Also found in: Wikipedia.
SNNSStuttgart Neural Network Simulator
SNNSSentinel Node Navigation Surgery
SNNSSwedish Neural Network Society
SNNSSorry No New Swappers
Copyright 1988-2018, All rights reserved.
References in periodicals archive ?
In the same study, Banerjee and Pramanik [118] developed three goal programming models with SNN. The authors provided comparision between the proposed goal propramming strategy and existing strategy in the literature.
In Korea, some surgeons, endoscopists, pathologists, and nuclear radiologists who were interested in SNNS for EGC treatment planned a multicenter phase III trial comparing conventional laparoscopic gastrectomy to laparoscopic SN biopsy with limited gastrectomy in clinical stage Ia gastric cancer patients [8].
For the successful SNNS, high detection rate and low false-negative rate are indispensable in EGC patients.
Finding at least 5 LNs at back table and frozen biopsy is a critical step for determining success or failure in SNNS. A meta-analysis found that sensitivity of SN biopsy in gastric cancer was significantly related to the number of harvested SNs [4].
Although more experience in SBD can reduce failure rate, institutions starting SNNS should be cautious in obese patients following early implementation of SNNS.
Many SNN computer simulation experiments show that distribution of synaptic delays is an equally important factor determining network behavior as distribution of synaptic weights.
Indeed, the primary purpose of this study was to demonstrate how a simple homogenous SNN can convert signal from rate/population coding form to temporal code.
A synergetic neural network (SNN) is used to learn the visual features of a class of objects.
Original SNN used pixel-wised features to represent an object which is not robust in case objects are in a variable shapes (e.g.
Therefore, boundaries of object are segmented out before the result is given to SNN.
Synergetic neural network (SNN) developed by Haken [20] describes the pattern recognition process in the human brain which tries to achieve the learned model with fast learning and no false state rather than traditional neural networks [21, 22] [20].
A valuable feature of SNNS is its ability to automatically generate C code to implement a neural network, through snns2c (a tool included with SNNS distributions).