Thalamus

Kurths, Jürgen; Forutan, Farhad

doi:10.1016/b978-0-12-374236-0.10019-7

Cited by 39 publications

(42 citation statements)

References 339 publications

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“…We propose a potential involvement of MDpl as well, based on reevaluations of the presented figures and the consideration of the difficult distinction between CL and MDpl [ (Hendry et al 1979), please see also Figs. 1, 2 and 3 in Mai and Forutan (2012)]. Retrograde tracing studies in CM/Pf (Royce et al 1991), Pc, and CL (Anderson and DeVito 1987;Niimi et al 1990), as well as MD (Ono and Niimi 1986; Anderson and DeVito 1987) support these observations.…”

Section: Il and MDsupporting

confidence: 62%

“…In primates, such an arrangement was not described (Yamamoto et al 1992;Tanibuchi et al 2009). Further indications suggesting convergence exist when considering the difficult differentiation of MDpl and CL (Jones 2007;Mai and Forutan 2012). In this way, conflicting reports from electrophysiological studies might be explained that found neuronal responses in CL but not MD following stimulations of the fastigial nucleus (Bava et al 1967).…”

Section: MDmentioning

confidence: 90%

“…Indications of nigral projections to this region can be found in rats (Gerfen et al 1982), cats (Hendry et al 1979;Anderson and DeVito 1987;Niimi et al 1990), and dogs (Sakai and Patton 1993), but not in primates. It is possible that missing observations of such projections in primates can be ascribed to the difficult differentiation of CL and MDpl (Jones 2007;Mai and Forutan 2012). Nigral and cerebellar projections to the primate MDpl are well described (see below).…”

Section: Ilmentioning

confidence: 93%

“…However, a number of anterograde tracing studies reported nigral terminals in the adjacent MDpl Ilinsky et al 1985;Percheron et al 1996;Sidib et al 2002). The differentiation of MDpl and CL has been assessed to be very difficult (Jones 2007;Mai and Forutan 2012). Furthermore, cerebellar projections to MDpl were also found (Ilinsky and Kultas-Ilinsky 1987;Yamamoto et al 1992;Sakai et al 1996).…”

Section: Il and MDmentioning

confidence: 99%

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Thalamic interactions of cerebellum and basal ganglia

2017

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Cerebellum and basal ganglia are reciprocally interconnected with the neocortex via oligosynaptic loops. The signal pathways of these loops predominantly converge in motor areas of the frontal cortex and are mainly segregated on subcortical level. Recent evidence, however, indicates subcortical interaction of these systems. We have reviewed literature that addresses the question whether, and to what extent, projections of main output nuclei of basal ganglia (reticular part of the substantia nigra, internal segment of the globus pallidus) and cerebellum (deep cerebellar nuclei) interact with each other in the thalamus. To this end, we compiled data from electrophysiological and anatomical studies in rats, cats, dogs, and non-human primates. Evidence suggests the existence of convergence of thalamic projections originating in basal ganglia and cerebellum, albeit sparse and restricted to certain regions. Four regions come into question to contain converging inputs: (1) lateral parts of medial dorsal nucleus (MD); (2) parts of anterior intralaminar nuclei and centromedian and parafascicular nuclei (CM/Pf); (3) ventromedial nucleus (VM); and (4) border regions of cerebellar and ganglia terminal territories in ventral anterior and ventral lateral nuclei (VA-VL). The amount of convergences was found to exhibit marked interspecies differences. To explain the rather sparse convergences of projection territories and to estimate their physiological relevance, we present two conceivable principles of anatomical organization: (1) a "core-and-shell" organization, in which a central core is exclusive to one projection system, while peripheral shell regions intermingle and occasionally converge with other projection systems and (2) convergences that are characteristic to distinct functional networks. The physiological relevance of these convergences is not yet clear. An oculomotor network proposed in this work is an interesting candidate to examine potential ganglia and cerebellar subcortical interactions.

