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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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Phase diagrams in the presence of network heterogeneity and/or synaptic failure.(A–D) The synchronous stationary state (sync.) corresponding to σ<5 ms is depicted as the blue region. ga and gb are expressed in nS and denote the values of the peak conductance g in Equation 12 for GABAA and GABAB, respectively. The dashed lines separating the synchronous state to the asychronous state were obtained by fitting the contour plot σ = 5 ms. The equations of the separating line are ga = 11gb (global, Pfailure = 0.5, (B)), ga = 14gb (heterogeneous, Pfailure = 0.0, (C) and ga = 25gb (heterogeneous, Pfailure = 0.5, (D)). (E) spike rasterplots are indicated for a network (heterogeneous connectivity and Pfailure = 0.5) with intact connections (ga = 1 nS and gb = 0.1 nS) and with GABAA or GABAB blocked. (F) Clustering of synchronized activity patterns. Two networks (A and B) of N = 100 neurons have been randomly generated with 0.5 probability of connection. At each oscillatory cycle, the network activity is represented as a binary vector in a multidimensional space (N = 100), where each dimension corresponds to the binary state of a given PN (1 if synchronized and 0 otherwise). The resolution at which synchronized neurons are determined is ε = 5 ms (see Methods). We pooled the binary data obtained at the different oscillatory cycles (extracted between 300 to 3000 ms), for the different networks (A and B) and from repeated trials (3 runs for each network). The data were projected, using logistic PCA [36], onto the first two principal components (PC). Red and blue points in the PCA plane are the projected data for networks A and B, respectively. Left is for intact networks, with GABAA and GABAB coupling (ga = 1 nS and gb = 0.1 nS). Middle and right are for GABAA or GABAB blocked, respectively.
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pcbi-1000139-g005: Phase diagrams in the presence of network heterogeneity and/or synaptic failure.(A–D) The synchronous stationary state (sync.) corresponding to σ<5 ms is depicted as the blue region. ga and gb are expressed in nS and denote the values of the peak conductance g in Equation 12 for GABAA and GABAB, respectively. The dashed lines separating the synchronous state to the asychronous state were obtained by fitting the contour plot σ = 5 ms. The equations of the separating line are ga = 11gb (global, Pfailure = 0.5, (B)), ga = 14gb (heterogeneous, Pfailure = 0.0, (C) and ga = 25gb (heterogeneous, Pfailure = 0.5, (D)). (E) spike rasterplots are indicated for a network (heterogeneous connectivity and Pfailure = 0.5) with intact connections (ga = 1 nS and gb = 0.1 nS) and with GABAA or GABAB blocked. (F) Clustering of synchronized activity patterns. Two networks (A and B) of N = 100 neurons have been randomly generated with 0.5 probability of connection. At each oscillatory cycle, the network activity is represented as a binary vector in a multidimensional space (N = 100), where each dimension corresponds to the binary state of a given PN (1 if synchronized and 0 otherwise). The resolution at which synchronized neurons are determined is ε = 5 ms (see Methods). We pooled the binary data obtained at the different oscillatory cycles (extracted between 300 to 3000 ms), for the different networks (A and B) and from repeated trials (3 runs for each network). The data were projected, using logistic PCA [36], onto the first two principal components (PC). Red and blue points in the PCA plane are the projected data for networks A and B, respectively. Left is for intact networks, with GABAA and GABAB coupling (ga = 1 nS and gb = 0.1 nS). Middle and right are for GABAA or GABAB blocked, respectively.

Mentions: In the previous sections, the effect of GABAA or GABAB on synchrony has been studied in isolation. We now consider a network of N = 100 neurons coupled with both fast and slow inhibition. A probability of synaptic failure (Pfailure = 0.5 and 0.0) is considered and two patterns of connectivity are taken into account: global (neurons are connected all-to-all) and heterogeneous (neurons are randomly connected with 0.5 probability). Figure 5 presents the spike time jitter estimated from simulations for different values of the GABAA and GABAB conductances ga and gb. In the absence of synaptic failure and network heterogeneity, the synchronized state (defined as σ<5 ms, blue region in Figure 5) extends to the entire phase space (Figure 5A). In the presence of network heterogeneity and/or synaptic failure, however, the synchronized state depends on the relative amount of received fast and slow inhibition. The dashed lines demarcating the synchronous state are similar in the case of global connectivity and Pfailure = 0.5 (Figure 5B) as well as in the case of heterogeneous connectivity and Pfailure = 0.0 (Figure 5C). Thus, network heterogeneity and synaptic failure play the same role in breaking synchrony. With heterogeneous connectivity and synaptic noise (Pfailure = 0.5), the line demarcating the synchronous state in Figure 5D is ga/gb≈25 (σ<5 ms when ga/gb>25).


