field (-90%). We also observed that lowered body and surface temperatures (<34 degrees C) resulted in a substantial depression of the N3 field (-50%). These results urge for some caution in the interpretations of studies on cerebellar network physiology performed in animals anesthetized with these drugs
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| 2. |
- Ghazaei, Mahdi, et al.
(author)
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ORF-MOSAIC for Adaptive Control of a Biomimetic Arm
- 2011
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In: IEEE International Conference on Robotics and Biomimetics. - 9781457721373 ; , s. 1273-1278
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Conference paper (peer-reviewed)abstract
- This study is an attempt to take advantage of a cerebellar model to control a biomimetic arm. The cerebellar controller is a modified MOSAIC model which adaptively controls the arm. We call this model ORF-MOSAIC (Organized by Receptive Fields MOdular Selection And Identification for Control). The arm features a musculoskeletal model which is controlled through muscle activations by means of optimization techniques. With as few as 16 modules, we were able to control the arm in a workspace of 30x30 cm. The system was able to adapt to an external field as well as handling new objects despite delays. The discussion section suggests that there are similarities between the microzones in the cerebellum and the modules of this new model.
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| 3. |
- Bengtsson, Fredrik, et al.
(author)
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Integration of sensory quanta in cuneate nucleus neurons in vivo
- 2013
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:2, s. e56630-
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Journal article (peer-reviewed)abstract
- Discriminative touch relies on afferent information carried to the central nervous system by action potentials (spikes) in ensembles of primary afferents bundled in peripheral nerves. These sensory quanta are first processed by the cuneate nucleus before the afferent information is transmitted to brain networks serving specific perceptual and sensorimotor functions. Here we report data on the integration of primary afferent synaptic inputs obtained with in vivo whole cell patch clamp recordings from the neurons of this nucleus. We find that the synaptic integration in individual cuneate neurons is dominated by 4-8 primary afferent inputs with large synaptic weights. In a simulation we show that the arrangement with a low number of primary afferent inputs can maximize transfer over the cuneate nucleus of information encoded in the spatiotemporal patterns of spikes generated when a human fingertip contact objects. Hence, the observed distributions of synaptic weights support high fidelity transfer of signals from ensembles of tactile afferents. Various anatomical estimates suggest that a cuneate neuron may receive hundreds of primary afferents rather than 4-8. Therefore, we discuss the possibility that adaptation of synaptic weight distribution, possibly involving silent synapses, may function to maximize information transfer in somatosensory pathways.
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| 4. |
- Enander, Jonas M.D., et al.
(author)
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A model for self-organization of sensorimotor function : spinal interneuronal integration
- 2022
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In: Journal of Neurophysiology. - : American Physiological Society. - 0022-3077 .- 1522-1598. ; 127:6, s. 1478-1495
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Journal article (peer-reviewed)abstract
- Control of musculoskeletal systems depends on integration of voluntary commands and somatosensory feedback in the complex neural circuits of the spinal cord. It has been suggested that the various connectivity patterns that have been identified experimentally may result from the many transcriptional types that have been observed in spinal interneurons. We ask instead whether the muscle-specific details of observed connectivity patterns can arise as a consequence of Hebbian adaptation during early development, rather than being genetically ordained. We constructed an anatomically simplified model musculoskeletal system with realistic muscles and sensors and connected it to a recurrent, random neuronal network consisting of both excitatory and inhibitory neurons endowed with Hebbian learning rules. We then generated a wide set of randomized muscle twitches typical of those described during fetal development and allowed the network to learn. Multiple simulations consistently resulted in diverse and stable patterns of activity and connectivity that included subsets of the interneurons that were similar to “archetypical” interneurons described in the literature. We also found that such learning led to an increased degree of cooperativity between interneurons when performing larger limb movements on which it had not been trained. Hebbian learning gives rise to rich sets of diverse interneurons whose connectivity reflects the mechanical properties of the system. At least some of the transcriptomic diversity may reflect the effects of this process rather than the cause of the connectivity. Such a learning process seems better suited to respond to the musculoskeletal mutations that underlie the evolution of new species. NEW & NOTEWORTHY We present a model of a self-organizing early spinal cord circuitry, which is attached to a biologically realistic sensorized musculoskeletal system. Without any a priori-defined connectivity or organization, learning induced by spontaneous, fetal-like motor activity results in the emergence of a well-functioning spinal interneuronal circuit whose connectivity patterns resemble in many respects those observed in the adult mammalian spinal cord. Hence, our result questions the importance of genetically controlled wiring for spinal cord function.
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| 5. |
- Jörntell, Henrik, et al.
(author)
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Mathematical Modeling of Brain Circuitry during Cerebellar Movement Control
- 2017
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In: Biologically Inspired : Robotics - Robotics. - : CRC Press. - 9781439854884 - 9781439854976 ; , s. 263-276
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Book chapter (peer-reviewed)abstract
- Reconstruction of movement control properties of the brain could result in many potential advantages for application in robotics. However, a hampering factor so far has been the lack of knowledge of the structure and function of brain circuitry in vivo during movement control. Much more detailed information has recently become available for the area of the cerebellum that controls arm-hand movements. In addition to previously obtained extensive background knowledge of the overall connectivity of the controlling neuronal network, recent studies have provided detailed characterizations of local microcircuitry connectivity and physiology in vivo. In the present study, we study one component of this neuronal network, the cuneate nucleus, and characterize its mathematical properties using system identi cation theory. The cuneate nucleus is involved in the processing of the sensory feedback evoked by movements. As a substrate for our work, we use a characterization of incoming and outgoing signals of individual neurons during sensory activation as well as a recently obtained microcircuitry characterization for this structure. We nd that system identi cation is a useful way to nd suitable mathematical models that capture the properties and transformation capabilities of the neuronal microcircuitry that constitutes the cuneate nucleus. Future work will show whether speci c aspects of the mathematical properties can be ascribed to a speci c microcircuitry and/or neuronal property.