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Section: Il and MDsupporting

confidence: 62%

Section: MDmentioning

confidence: 90%

Section: Ilmentioning

confidence: 93%

Section: Il and MDmentioning

confidence: 99%

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Thalamic interactions of cerebellum and basal ganglia

2017

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“…The La.m can be clearly identified in the dorsal 3 sections, but it looks faded and weakly contoured in the most ventral section in the right image of Fig 2. The most probable reasons are intralaminar cell clusters and the larger lateromedial width of this lamina at more ventral levels. 23 Only in the section 10 mm dorsal to the ACPC plane does the boundary between the D.o.i and Z.im.i appear as a well-defined edge. In the neighboring sections, gradual signal-intensity transitions, in an anteroposterior direction, of the lateral thalamus are discernible.…”

Section: Resultsmentioning

confidence: 99%

Direct Visualization of Anatomic Subfields within the Superior Aspect of the Human Lateral Thalamus by MRI at 7T

Kanowski¹,

Voges

Buentjen

et al. 2014

American Journal of Neuroradiology

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Emerging Artificial Synaptic Devices for Neuromorphic Computing

Wan

Sharbati

Erickson

et al. 2019

Adv Materials Technologies

218

190

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these machines offer computational capabilities on the peta-flop scale, making the brain a truly extraordinarily efficient device. [1] One of the major causes of this disparity in energy usage is what is referred to as the von Neumann bottleneck. [3] In modern computing systems, the dedicated central processing units (CPUs) are physically separated from the main memory areas. In addition, these CPUs are programmed to execute operations sequentially, where relevant information needs to be shuttled back and forth between the CPU and the memory. [4] This shuttling of bits puts an inherent cap on the speed of computations, as well as drastically increasing the energy usage.For this reason, researchers are motivated to develop neuromorphic computing systems that can rival or even exceed the cognitive capabilities and energy efficiency of the human brain. As biological systems use complicated systems of networks, all of which work together to form the nervous system, [5] it is going to take a similar multidisciplinary effort for neuromorphic computing to evolve to the point of emulating or even surpassing the human brain, with a concerted approach from material scientists, device engineers, circuit designers, and computer architecture engineers, etc. One particularly exciting facet of this grand work is the synapse used in the neural network. These synapses are capable of both storing information and performing complex operations at the same location, allowing networks to carry out computations in a massively parallel framework, reducing the energy cost per operation. [6] In this pursuit, artificial neural networks (ANNs) have been developed and successfully applied in various fields including: image and pattern recognition, [7] speech recognition, [8] machine translation, [9] and beating humans at chess and recently, Go. [10] Despite these recent strides in neuromorphic computing, the hardware implementation of these ANNs have been hampered by the fact that the digital transistors, the basic computing unit of modern computers, do not behave in the same manner as the analog synapses, the basic building block of the biological neural network. In this paper, we will review a number of different approaches currently being investigated that aim to improve the performance of synaptic devices towards the hardware acceleration of ANNs. First, we will discuss phase change memory (PCM) based synaptic devices, followed by three types In today's era of big-data, a new computing paradigm beyond today's von-Neumann architecture is needed to process these large-scale datasets efficiently. Inspired by the brain, which is better at complex tasks than even supercomputers with much better efficiency, the field of neuromorphic computing has recently attracted immense research interest and can have a profound impact in next-generation computing. Unlike modern computers that use digital "0" and "1" for computation, biological neural networks exhibit analog changes in synaptic connections during the decision-making and learning processes....

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Thalamus

Cited by 39 publications

References 339 publications

Thalamic interactions of cerebellum and basal ganglia

Thalamic interactions of cerebellum and basal ganglia

Direct Visualization of Anatomic Subfields within the Superior Aspect of the Human Lateral Thalamus by MRI at 7T

Emerging Artificial Synaptic Devices for Neuromorphic Computing

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