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A model of stimulus-specific neural assemblies in the insect antennal lobe.

Martinez D, Montejo N - PLoS Comput. Biol. (2008)

Bottom Line: These findings suggest a wiring scheme that triggers stimulus-specific synchronized assemblies.Inhibitory connections are set by Hebbian learning and selectively activated by stimulus patterns to form a spiking associative memory whose storage capacity is comparable to that of classical binary-coded models.We conclude that fast inhibition acts in concert with slow inhibition to reformat the glomerular input into odor-specific synchronized neural assemblies.

View Article: PubMed Central - PubMed

Affiliation: LORIA, Campus Scientifique, Vandoeuvre-lès-Nancy, France. dominique.martinez@loria.fr

ABSTRACT
It has been proposed that synchronized neural assemblies in the antennal lobe of insects encode the identity of olfactory stimuli. In response to an odor, some projection neurons exhibit synchronous firing, phase-locked to the oscillations of the field potential, whereas others do not. Experimental data indicate that neural synchronization and field oscillations are induced by fast GABA(A)-type inhibition, but it remains unclear how desynchronization occurs. We hypothesize that slow inhibition plays a key role in desynchronizing projection neurons. Because synaptic noise is believed to be the dominant factor that limits neuronal reliability, we consider a computational model of the antennal lobe in which a population of oscillatory neurons interact through unreliable GABA(A) and GABA(B) inhibitory synapses. From theoretical analysis and extensive computer simulations, we show that transmission failures at slow GABA(B) synapses make the neural response unpredictable. Depending on the balance between GABA(A) and GABA(B) inputs, particular neurons may either synchronize or desynchronize. These findings suggest a wiring scheme that triggers stimulus-specific synchronized assemblies. Inhibitory connections are set by Hebbian learning and selectively activated by stimulus patterns to form a spiking associative memory whose storage capacity is comparable to that of classical binary-coded models. We conclude that fast inhibition acts in concert with slow inhibition to reformat the glomerular input into odor-specific synchronized neural assemblies.

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Spike timing precision with asynchronous GABA release.The stars represent the spike time jitter σ2 estimated from simulations with asynchronous GABA release. For the simulations, we considered a network of N = 100 neurons coupled all-to-all with fast GABAA synapses (τGABA = 10 ms). Each presynaptic spike triggers 10 post-synaptic events, released asynchronously according to an exponential distribution of variance λ2 (Equation 14 in Methods). The solid line is given by Equation 4.
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pcbi-1000139-g003: Spike timing precision with asynchronous GABA release.The stars represent the spike time jitter σ2 estimated from simulations with asynchronous GABA release. For the simulations, we considered a network of N = 100 neurons coupled all-to-all with fast GABAA synapses (τGABA = 10 ms). Each presynaptic spike triggers 10 post-synaptic events, released asynchronously according to an exponential distribution of variance λ2 (Equation 14 in Methods). The solid line is given by Equation 4.

Mentions: Inhibitory cells may release transmitters synchronously or asynchronously [30],[31]. In the olfactory bulb for example, GABAergic inhibition released by Granule Cells and received by Mitral Cells is asynchronous and variable across repeated trials [32],[33]. What might be the effect of asynchronous GABA release on the spike timing precision? As shown in Text S1 (Equation A-4), the spike time jitter σ2(n) of the PN population at the n-th cycle is(3)where λ is the time constant of the exponential release distribution (Equation 14 in Methods). A high value of λ models the effect of asynchronous inhibition, where synaptic events may be released well after the arrival of an action potential on a synapse. On the contrary, a lower value of λ models the effect of synchronous inhibition. When λ = 0, Equation 3 becomes equivalent to Equation 1. At convergence of Equation 3, we have σ2(n) = σ2(n−1) = σasyn2 and(4)where σ2 is the spike time jitter obtained in the case of synchronous GABA release and is simply given by Equation 2. Asynchronous release accentuates temporal dispersion by adding the extra term λ2/(〈k〉−1). Figure 3 compares the theoretical σasyn2 to the one obtained from simulations for different values of λ2. For the simulations, we considered a network of N = 100 neurons coupled all-to-all with fast GABAA synapses (τGABA = 10 ms, ga = 1 nS, Pfailure = 0.5). For λ = 0 ms (synchronous release), we have σasyn = σ = 1 ms (temporal dispersion obtained with GABAA synapses, see previous section). We observe that σasyn2 increases linearly with λ2, as predicted by Equation 4. From Equation 4, σasyn = 10 ms when λ = 70 ms, which is the same level of temporal dispersion as the one obtained with synchronous release and slow GABAB synapses (τGABA = 100 ms, see previous section). The loss of spike-timing precision is thus achieved with asynchronous release, despite fast GABAA synapses. Actually, the asynchronous synaptic events sum gradually over time so as to produce a resulting inhibition which decays with a time constant approximately equal to λ (when λ is large as shown previously [34]). Asynchronous release can be seen as a way to produce long-lasting inhibition despite the fast decay time of individual events mediated by GABAA receptors.


