Elsevier

Neurocomputing

Volume 69, Issues 16–18, October 2006, Pages 1946-1953
Neurocomputing

Spatio-temporal information coding in the cuneate nucleus

https://doi.org/10.1016/j.neucom.2005.11.015Get rights and content

Abstract

The dorsal column nuclei, cuneatus and gracilis, receive somesthetic information impinging on projection cells and local inhibitory interneurons. The presence of these interneurons allows spatio-temporal progressive coding of information that can be modeled [E. Sánchez, J. Aguilar, C. Rivadulla, A. Canedo, The role of Glycinergic Interneurons in the Dorsal Column Nuclei, Neurocomputing 58–60 (2004) 1049–1055 [14]] using their known synaptic connections with projection cells [J. Aguilar, C. Rivadulla, C. Soto, A. Canedo, New corticocuneate cellular mechanisms underlying the modulation of cutaneous ascending transmission in anesthetized cats, J. Neurophysiol. 89 (2003) 3328–3339 [1], J. Aguilar, C. Soto, C. Rivadulla, A. Canedo, The lemniscal-cuneate recurrent excitation is suppressed by strychnine and enhancedd by GABAA antagonists in the anesthetized cat, Eur. J. Neurosci. 16 (2002) 1697–1704 [2], J. Mariño, L. Martinez, A. Canedo, Sensorimotor integration at the dorsal column nuclei, NIPS 14 (1999) 231–237 [9]. Here we explore the dependency of the processing time required to complete the progressive coding with regard to cutaneous stimuli varying in size and regularity of the intensity profile.

Introduction

The dorsal middle region of the dorsal column nuclei (DCN) is constituted for two classes of neurons, glutamatergic cells projecting into the contralateral medial lemniscus and local interneurons releasing GABA, glycine or both neurotransmitters [11]. The cat's DCN receive cortical input from the primary somatosensory cortex [6], [10], [12], [18] and primary glutamatergic afferents topographically aligned [4], [7], [8], [13].

Recent studies in the cuneate nucleus (CN) using intracellular as well as extracellular recording combined with microiontophoresis have revealed that: (i) the cuneate neurons projecting to the medial lemniscus present a center-surround antagonism [5], (ii) the internal circuitry of the cutaneous sector of the cat's CN is such that the projecting cells with matched receptive fields monosynaptically activate each other through recurrent collaterals re-entering the nucleus, while inhibiting other projection neurons with different RFs [2], and (iii) the cortico-cuneate cells [1] and primary afferents [16] with matched RFs activate and disinhibit aligned cuneo-lemniscal neurons and inhibit other neighbouring projection neurons with unmatched RFs. The activation at the centre of the RF is produced through NMDA and non-NMDA glutamate receptors, the lateral inhibition is produced through GABAergic interneurons and the disinhibition is mediated by serial glycinergic-GABAergic-projection cells interactions [1], [2], [16].

The above results are the basis to determine the influences over each projecting neuron and were used to develop a computational model for the CN [14]. Both projection neurons and interneurons are represented as MacCulloch-Pits processing units. Concretely, the activity of the processing units representing the projection neurons is under the modulating influence of primary afferent, collateral recurrent and corticocuneate inputs affecting these cells as described above. The different weight values wji model the synaptic interactions among the distinct classes of neurons and are grouped into matrixes whose values allow for adjusting the contribution of each neuronal class to the network representing the CN.

Section snippets

Methods

In this work we explore the behaviour of the computational model proposed by Sánchez et al. [14]. The model (Fig. 1) consists of 40,000 units distributed over four layers representing: (1) afferent GAB interneurons, (2) projection or cuneolemniscal (CL) neurons, (3) GABaergic interneurons, and (4) glycinergic interneurons. CL units show an excitatory centre—inhibitory surround afferent 3×3 RF, as well as recurrent inhibition mediated through GABaergic interneurons. These units have a 7×7

Results

In general, when a stimulus is presented to the network, three main elements in the output are clearly observed: (1) stimulus edge detection through the excitatory centre—inhibitory surround generated by primary afferents, (2) an oscillatory response reaching a stable state and determined by recurrent inhibition, and (3) a progressive coding starting from regions with lower intensity regularity and finishing at those with higher regularity, and that is induced by the inhibitory action of

Discussion

Based on the results, the network behaviour is robust as it performs edge detection and fill-in progressive coding under a variety of presented stimuli. However, the network processing varies depending on the intensity regularity and size of the stimulus. According to the results, this processing seems highly predictable and in some cases can be easily quantified. The explanation of this complex behaviour lies on the network architecture, which was constructed based on experimental data

Juan Navarro was born in Spain in 1953. He graduated in Medicine at the University of Santiago de Compostela in 1980. Nowadays, he is working as Associate Professor of the Department of Physiology at the University of Santiago de Compostela. His research interest is in computational models of somatosensory system.

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Cited by (1)

  • Classification of somatosensory stimuli on the basis of the temporal coding at the cuneate nucleus

    2015, Neurocomputing
    Citation Excerpt :

    The neuron׳s RFs described above are also depicted in the right inset. In a previous work [9], it has been shown that this circuitry produces a spatio-temporal progressive coding that starts signaling regions with lower regularity (higher intensity contrast), and progressively covers regions with an increasing degree of regularity (lower intensity contrast) until the stimulus is filled. In order to visually explore this code evolving over time, a global output variable has been chosen.

Juan Navarro was born in Spain in 1953. He graduated in Medicine at the University of Santiago de Compostela in 1980. Nowadays, he is working as Associate Professor of the Department of Physiology at the University of Santiago de Compostela. His research interest is in computational models of somatosensory system.

Eduardo M. Sánchez Vila was born in Barcelona, Spain in 1970. He received the B.S. degree in physics from the University of Santiago de Compostela in 1993, a MS. degree in Neuroscience at the International University of Andalucía in 1996, and a Ms. degree in Computer Science at the University of Southern California in 2001. He also received the PhD in Physics in 2000 at the University of Santiago de Compostela. Nowadays, he is working as Professor of the Department of Electronics and Computer Science at the University of Santiago de Compostela. His research interest is in computational models of somatosensory and visual system, distributed computing, information coding and complex network theory.

Antonio Canedo is professor of Physiology at the School of Medicine, Universidad de Santiago de Compostela, Spain. He received his PhD from the University of Santiago de Compostela in 1980. His main research interest is to study the mechanisms leading to sensorimotor integration at supraspinal level, using electrophysiological techniques.

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