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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: In response to an odor, some projection neurons exhibit synchronous firing, phase-locked to the oscillations of the field potential, whereas others do not.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 GABAA or GABAB inhibition.In (A–G), the failure probability is Pfailure = 0.5. (A): Spike rasterplot for GABAA coupling. The peak GABAA conductance is ga = 1 nS. The frequency of the network oscillation is F ∼20 Hz. (B) Spike rasterplot for GABAB coupling (gb = 0.1 nS, F ∼10 Hz). (C,D) Temporal evolution of the spike time jitter σ(n), where n is the index of the oscillatory cycle. Convergence is reached in about 3 cycles, i.e., 300 ms with GABAB and 150 ms with GABAA. The initial condition is the desynchronized state (see Methods). (E,F) Same conventions as in (C–D), except that the initial condition is now the synchronized state. (G) Spike time jitter σ obtained at convergence (σ(n) averaged over the last two oscillatory cycles) as a function of the mean inhibitory drive 〈k〉 received by the neurons (the number of neurons N scales from 50 to 400). (H) σ as a function of the failure probability Pfailure. In (C–H), the stars represent the spike time jitter estimated from simulations (see Methods, means and standard deviations estimated over 10 runs). The solid curves are for theoretical values obtained from Equation 1 (in C–F) or from Equation 2 (in G–H).
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pcbi-1000139-g002: Spike timing precision with GABAA or GABAB inhibition.In (A–G), the failure probability is Pfailure = 0.5. (A): Spike rasterplot for GABAA coupling. The peak GABAA conductance is ga = 1 nS. The frequency of the network oscillation is F ∼20 Hz. (B) Spike rasterplot for GABAB coupling (gb = 0.1 nS, F ∼10 Hz). (C,D) Temporal evolution of the spike time jitter σ(n), where n is the index of the oscillatory cycle. Convergence is reached in about 3 cycles, i.e., 300 ms with GABAB and 150 ms with GABAA. The initial condition is the desynchronized state (see Methods). (E,F) Same conventions as in (C–D), except that the initial condition is now the synchronized state. (G) Spike time jitter σ obtained at convergence (σ(n) averaged over the last two oscillatory cycles) as a function of the mean inhibitory drive 〈k〉 received by the neurons (the number of neurons N scales from 50 to 400). (H) σ as a function of the failure probability Pfailure. In (C–H), the stars represent the spike time jitter estimated from simulations (see Methods, means and standard deviations estimated over 10 runs). The solid curves are for theoretical values obtained from Equation 1 (in C–F) or from Equation 2 (in G–H).

Mentions: We consider two distinct networks of N = 100 neurons completely connected, one with fast GABAA synapses (τGABA = 10 ms) and another with slow GABAB synapses (τGABA = 100 ms). Since chemical synapses are believed to be quite unreliable [23], a probability of synaptic failure Pfailure is taken into account. Rasterplots in Figure 2A and 2B present network oscillations in the presence of fast or slow inhibition, the frequency being higher with fast inhibition (F ∼20 Hz with GABAA and F ∼10 Hz with GABAB). As revealed by Equation A-1 (see Text S1), the period T of the network oscillations grows as ln g where g is the peak synaptic conductance. The period is thus quite robust to changes in the strength of inhibition. However, it depends linearly on the decay time τGABA of the inhibitory synapse. This observation is in agreement with simulation results (see Figure S1) and with previous studies, e.g., [29]. In Figure 2A and 2B, the PN population is partially synchronized but with higher temporal dispersion in the presence of slow inhibition. We now quantify analytically the temporal dispersion of the spiking events within each cycle. As shown in Text S1 (Equation A-3), the spike time jitter σ2(n) of the PN population at the n-th cycle can be expressed as a simple linear recursive relation(1)where 〈k〉 = N(1−Pfailure) and σk2+NPfailure (1−Pfailure) are the mean and variance in the number k of inhibitory synaptic events received by the PNs at each cycle. Note that the mathematical analysis in Text S1 did not take into account the PNs that do not receive any inhibition. Equation 1 is therefore not valid when Pfailure = 1. Figure 2C and 2D compares the theoretical jitter σ2(n) given by Equation 1 to the one obtained from simulations (see Methods). From the figure, we see that the spike time jitter reaches a stable state in about n = 3 cycles (300 ms with GABAB versus 150 ms with GABAA). This stable state does not depend on initial conditions (compare Figure 2C and 2D with Figure 2E and 2F) but does depend on the time constant of the inhibitory synapse : σ≈1 ms for GABAA and σ≈10 ms for GABAB. From Equation 1, the spike time jitter obtained at convergence is given by(2)


