Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference
Fig 1
Connectivity among KCs and PNs.
(A) Clusters of KCs and their connectivity. The connections shown here are the low-inhibition connections (blue) and high-inhibition (red) between specific KCs and the medium inhibition between members of a cluster (green). The high-inhibition all-to-all connections are not depicted. Each neuron is connected (as an example) to three other neurons outside of its cluster. A line with a single dot at the end means that the connection is one-way only. The neurons in one cluster connect to each other and to elements outside the cluster. Those neurons that receive input from more than one KC in a cluster form the cluster to be activated next. Each KC could connect with a neuron that is not part of the next cluster; these connections are labeled KCn, KCn+1, etc. and arise because KCs can belong to other clusters as well. (B) Conditions for the connectivity matrix in the proposed model and in the [11] model. In the proposed model, all components of the connectivity matrix ρ are negative (inhibition), whereas the [11] model uses both inhibition and excitation. Ck is the k-th cluster, i.e. i ϵ Ck for all neurons in cluster k. (C) Example of connections between KCs and PNs. KCs work as coincidence detectors, being activated only when both the PNs they are connected to are activated. Shown, a population of 6 KCs divided in two clusters. Four PNs are connected to the KCs so that each KC sees half of the PN population. When PNs 1, 2 and 3 are active, cluster 1 is activated. Cluster 2 is activated with PNs 2, 3 and 4.