Model of cellular and network mechanisms for odor-evoked temporal patterning in the locust antennal lobe

Abstract

Locust antennal lobe (AL) projection neurons (PNs) respond to olfactory stimuli with sequences of depolarizing and hyperpolarizing epochs, each lasting hundreds of milliseconds. A computer simulation of an AL network was used to test the hypothesis that slow inhibitory connections between local neurons (LNs) and PNs are responsible for temporal patterning. Activation of slow inhibitory receptors on PNs by the same GABAergic synapses that underlie fast oscillatory synchronization of PNs was sufficient to shape slow response modulations. This slow stimulus- and neuron-specific patterning of AL activity was resistant to blockade of fast inhibition. Fast and slow inhibitory mechanisms at synapses between LNs and PNs can thus form dynamical PN assemblies whose elements synchronize transiently and oscillate collectively, as observed not only in the locust AL, but also in the vertebrate olfactory bulb.

Figure 1. Odor-Specific Slow Temporal Patterns in Locust PNs

Simultaneous intracellular recordings from 3 PNs show odor-specific slow temporal patterns. The odor responses in each PN are shown for two different stimuli (gray bars). (A) PNs membrane potentials. (B) PSTHs corresponding to the spike trains shown in (A), (12 trials).

Figure 2. Network Properties

(A) A simulated network model consisted of 90 PNs and 30 LNs. All interconnections were random with probability 0.5. Forty percent of PNs and 30% of LNs were stimulated by current pulses. Dashed lines illustrate connections to the rest of the network. (B) Fast GABA_A IPSPs (dashed line) and the sum of fast GABA_A and slow GABA IPSPs (solid line) are shown for three different inputs—different numbers of presynaptic spikes.

Figure 3. Oscillation in Response to External Stimulation

(A) Two sequential stimuli each lasting 500 ms were delivered with 2500 ms delay to a randomly selected subset of PNs and LNs (40% and 30% of the total populations, respectively). Field potential oscillations (upper panel) and stimulus (lower panel) are shown. (B) Effect of slow inhibition on temporal patterning. Four representative examples of PNs are shown in response to three presentations of two different stimuli. Stimuli (500 ms duration) are marked by horizontal bars. (B1) Stimulus-specific slow temporal structure was found in the network with intact slow inhibition. (B2) Blocking the slow inhibitory receptors eliminated temporal patterning.

Figure 4. Disinhibition in LN-PN Network

(A) Four representative PNs from intact (upper panels) and disinhibited (lower panels) networks. In disinhibited network, peak conductance for GABA_A synapses was reduced by a factor of 50. It was not completely eliminated because it is likely that, in physiological experiments with PCT, a small fraction of the fast inhibitory channels remained unaffected. Slow inhibition was preserved in both models. (B) PSTHs (four trials) for the same neurons from intact (upper panels) and disinhibited (lower panels) networks. The slow temporal structure was remained intact after fast inhibitory receptors were blocked. (C) The maximal conductance for slow inhibitory receptors was gradually decreased from 100% to zero in intact (left) and disinhibited (right) networks. When both fast and slow inhibitions were blocked (right, bottom), PNs continued to fire even between stimuli. Top trace, LFP, and second trace down, PN.

Figure 5. Effect of Kinetic Parameters and Spatial Density of Slow Inhibitory Receptors

(A) Increase (left) or decrease (right) in decay time constant for slow inhibition significantly decrease or increase, respectively, the network response. Insets show slow IPSPs in response to 2 presynaptic spikes before modification (dashed line) and after modification (solid line). (B) Increase (left) or decrease (right) of the density of slow GABAergic interconnections led to relatively small changes in PN activity. In 2D panels, y axis represents PN label (1–90).

Figure 6. Temporal Structure of PN Activity during Different Stimuli

PSTHs for 4 representative PNs during five stimulus presentations are shown. (A) The same two stimuli as in Figure 3B are shown—identical PNs but different LNs were stimulated. (B) Spatially overlapping stimuli: 50% overlap between stimuli 3 and 4 and 90% overlap between stimuli 3 and 5. (C) Total number of Ca²⁺ spikes in all presynaptic LNs versus number of spikes in their postsynaptic PN at each cycle of population oscillations. Different symbols indicate results for different PNs. (D) Field potential (top) and 3 different PNs are shown in response to six different stimuli. First six cycles of network oscillations following stimulus onset are presented.

Figure 7. Temporal Structure of PN Responses and Stimulus Discrimination

(A) Spatiotemporal dynamics of the PNs during two different stimulus presentations (90% spatial overlap between stimuli). Each plot (9 × 10) corresponds to one cycle of the field potential oscillation (10 cycles are shown for each stimulus). Light color shows the presence of an action potential and characterizes its phase relative to the nearest peak of the field potential (red corresponds to precise synchronization—zero phase shift, light blue indicates a spike occurring precisely between two peaks of the field potential—±π phase shift). Dark blue color indicates silent cells. (B) Misclassification rate calculated for all PNs using cluster algorithm (τ = 200 ms). Intact network (green); maximal conductance for GABA_A synapses reduced by factor of 50 (blue); slow inhibitory receptors blocked (yellow); maximal conductance for GABA_A synapses reduced by factor of 50 and maximal conductance for slow inhibitory receptors reduced by factor of 8 (red). Stimuli with different degrees of spatial overlap (50% and 90%) are shown. Misclassification rate of 0.5 indicates a complete loss of discriminability.