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| 6. |
- Rongala, Udaya B., et al.
(author)
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Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions
- 2020
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In: Neural Networks. - : Elsevier BV. - 0893-6080. ; 123, s. 273-287
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Journal article (peer-reviewed)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.
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| 7. |
- Cenci, M. Angela, et al.
(author)
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On the neuronal circuitry mediating l-DOPA-induced dyskinesia
- 2018
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In: Journal of neural transmission. - : Springer. - 0300-9564 .- 1435-1463. ; 125:8, s. 1157-1169
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Research review (peer-reviewed)abstract
- With the advent of rodent models of l-DOPA-induced dyskinesia (LID), a growing literature has linked molecular changes in the striatum to the development and expression of abnormal involuntary movements. Changes in information processing at the striatal level are assumed to impact on the activity of downstream basal ganglia nuclei, which in turn influence brain-wide networks, but very little is actually known about systems-level mechanisms of dyskinesia. As an aid to approach this topic, we here review the anatomical and physiological organisation of cortico-basal ganglia-thalamocortical circuits, and the changes affecting these circuits in animal models of parkinsonism and LID. We then review recent findings indicating that an abnormal cerebellar compensation plays a causal role in LID, and that structures outside of the classical motor circuits are implicated too. In summarizing the available data, we also propose hypotheses and identify important knowledge gaps worthy of further investigation. In addition to informing novel therapeutic approaches, the study of LID can provide new clues about the interplay between different brain circuits in the control of movement.
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| 8. |
- Norrlid, Johanna, et al.
(author)
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Multi-structure Cortical States Deduced From Intracellular Representations of Fixed Tactile Input Patterns
- 2021
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In: Frontiers in Cellular Neuroscience. - : Frontiers Media SA. - 1662-5102.
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Journal article (peer-reviewed)abstract
- The brain has a never-ending internal activity, whose spatiotemporal evolution interacts with external inputs to constrain their impact on brain activity and thereby how we perceive them. We used reproducible touch-related spatiotemporal sensory inputs and recorded intracellularly from rat (Sprague-Dawley, male) neocortical neurons to characterize this interaction. The synaptic responses, or the summed input of the networks connected to the neuron, varied greatly to repeated presentations of the same tactile input pattern delivered to the tip of digit 2. Surprisingly, however, these responses tended to sort into a set of specific time-evolving response types, unique for each neuron. Further, using a set of eight such tactile input patterns, we found each neuron to exhibit a set of specific response types for each input provided. Response types were not determined by the global cortical state, but instead likely depended on the time-varying state of the specific subnetworks connected to each neuron. The fact that some types of responses recurred indicates that the cortical network had a non-continuous landscape of solutions for these tactile inputs. Therefore, our data suggest that sensory inputs combine with the internal dynamics of the brain networks, thereby causing them to fall into one of the multiple possible perceptual attractor states. The neuron-specific instantiations of response types we observed suggest that the subnetworks connected to each neuron represent different components of those attractor states. Our results indicate that the impact of cortical internal states on external inputs is substantially more richly resolvable than previously shown.
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| 9. |
- Kohler, Matthias, et al.
(author)
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Biological Data Questions the Support of the Self Inhibition Required for Pattern Generation in the Half Center Model
- 2020
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In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 15:9 September
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Journal article (peer-reviewed)abstract
- Locomotion control in mammals has been hypothesized to be governed by a central pattern generator (CPG) located in the circuitry of the spinal cord. The most common model of the CPG is the half center model, where two pools of neurons generate alternating, oscillatory activity. In this model, the pools reciprocally inhibit each other ensuring alternating activity. There is experimental support for reciprocal inhibition. However another crucial part of the half center model is a self inhibitory mechanism which prevents the neurons of each individual pool from infinite firing. Self-inhibition is hence necessary to obtain alternating activity. But critical parts of the experimental bases for the proposed mechanisms for self-inhibition were obtained in vitro, in preparations of juvenile animals. The commonly used adaptation of spike firing does not appear to be present in adult animals in vivo. We therefore modeled several possible self inhibitory mechanisms for locomotor control. Based on currently published data, previously proposed hypotheses of the self inhibitory mechanism, necessary to support the CPG hypothesis, seems to be put into question by functional evaluation tests or by in vivo data. This opens for alternative explanations of how locomotion activity patterns in the adult mammal could be generated.
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| 10. |
- Kohler, Matthias, et al.
(author)
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The Bcm rule allows a spinal cord model to learn rhythmic movements
- 2023
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In: Biological Cybernetics. - 0340-1200. ; 117:4-5, s. 275-284
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Journal article (peer-reviewed)abstract
- Currently, it is accepted that animal locomotion is controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current central pattern generator models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the Bienenstock–Cooper–Munro learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to central pattern generator models, because rhythm generating neurons and genetically defined connectivity are not required in our model. Though our results are not in contradiction to current models, as existing neural mechanism and structures, not used in our model, can be expected to facilitate the kind of learning demonstrated here. Therefore, our model could be used to augment existing models.
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