Sensorimotor cortical influences on cuneate nucleus rhythmic activity in the anesthetized cat

Mariño, Jorge; Canedo, Antonio; Aguilar, Juan

doi:10.1016/s0306-4522(99)00414-5

Cited by 37 publications

(39 citation statements)

References 53 publications

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“…The corticocuneate connections were modeled on the basis of data extracted from Mariño et al (1999Mariño et al ( , 2000, and Canedo et al (2000). The corresponding experiments were performed in intact-brain preparations in anaesthetized cats.…”

Section: Modelling the Corticocuneate Circuitrymentioning

confidence: 99%

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Cortical modulation of dorsal column nuclei: A computational study

et al. 2006

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We present a computational study aimed at exploring the sensorimotor cortex modulation of the behaviour of dorsal column nuclei, specifically the impact of synaptic parameters, during both sleep and waking conditions. On the basis of the circuit proposed by Canedo et al. (2000), we have developed realistic computational models that have been tested with simultaneous electrocorticographic as well as intracellular cuneate recordings performed in anaesthetized cats. The results show that, (1) under sleep conditions, the model can block the transmission of afferent sensory information and, (2) operations expected during wakefulness, such as filtering and facilitation, can be performed if synaptic parameters are appropriately tuned.

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Section: Modelling the Corticocuneate Circuitrymentioning

confidence: 99%

“…Mariño et al (2000) observed these rhythms in the DCN after deafferenting the pyramidal tract (which contains corticofugal fibers to DCN), indicating that cuneate neurons may intrinsically generate delta rhythms. Finally, cortical circuits would generate slow rhythms (<1 Hz) and impose them onto subcortical structures (see Fig.…”

Section: Dcn and Sleep Statesmentioning

confidence: 99%

Cortical modulation of dorsal column nuclei: A computational study

et al. 2006

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“…The PAFs establish synaptic contact with projection cells and interneurons [13]. The cortical fibres from the contralateral sensorimotor cortex run in the pyramidal tract [34,39] and, within the middle cuneate, make synaptic contact mostly with inhibitory interneurons located in the shell [8]. On the other hand, the great majority of the projection neurons in the middle cuneate send their axons to the contralateral ventroposterolateral thalamic (VPL) nucleus via the medial lemniscus [3,13,30].…”

Section: Introductionmentioning

confidence: 99%

“…To study the neuronal properties of the middle cuneate, intracellular and patch-clamp techniques have recently been used in vivo [4,5,34,36,37]. It was found that cuneothalamic neurons possess two modes of activity: oscillatory and tonic [5].…”

Section: Introductionmentioning

confidence: 99%

A computational model of cuneothalamic projection neurons

Sánchez

Barro

Mariño

et al. 2003

Network: Computation in Neural Systems

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The dorsal column nuclei, cuneatus and gracilis, play a fundamental role in the processing and integration of somesthetic ascending information. Intracellular and patch-clamp recordings obtained in cat in vivo have shown that cuneothalamic projection neurons present two modes of activity: oscillatory and tonic (Canedo et al 1998 Neuroscience 84 603-17). The former is the basis of generating, in sleep and anaesthetized states, slow, delta and spindle rhythms under the control of the cerebral cortex (Mariño et al 2000 Neuroscience 95 657-73). The latter is needed, during wakefulness, to process somesthetic information in real time. To study this behaviour we have developed the first realistic computational model of the cuneothalamic projection neurons. The modelling was guided by experimental recordings, which suggest the existence of hyperpolarization-activated inward currents, transient low- and high-threshold calcium currents, and calcium-activated potassium currents. The neuronal responses were simulated during (1) sleep, (2) transition from sleep to wakefulness and (3) wakefulness under both excitatory and inhibitory synaptic input. In wakefulness the model predicts a set of synaptically driven firing modes that could be associated with information processing strategies in the middle cuneate nucleus.

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“…Different algorithms like neural networks, logistic regression, and neurofuzzy model can be applied for detection of epileptic seizure [2,6,7] . There are many similarities between human and animal brain's neural coding and many studies used animal modeling for investigation the spike discharge [8][9][10][11] . Johnsen and Levine [1] analyzed as follows.…”

Section: Introductionmentioning

confidence: 99%

Spike discharge prediction based on neuro-fuzzy system

Zarei¹

2017

JUMD

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How to cite this article: Zarei M. Spike discharge prediction based on neuro-fuzzy system. J Unexplored Med Data 2017;2:88-101. This paper presents the development and evaluation of different versions of neuro-fuzzy model for prediction of spike discharge patterns. The author aims to predict the spike discharge variation using first spike latency and frequency-following interval. In order to study the spike discharge dynamics, the author analyzed the cerebral cortex data of the cat. Adaptive neuro-fuzzy inference systems (ANFIS), Wang and Mendel, dynamic evolving neural-fuzzy inference system, hybrid neural fuzzy inference system, genetic for lateral tuning and rule selection of linguistic fuzzy system (GFS.LT.RS) and subtractive clustering and fuzzy c-means algorithms are applied for data. Among these algorithms, ANFIS and GFS.LT.RS models have better performance. On the other hand, ANFIS and GFS.LT.RS algorithms can be used to predict the spike discharge dynamics as a function of first spike latency and frequency with a higher accuracy compared to other algorithms. Key words:Spike discharge, spike latency, spike frequency, neuro-fuzzy system ABSTRACT Article history:

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Sensorimotor cortical influences on cuneate nucleus rhythmic activity in the anesthetized cat

Cited by 37 publications

References 53 publications

Cortical modulation of dorsal column nuclei: A computational study

Cortical modulation of dorsal column nuclei: A computational study

A computational model of cuneothalamic projection neurons

Spike discharge prediction based on neuro-fuzzy system

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