A model of stimulus-specific neural assemblies in the insect antennal lobe.

Martinez D, Montejo N - PLoS Comput. Biol. (2008)

Spike timing precision with asynchronous GABA release.The stars represent the spike time jitter σ2 estimated from simulations with asynchronous GABA release. For the simulations, we considered a network of N = 100 neurons coupled all-to-all with fast GABAA synapses (τGABA = 10 ms). Each presynaptic spike triggers 10 post-synaptic events, released asynchronously according to an exponential distribution of variance λ2 (Equation 14 in Methods). The solid line is given by Equation 4.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2536510&req=5

pcbi-1000139-g003: Spike timing precision with asynchronous GABA release.The stars represent the spike time jitter σ2 estimated from simulations with asynchronous GABA release. For the simulations, we considered a network of N = 100 neurons coupled all-to-all with fast GABAA synapses (τGABA = 10 ms). Each presynaptic spike triggers 10 post-synaptic events, released asynchronously according to an exponential distribution of variance λ2 (Equation 14 in Methods). The solid line is given by Equation 4.
Mentions: Inhibitory cells may release transmitters synchronously or asynchronously [30],[31]. In the olfactory bulb for example, GABAergic inhibition released by Granule Cells and received by Mitral Cells is asynchronous and variable across repeated trials [32],[33]. What might be the effect of asynchronous GABA release on the spike timing precision? As shown in Text S1 (Equation A-4), the spike time jitter σ2(n) of the PN population at the n-th cycle is(3)where λ is the time constant of the exponential release distribution (Equation 14 in Methods). A high value of λ models the effect of asynchronous inhibition, where synaptic events may be released well after the arrival of an action potential on a synapse. On the contrary, a lower value of λ models the effect of synchronous inhibition. When λ = 0, Equation 3 becomes equivalent to Equation 1. At convergence of Equation 3, we have σ2(n) = σ2(n−1) = σasyn2 and(4)where σ2 is the spike time jitter obtained in the case of synchronous GABA release and is simply given by Equation 2. Asynchronous release accentuates temporal dispersion by adding the extra term λ2/(〈k〉−1). Figure 3 compares the theoretical σasyn2 to the one obtained from simulations for different values of λ2. For the simulations, we considered a network of N = 100 neurons coupled all-to-all with fast GABAA synapses (τGABA = 10 ms, ga = 1 nS, Pfailure = 0.5). For λ = 0 ms (synchronous release), we have σasyn = σ = 1 ms (temporal dispersion obtained with GABAA synapses, see previous section). We observe that σasyn2 increases linearly with λ2, as predicted by Equation 4. From Equation 4, σasyn = 10 ms when λ = 70 ms, which is the same level of temporal dispersion as the one obtained with synchronous release and slow GABAB synapses (τGABA = 100 ms, see previous section). The loss of spike-timing precision is thus achieved with asynchronous release, despite fast GABAA synapses. Actually, the asynchronous synaptic events sum gradually over time so as to produce a resulting inhibition which decays with a time constant approximately equal to λ (when λ is large as shown previously [34]). Asynchronous release can be seen as a way to produce long-lasting inhibition despite the fast decay time of individual events mediated by GABAA receptors.

Bottom Line: These findings suggest a wiring scheme that triggers stimulus-specific synchronized assemblies.Inhibitory connections are set by Hebbian learning and selectively activated by stimulus patterns to form a spiking associative memory whose storage capacity is comparable to that of classical binary-coded models.We conclude that fast inhibition acts in concert with slow inhibition to reformat the glomerular input into odor-specific synchronized neural assemblies.

View Article: PubMed Central - PubMed

Affiliation: LORIA, Campus Scientifique, Vandoeuvre-lès-Nancy, France. dominique.martinez@loria.fr

ABSTRACT
It has been proposed that synchronized neural assemblies in the antennal lobe of insects encode the identity of olfactory stimuli. In response to an odor, some projection neurons exhibit synchronous firing, phase-locked to the oscillations of the field potential, whereas others do not. Experimental data indicate that neural synchronization and field oscillations are induced by fast GABA(A)-type inhibition, but it remains unclear how desynchronization occurs. We hypothesize that slow inhibition plays a key role in desynchronizing projection neurons. Because synaptic noise is believed to be the dominant factor that limits neuronal reliability, we consider a computational model of the antennal lobe in which a population of oscillatory neurons interact through unreliable GABA(A) and GABA(B) inhibitory synapses. From theoretical analysis and extensive computer simulations, we show that transmission failures at slow GABA(B) synapses make the neural response unpredictable. Depending on the balance between GABA(A) and GABA(B) inputs, particular neurons may either synchronize or desynchronize. These findings suggest a wiring scheme that triggers stimulus-specific synchronized assemblies. Inhibitory connections are set by Hebbian learning and selectively activated by stimulus patterns to form a spiking associative memory whose storage capacity is comparable to that of classical binary-coded models. We conclude that fast inhibition acts in concert with slow inhibition to reformat the glomerular input into odor-specific synchronized neural assemblies.

Show MeSH
Related in: MedlinePlus