Fig 1.
Schematic view of the honeybee olfactory system.
(A) Frontal view of morphological connectivity of olfactory pathways. The antennal lobe is the primary site of olfactory processing which receives input from ~60,000 olfactory receptor neurons (ORNs) distributed along the placode sensilla on the antenna. ORNs project to 65 glomeruli that contain 800 excitatory projection neurons (PNs) and 4000 inhibitory local neurons (LNs). Two distinct groups of glomeruli within the antennal lobe (AL) are shown in brown and red spheres specialized for m-PNs and l-PNs. Glomeruli are laterally interconnected by a set of local inhibitory neurons (blue neurons). Axonal PNs extend from the antennal lobe to higher processing centres, such as the mushroom bodies (MB) and Lateral horn (LH) via two tracts, the medial antennal lobe tract (m-ALT, brown) and the lateral antennal lobe tract (l-ALT, red). An octopaminergic neuron (in yellow), VUM-mx1 projects from suboesophageal ganglion (SOG) to three areas of honeybee brain, AL, MB calyces and LH which represents reinforcement signal. Electron micrograph by Axel Brockmann [98]; figure design by Marie Guiraud. (B) The model network of the honeybee lateral antennal lobe tract. The model uses 36 ORNs types (in pink) that are activated by odorants (shown by different shapes; squares, triangles and stars). One ORN responds to multiple ligands of odorants with different sensitivities (One ligand can activate multiple ORNs). ORNs of the same type (i.e., the same sensitivity to ligands) project to PNs and LNs in the same glomerulus. Inhibitory LNs interact with PNs and LNs, both in other glomeruli. More specifically, PNs are disinhibited by the LN-LN connections. Although each glomerulus includes dendrites of several PNs, only one PN and LN are shown for the 3 glomeruli. PNs send axons into LH for connection with a single decision neuron, LHN. The VUM-mx1 neuron modulates inhibitory spike timing-dependent plasticity (iSTDP) of LN-LN and PN-LHN synapses. (C) An artificial odorant stimulus shown as a vector representation. Elements of the vector represent concentration of 36 different ligands. A single odour was modelled by a vector consisting of 2 to 5 active elements because an odour typically contains 2 to 5 ligands. Here, the concentration of each ligand (log[C]) in the odour vector was displayed in colours ranging from blue (lowest concentration) to red (highest concentration).
Fig 2.
Firing rate properties of the stimulated olfactory receptor neurons.
A) Simulated spontaneous and evoked spiking activity of a group of 36 olfactory receptor neuron (ORN) types for 1000 ms. The raster plot exhibits high spontaneous activity before and after the evoked activity of a stimulus (shown below with two active ligands) at times 250 ms and 750 ms. Multiple ORN types are activated by a single ligand. B) Firing rates of 3 different ORNs evoked by the odorant. These exemplary firing rates show a same odour stimulus excites (red) or inhibits (blue) olfactory receptor neurons, and some receptors are insensitive to the odour (green). ORN responses are dynamic and those sensitive ORNs fire most strongly at the stimuli onset. C) Mean and standard error (SE) of the firing rates of three different ORNs across 50 trials are plotted as a function of the ligand concentration which. Blue and red curves show how ligands of an odour suppress and activate the receptor's spike rate below and above the spontaneous activity. Error bars = SE.
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.
Fig 4.
Example of pattern activity of dorsal glomeruli output (response of olfactory projection neurons).
A) Different odorants cause different activation patterns in the dorsal region of the antennal lobes (AL). Each row of matrices exhibits the antennal lobe activity through the non-associative learning for three different odours (A, B and the odour mixture AB). Matrices show the odour representation of PNs in the dorsal region of AL containing 36 projection neurons (PNs). They are arranged in a square with 6 × 6 pixels. The colour of elements (i, j) shows a firing rate of PNi*j. B) Angular distance between PN responses for odour A, odour B, or odour AB are plotted for 50 different simulated bees (mean+- SE). The structured inhibitory connectivity from antennal lobe local neurons to PNs enhances separation between activity patterns for stimuli in the antennal lobe. C) Average activity sparseness for odour representations in the antennal lobes during the training. The low sparseness index corresponds to high sparseness population activity.
Fig 5.
Model performance in differential olfactory conditioning of the proboscis extension reflex.
A) Firing rates of the LHN response to a rewarded odour A (CS+) and unrewarded odour B (CS-) during three stages of the PER task; pre-training, training, and test. The red and blue points show responses of the LHN to the CS+ and CS-, respectively. Synaptic strengths between antennal lobe projection neurons to the LHN are modified only during the training (white area). Conditioning the model with CS+ induces increased firing rate in LHN during training. B) Responses of LHN to both CS+ and CS- before and after the conditioning for two different models, one with the structured connectivity and the other with random connectivity within the antennal lobe. The red and blue bars represent the LHN activity for CS+ and CS-, respectively. Standard error (SE) bars were calculated from the LHN’s firing rate for 50 different odours and different initial parameters in the differential conditioning. Bees were able to learn to discriminate significantly between rewarded stimuli and unrewarded stimuli (p-value < 10−6) while bees with random connectivity between local neurons and projection neurons cannot distinguish the CS+ (p-value = 0.29).
Fig 6.
A) The lateral horn neuron (LHN) responds to rewarded stimuli (CS) and two novel odorants with different level of the similarity to the CS (A’ is more similar to A than A”) after training to CS. LHN’s response to the A’ exhibits more perceptually similar to the CS for bees than to A”. B) The colour matrix shows the olfactory generalization matrix which represents the LHN response to six odours in the tests performed by bees trained with different CSs. Colour pixels (i,j) indicate the firing rate of LHN for the jth odour when the model was trained by the ith odours. The results show asymmetric generalization between odours.
Fig 7.
Non-elemental learning performance.
A) Mean and SE of responses of LHN to unrewarded single odorants (A- or B-) and to a rewarded mixture odours (AB+) during the pre-training (left) and the test (right) of the conditioned PER. Simulated bees learned to discriminate mixture odorant AB from the single odorants A or B (n = 50, t-test; p-value < 0.003). B) Responses of LHN to rewarded mixture odorants (AB+) versus unrewarded components of the CS+ (n = 50, t-test; p-value = 0.23). The model was unable to learn the negative patterning tasks.