Fig 1.
Schematic diagram of the proposed olfactory model.
The model consists of a glomerular layer, mitral and granular layer, and a dissimilarity evaluation module. The model takes the glomerular activity patterns of odorants composing an odor as input, and considers respiration cycles to simulate the glomerular response to odorant mixture. The neural activity in mitral and granular cells is simulated based on the models proposed in a previous study [20, 21]. The dissimilarity evaluation module defines a dissimilarity index E and compares the activity patterns evoked in the mitral layer by different input odorant mixtures.
Fig 2.
Method of generating the model input from measured glomerular activity patterns provided by a web database (http://gara.bio.uci.edu/) (adapted from [5]).
The original image of the glomerular activity pattern (left) is composed of 357 × 197 pixels, and the grayscale of each pixel corresponds to the activity strength. The original image is divided into 1805 lattices, approximately equal to the actual number of glomeruli distributed on the olfactory bulb. The average activity strength is calculated for each pixel and converted into a vector representing an activity pattern.
Fig 3.
Configuration of synapse connections.
The figure represents the connection range of a mitral or a granular unit. A unit at the center of the grey circle is connected to all units within a range of the circle. If the connection range exceeds the limit, it is folded back to the other end, considering the bulbous shape of the olfactory bulb, using Eqs (7)–(12).
Fig 4.
Prediction of perceptual similarity for odors composed of IA, EB, and Ci.
(a) Output from the glomerular layer for odorant input [IA, Ci, EB]. The figure shows the steps taken to generate output for the glomerular layer. The activity strength is represented in grayscale, where whiter pixels correspond to higher activity. The uppermost row shows the glomerular activity pattern for the odorant composing odor [IA, EB, Ci] obtained from Johnson et al. [27–31]. The middle row shows the binarized activity patterns given by adapting Eq (2) after dividing the pixels into 1805 lattices and generalizing the activity strength into the range [0, 1]. The third row shows the output from the glomerular layer, and the bottom row shows that from the mitral layer generated by the procedure described in Section 2. (b) Comparison between discrimination rates of mice obtained from experiments and dissimilarity obtained from simulations (ζm = 4, ζg = 15). The figure compares the dissimilarity index E obtained from the simulation and discrimination rate for each odor. The orange bars denote simulation results and the blue bars represent the experimental results. The error bars added to the experimental results address the standard deviation in 10 mice, and the error bars added to the simulation results correspond to the standard deviation of 20 sets of synapse connection parameters. Orange and blue lines above the bars denote a significant difference of p<0.01 between odor pairs. Orange lines represent multiple comparison results from the simulation, and blue lines represent that of experiments on mice. (c) Scatter plot between discrimination rates of mice obtained from experiments and dissimilarity index E obtained from simulations. The figure shows a scatter plot between the dissimilarity index and discrimination rate. The error bars correspond to those in (b).
Table 1.
Difference between discrimination rates (Experiment) of odor pairs and that between dissimilarity index (Simulation).
Fig 5.
(a) Convergence of the synapse connection strength of H. (b) Changes of activity patterns in mitral layer. The deep red parts are the most activated and blue parts are least activated.
Fig 6.
Parameter set ζm, ζg and prediction accuracy.
Pearson correlations between dissimilarity index E and discrimination rate of the mice obtained from experiments are plotted with different ζg and ζm.