Abstract
By using single neuron level resolution Drosophila antennal lobe local interneuron data, we process a series of statistical analysis to obtain the characteristics of the local neuron network, then to build a virtual artificial neuron network of Drosophila antennal lobe, which is able to be used for certain kinds of computer simulation experiments. Different from some theoretical research that to build totally random artificial networks, by the reason lack of detailed picture of its neural circuit, our research is based on real neuron network data. Since the LN network data resolution is so accurate, reached single cell definition, our statistical analysis is so detailed which enable us to build up virtual artificial networks, that coincide all the features of the real LN data. Our preliminary computer simulation model is able to be used for reproducing many recently published experiments results, such as lateral inhibition (Olsen and Wilson, 2008b; Olsen et al., 2010), contrast enhancement (Laurent, 2002), sparse inhibition (Ghatpande, 2009; Fantana et al.,2008), gain control and divisive normalization (Olsen and Wilson, 2008b; Olsen et al., 2010), and coding modulation (Koulakov et al., 2007). Especially, when talk about odor distinguish, we have revealed something exciting, that is, by the analysis of Euclidean distance, LN tend to process odor information in an interesting way: if the ORN responses are similar, LN tend to make their patterns even more similar; if the ORN response are kind of different, LN tend to enlarge the difference.