Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities
Fig 3
Non-associative plasticity in the antennal lobe and the effect of inhibitory feedback on network decorrelation.
(A) Weight matrices of the synaptic connectivity from 36 antennal lobe local neurons to 36 projection neurons (PNs) in the presence of iSTDP between these connections (From left to right: random weights before training; weights after 500, 1000 and 2000 stimuli presentations). Each column of matrices exhibits strength of an inhibitory antennal lobe local neuron connection to different PNs. The initial connectivity matrix (left) was generated by a random Gaussian distribution, N(0, 10); (see S1 Video). (B) Correlation matrices of PN outputs before and after the exposure to stimuli. Positive and negative correlations are coloured by red and blue respectively. The correlation matrices approaches to a diagonal matrix, indicating that PN activity becomes decorrelated over training. Correlation matrices are calculated from the PNs’ firing rate activated by 64 different stimuli. This comparison shows that correlations between PNs are reduced over different stimulus presentation. C) The entropy reduction that measures the strength of correlations between PNs is plotted as a function of the number of presented odour. The entropy reductions of the PNs' activity of 20 different simulated bees (different initial conditions and a different set of 2000 stimuli) are plotted as a function of the number of stimuli presentation for different values of the global inhibitory neuron (GIN) (Black for strong inhibitory feedback and grey for weak inhibitory feedback). Here low entropy reduction indicates less correlation. The entropy reduces after more odours are presented to the model. Increasing the inhibitory feedback signal from GIN accelerates decorrelation of PN activity.