Introduction It has been reported that inhibitory control at the superficial dorsal horn (SDH) can act in a regionally distinct manner, which suggests that regionally specific subpopulations of SDH inhibitory neurons may prevent one specific neuropathic condition. Methods In an attempt to address this issue, we provide an alternative approach by integrating neuroanatomical information provided by different studies to construct a network-model of the SDH. We use Neuroids to simulate each neuron included in that model by adapting available experimental evidence. Results Simulations suggest that the maintenance of the proper level of pain sensitivity may be attributed to lamina II inhibitory neurons and, therefore, hyperalgesia may be elicited by suppression of the inhibitory tone at that lamina. In contrast, lamina III inhibitory neurons are more likely to be responsible for keeping the nociceptive pathway from the mechanoreceptive pathway, so loss of inhibitory control in that region may result in allodynia. The SDH network-model is also able to replicate non-linearities associated to pain processing, such as Aβ-fiber mediated analgesia and frequency-dependent increase of the neural response. Discussion By incorporating biophysical accuracy and newer experimental evidence, the SDH network-model may become a valuable tool for assessing the contribution of specific SDH connectivity patterns to noxious transmission in both physiological and pathological conditions.

El proyecto presenta un sistema modular para la adquisición de señales bioeléctricas proveniente de poblaciones neuronales, el acondicionamiento y registro de la información obtenida así como la evaluación de los resultados empleando métodos de procesamiento de señales. La etapa adquisición parte del diseño y elaboración de electrodos (fabricados con “ITO”: aleación de óxido de indio y óxido de estaño) empleando técnicas de pulverización catódica y evaporación térmica. Se observó que la técnica de pulverización catódica ofreció mejores resultados en cuanto a composición de las películas, transparencia y conductividad eléctrica. La etapa de acondicionamiento y pre - procesamiento de la señal está representada por un amplificador diferencial con filtro de banda pasante, con frecuencia inferior de corte (100 Hz) y corrección de línea base, un filtro Butterworth de cuarto orden de condensador conmutado para establecer la frecuencia superior de corte (2 kHz) y un amplificador de aislamiento eléctrico de alta precisión. Realizando un análisis temporal y espectral del comportamiento del circuito, se determinó que cumplía con los requerimientos estipulados (alto rechazo a modo común, eliminación de efecto triboeléctrico y ancho de banda entre 100 y 2 kHz). La etapa de procesamiento de la información se fundamenta en la descomposición de la misma empleando métodos matemáticos, basados en transformada de ondículas. A través de bancos de filtros pasa - bajo, se pudo determinar que la descomposición de mayor nivel de cada ondícula ofrece información sobre la magnitud de la actividad neuronal, mientras que al aplicar bancos de filtros pasa - alto se resaltaron determinados componentes ruidosos de la señal, permitiendo eliminar dichas perturbaciones.

The project presents a modular system for bioelectric signal acquisition from neuronal populations, signals conditioning and recording, and the assessment of the results using signal processing methods. The acquisition stage of the design consisted on the development of electrodes (made from “ITO”) using sputtering and thermal evaporation techniques. It was observed that the sputtering technique produced better results in terms of composition of the films, transparency and electrical conductivity. The conditioning step and pre-processing of the signal is represented by a differential amplifier with a bandpass filter with low cutoff frequency (100 Hz) and baseline correction, a fourth-order Butterworth filter switched capacitor to set the upper cutoff frequency (2 kHz) and an electrical isolation amplifier with high accuracy. Through a temporal and spectral analysis of the behavior of the circuit, it fulfilled the defined requirements (high common mode rejection, triboelectric effect removal, and bandwidth between 100 and 2 kHz). The processing stage of the information is based on the decomposition of the signal using mathematical methods based on wavelet transform. Using low pass filters banks, it was determined that the highest-level decomposition of each wavelet provides information on the magnitude of neuronal activity while applying high-pass filter banks certain noisy signal components were highlighted allowing the elimination of such disturbances.

INTRODUCTION: Several theories have been proposed to elucidate the mechanisms related with pain perception, among which, the Gate Control Theory (GCT) provides one of the most explicit explanations. This theory, as elegantly conceived, is unable to explain how the Frequency-Intensity (F-I) curves exhibited by Aβ- and C-fibres influence pain processing. In this paper, a novel neuron-model known as the Neuroid, which emphasizes the functional rather the physiological character of nerve cells, was used as the main building block to replicate the Gate Control System (GCS). METHODS: Two Aβ-fibre models were built: one model that preserved the paradoxical relation between the activation threshold and the F-I curve slope, and one model based on the hypothetical average response across the receptive field. RESULTS: The results suggest that the average response of the Aβ-fibres does not increase monotonically but reaches a plateau for high intensity stimuli. In addition, it was seen that activation of C-fibres does not necessarily imply the activation of projection neurons and, therefore, the onset of pain sensation. Also, we observed that the activation of Aβ-fibres may both, decrease and increase the activity of the projections neurons, an aspect which has not been directly described in previous works. CONCLUSION: Hypothetical implications arise as a consequence of the implementation of the Neuroid, specifically, about the correlation between the intensity of stimulation and the physiological pain threshold.