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

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

Spike timing precision with GABAA or GABAB inhibition.In (A–G), the failure probability is Pfailure = 0.5. (A): Spike rasterplot for GABAA coupling. The peak GABAA conductance is ga = 1 nS. The frequency of the network oscillation is F ∼20 Hz. (B) Spike rasterplot for GABAB coupling (gb = 0.1 nS, F ∼10 Hz). (C,D) Temporal evolution of the spike time jitter σ(n), where n is the index of the oscillatory cycle. Convergence is reached in about 3 cycles, i.e., 300 ms with GABAB and 150 ms with GABAA. The initial condition is the desynchronized state (see Methods). (E,F) Same conventions as in (C–D), except that the initial condition is now the synchronized state. (G) Spike time jitter σ obtained at convergence (σ(n) averaged over the last two oscillatory cycles) as a function of the mean inhibitory drive 〈k〉 received by the neurons (the number of neurons N scales from 50 to 400). (H) σ as a function of the failure probability Pfailure. In (C–H), the stars represent the spike time jitter estimated from simulations (see Methods, means and standard deviations estimated over 10 runs). The solid curves are for theoretical values obtained from Equation 1 (in C–F) or from Equation 2 (in G–H).
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Related In: Results  -  Collection

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pcbi-1000139-g002: Spike timing precision with GABAA or GABAB inhibition.In (A–G), the failure probability is Pfailure = 0.5. (A): Spike rasterplot for GABAA coupling. The peak GABAA conductance is ga = 1 nS. The frequency of the network oscillation is F ∼20 Hz. (B) Spike rasterplot for GABAB coupling (gb = 0.1 nS, F ∼10 Hz). (C,D) Temporal evolution of the spike time jitter σ(n), where n is the index of the oscillatory cycle. Convergence is reached in about 3 cycles, i.e., 300 ms with GABAB and 150 ms with GABAA. The initial condition is the desynchronized state (see Methods). (E,F) Same conventions as in (C–D), except that the initial condition is now the synchronized state. (G) Spike time jitter σ obtained at convergence (σ(n) averaged over the last two oscillatory cycles) as a function of the mean inhibitory drive 〈k〉 received by the neurons (the number of neurons N scales from 50 to 400). (H) σ as a function of the failure probability Pfailure. In (C–H), the stars represent the spike time jitter estimated from simulations (see Methods, means and standard deviations estimated over 10 runs). The solid curves are for theoretical values obtained from Equation 1 (in C–F) or from Equation 2 (in G–H).
Mentions: We consider two distinct networks of N = 100 neurons completely connected, one with fast GABAA synapses (τGABA = 10 ms) and another with slow GABAB synapses (τGABA = 100 ms). Since chemical synapses are believed to be quite unreliable [23], a probability of synaptic failure Pfailure is taken into account. Rasterplots in Figure 2A and 2B present network oscillations in the presence of fast or slow inhibition, the frequency being higher with fast inhibition (F ∼20 Hz with GABAA and F ∼10 Hz with GABAB). As revealed by Equation A-1 (see Text S1), the period T of the network oscillations grows as ln g where g is the peak synaptic conductance. The period is thus quite robust to changes in the strength of inhibition. However, it depends linearly on the decay time τGABA of the inhibitory synapse. This observation is in agreement with simulation results (see Figure S1) and with previous studies, e.g., [29]. In Figure 2A and 2B, the PN population is partially synchronized but with higher temporal dispersion in the presence of slow inhibition. We now quantify analytically the temporal dispersion of the spiking events within each cycle. As shown in Text S1 (Equation A-3), the spike time jitter σ2(n) of the PN population at the n-th cycle can be expressed as a simple linear recursive relation(1)where 〈k〉 = N(1−Pfailure) and σk2+NPfailure (1−Pfailure) are the mean and variance in the number k of inhibitory synaptic events received by the PNs at each cycle. Note that the mathematical analysis in Text S1 did not take into account the PNs that do not receive any inhibition. Equation 1 is therefore not valid when Pfailure = 1. Figure 2C and 2D compares the theoretical jitter σ2(n) given by Equation 1 to the one obtained from simulations (see Methods). From the figure, we see that the spike time jitter reaches a stable state in about n = 3 cycles (300 ms with GABAB versus 150 ms with GABAA). This stable state does not depend on initial conditions (compare Figure 2C and 2D with Figure 2E and 2F) but does depend on the time constant of the inhibitory synapse : σ≈1 ms for GABAA and σ≈10 ms for GABAB. From Equation 1, the spike time jitter obtained at convergence is given by(2)

Bottom Line: In response to an odor, some projection neurons exhibit synchronous firing, phase-locked to the oscillations of the field potential, whereas others do not.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