The representation of the lip in area 2 of the postcentral somatosensory cortex was studied in conscious macaque monkeys by recording single-neurone activities. Seventy penetrations were made in the oral region of six hemispheres of four animals and 1157 neurones were isolated. The receptive field characteristics of 839 neurones were identified. Among them, 363 neurones along 47 penetrations responded to mechanical lip stimulation (lip neurones). A substantial number of lip neurones (17%, 62/363) had composite receptive fields that included not only the lip but also other oral structures. Although, the majority of lip neurones had receptive fields on either the upper or the lower lip (unilabial neurones), about 20% had receptive fields including both the upper and lower lips (bilabial neurones). Receptive field features of bilabial neurones were summarized as follows: (1) the receptive fields always included the corresponding sites of the upper and lower lips that would come into contact when the jaw closed; (2) the submodality preferences of the upper and lower portions of the receptive fields were identical in all cases; (3) if a light stroking stimulus in a specific direction was adequate, portions of the receptive field on the upper and lower lips responded with a common directional preference. Furthermore, bilabial receptive fields were unlikely to be the simple 'dimer' of unilabial receptive fields: the relative incidence of neurones with bilateral or composite receptive fields was much higher in bilabial than in unilabial neurones. That is, bilabial integration was accompanied by the integration of both sides of the lips, and of the lip and other adjacent oral structures. These features of bilabial neurones appear to be suitable for the form discrimination of objects held in the anterior part of the mouth. These neurones may be the prerequisite neural basis for the oral stereognosis that would take place in the neighbouring association cortices.
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http://dx.doi.org/10.1016/s0003-9969(02)00024-9 | DOI Listing |
Neuroimage
January 2025
Division of Arts and Sciences, NYU Shanghai, 567 West Yangsi Road, Pudong New District, 200124, Shanghai, China; Center for Neural Science, New York University, 4 Washington Place, NY, 10003, NY, USA; NYU-ECNU Institute of Brain and Cognitive Science, 3663 Zhongshan Road North, Putuo District, 200062, Shanghai, China. Electronic address:
BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF ("quick PRF"), a system for accelerated PRF modeling that reduced the computation time by a factor ¿1,000 without losing goodness-of-fit when compared to another widely available PRF modeling package (Kay et al.
View Article and Find Full Text PDFFront Comput Neurosci
December 2024
Sussex AI, School of Engineering and Informatics, University of Sussex, Brighton, United Kingdom.
We present a Spiking Neural Network (SNN) model that incorporates learnable synaptic delays through two approaches: per-synapse delay learning via Dilated Convolutions with Learnable Spacings (DCLS) and a dynamic pruning strategy that also serves as a form of delay learning. In the latter approach, the network dynamically selects and prunes connections, optimizing the delays in sparse connectivity settings. We evaluate both approaches on the Raw Heidelberg Digits keyword spotting benchmark using Backpropagation Through Time with surrogate gradients.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer and Control Engineering, Qiqihar University, Qiqihar, 161003, China.
In semantic segmentation research, spatial information and receptive fields are essential. However, currently, most algorithms focus on acquiring semantic information and lose a significant amount of spatial information, leading to a significant decrease in accuracy despite improving real-time inference speed. This paper proposes a new method to address this issue.
View Article and Find Full Text PDFNeural Netw
December 2024
Institute of Automation, Chinese Academy of Sciences, MAIS, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 101408, China.
In the rapidly evolving field of deep learning, Convolutional Neural Networks (CNNs) retain their unique strengths and applicability in processing grid-structured data such as images, despite the surge of Transformer architectures. This paper explores alternatives to the standard convolution, with the objective of augmenting its feature extraction prowess while maintaining a similar parameter count. We propose innovative solutions targeting depthwise separable convolution and standard convolution, culminating in our Multi-scale Progressive Inference Convolution (MPIC).
View Article and Find Full Text PDFPLoS One
January 2025
Julius Kühn-Institut (JKI), Federal Research Centre for Cultivated Plants, Institute for Epidemiology and Pathogen Diagnostics, Rodent Research, Muenster, Germany.
Small rodents can cause problems on farms such as infrastructure damage, crop losses or pathogen transfer. The latter threatens humans and livestock alike. Frequent contacts between wild rodents and livestock favour pathogen transfer and it is therefore important to understand the movement patterns of small mammals in order to develop strategies to prevent damage and health issues.
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