Rons with additional distant preferred angles [28]. Having said that, the strength from the interactions is much stronger in modality 1, the deprived one particular, reflecting stronger amplification of its direct inputs (Fig 6C). The cross-talk interactions from modality 2 to modality 1 are primarily excitatory, whereas the cross speak interactions from modality 1 to modality 2 are mainly inhibitory (Fig 6B), resembling the behaviour in the simple model (Fig 3D).PLOS Computational Biology | DOI:10.1371/journal.pcbi.1004959 July eight,8 /A Neuronal Network Model of SyneasthesiaFig five. A network model for studying the evolution of synaesthetic mappings. A. Network architecture. The network is composed of two interacting modalities. Every modality receives a two-dimensional input characterized by an angle and also a distance from the origin. This input is mapped into a higher dimensional representation. You will find recurrent connections amongst all of the neurons in the output layer, namely within and in between modalities. For clarity, only a couple of connections are shown. B. Feedforward connections and input distribution. The feedforward connections (red radial lines) are unit vectors with angles equally spaced from 0to 360 They’re fixed all through the learning. The input to every neuron is proportional to the projection in the input around the corresponding unit vector and includes a cosine tuning around the corresponding angle, which represents its preferred feature. For clarity, the figure shows only a few lines, but in the numerical simulations we utilised 71 output neurons in each and every modality. The blue dots depict the input distribution to a single modality. The angles are uniformly distributed as well as the distance from the origin has a Gaussian distribution around a characteristic distance (0.1 in this example), which represents stimulus intensity. doi:ten.1371/journal.pcbi.1004959.gWe also checked the existence PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/20185357 of synesthetic order BLU-554 behavior by straight stimulating one modality and testing the response in the other. Fig 6D shows the response of modality 1 to stimulation of modality two at an angle of 30 A compact representation of your response is provided by the magnitude and angle of the population vector (Methods; [28]). The magnitude of your population vector of modality 1 in response to stimulation of modality 2 at distinctive angles is finite (Fig 6E, red). In contrast, the magnitude on the population vector of modality two in response to stimulation of modality 1 is effectively zero (Fig 6E, blue). The angle of your population vector of modality 1 in response to stimulation of modality 2 shows a clear systematic mapping (Fig 6F). The fact that the mapping is phase-shifted and decreasing will not be essential given that the values are arbitrary, but the fact that there’s a monotonic connection at all isn’t trivial (offered that no such mapping was present inside the input) Fig 7 summarizes the results from five simulations and demonstrates the various scenarios which will lead to the evolution of synaesthesia. The values in the input magnitudes and the level of plasticity seem inside every panel. The first three simulations (Fig 7AC) describe conditionsPLOS Computational Biology | DOI:10.1371/journal.pcbi.1004959 July eight,9 /A Neuronal Network Model of SyneasthesiaFig six. Evolution of synaesthetic mapping. The figure shows final results from a simulation in which the network created synaesthesia where modality 2 would be the inducer and modality 1 would be the concurrent. The input to modality 1 was r1 = 0.two and also the input to modality two was r2 = 2. A. Pat.