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

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

Phase diagrams in the presence of network heterogeneity and/or synaptic failure.(A–D) The synchronous stationary state (sync.) corresponding to σ<5 ms is depicted as the blue region. ga and gb are expressed in nS and denote the values of the peak conductance g in Equation 12 for GABAA and GABAB, respectively. The dashed lines separating the synchronous state to the asychronous state were obtained by fitting the contour plot σ = 5 ms. The equations of the separating line are ga = 11gb (global, Pfailure = 0.5, (B)), ga = 14gb (heterogeneous, Pfailure = 0.0, (C) and ga = 25gb (heterogeneous, Pfailure = 0.5, (D)). (E) spike rasterplots are indicated for a network (heterogeneous connectivity and Pfailure = 0.5) with intact connections (ga = 1 nS and gb = 0.1 nS) and with GABAA or GABAB blocked. (F) Clustering of synchronized activity patterns. Two networks (A and B) of N = 100 neurons have been randomly generated with 0.5 probability of connection. At each oscillatory cycle, the network activity is represented as a binary vector in a multidimensional space (N = 100), where each dimension corresponds to the binary state of a given PN (1 if synchronized and 0 otherwise). The resolution at which synchronized neurons are determined is ε = 5 ms (see Methods). We pooled the binary data obtained at the different oscillatory cycles (extracted between 300 to 3000 ms), for the different networks (A and B) and from repeated trials (3 runs for each network). The data were projected, using logistic PCA [36], onto the first two principal components (PC). Red and blue points in the PCA plane are the projected data for networks A and B, respectively. Left is for intact networks, with GABAA and GABAB coupling (ga = 1 nS and gb = 0.1 nS). Middle and right are for GABAA or GABAB blocked, respectively.
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pcbi-1000139-g005: Phase diagrams in the presence of network heterogeneity and/or synaptic failure.(A–D) The synchronous stationary state (sync.) corresponding to σ<5 ms is depicted as the blue region. ga and gb are expressed in nS and denote the values of the peak conductance g in Equation 12 for GABAA and GABAB, respectively. The dashed lines separating the synchronous state to the asychronous state were obtained by fitting the contour plot σ = 5 ms. The equations of the separating line are ga = 11gb (global, Pfailure = 0.5, (B)), ga = 14gb (heterogeneous, Pfailure = 0.0, (C) and ga = 25gb (heterogeneous, Pfailure = 0.5, (D)). (E) spike rasterplots are indicated for a network (heterogeneous connectivity and Pfailure = 0.5) with intact connections (ga = 1 nS and gb = 0.1 nS) and with GABAA or GABAB blocked. (F) Clustering of synchronized activity patterns. Two networks (A and B) of N = 100 neurons have been randomly generated with 0.5 probability of connection. At each oscillatory cycle, the network activity is represented as a binary vector in a multidimensional space (N = 100), where each dimension corresponds to the binary state of a given PN (1 if synchronized and 0 otherwise). The resolution at which synchronized neurons are determined is ε = 5 ms (see Methods). We pooled the binary data obtained at the different oscillatory cycles (extracted between 300 to 3000 ms), for the different networks (A and B) and from repeated trials (3 runs for each network). The data were projected, using logistic PCA [36], onto the first two principal components (PC). Red and blue points in the PCA plane are the projected data for networks A and B, respectively. Left is for intact networks, with GABAA and GABAB coupling (ga = 1 nS and gb = 0.1 nS). Middle and right are for GABAA or GABAB blocked, respectively.
Mentions: In the previous sections, the effect of GABAA or GABAB on synchrony has been studied in isolation. We now consider a network of N = 100 neurons coupled with both fast and slow inhibition. A probability of synaptic failure (Pfailure = 0.5 and 0.0) is considered and two patterns of connectivity are taken into account: global (neurons are connected all-to-all) and heterogeneous (neurons are randomly connected with 0.5 probability). Figure 5 presents the spike time jitter estimated from simulations for different values of the GABAA and GABAB conductances ga and gb. In the absence of synaptic failure and network heterogeneity, the synchronized state (defined as σ<5 ms, blue region in Figure 5) extends to the entire phase space (Figure 5A). In the presence of network heterogeneity and/or synaptic failure, however, the synchronized state depends on the relative amount of received fast and slow inhibition. The dashed lines demarcating the synchronous state are similar in the case of global connectivity and Pfailure = 0.5 (Figure 5B) as well as in the case of heterogeneous connectivity and Pfailure = 0.0 (Figure 5C). Thus, network heterogeneity and synaptic failure play the same role in breaking synchrony. With heterogeneous connectivity and synaptic noise (Pfailure = 0.5), the line demarcating the synchronous state in Figure 5D is ga/gb≈25 (σ<5 ms when ga/gb>25).

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