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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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Storage and recall in inhibitory sub-circuits.(A) Trained GABAA connectivity. The spiking associative memory consists of oscillatory PNs (one PN per input component) coupled with GABAA and GABAB synapses. Following clipped Hebbian learning (Equation 7), GABAA connections are created between the first, second and fourth PNs (neurons associated to active bits in the training pattern ξμ). For simplicity, we consider that the GABAB network is global. (B) Hypothetical input-dependent gating of lateral inhibition in the AL. Two PNs (PN i and j) are represented as large circles. Lateral inhibition between PNs is gated by inhibitory LNs (small circles) receiving glomerular input. In the presence of an odor, the active glomerulus (black square) turns on the LN (black circle) associated to the connection j → i. The LN releases GABA that binds to GABAA and GABAB receptors onto the postsynaptic cell (PN i). On the contrary, the inactive glomerulus (white square) turns off the LN (white circle) thereby keeping silent the connection i → j. (C) Input-dependent gating of lateral inhibition in the spiking associative memory. The input pattern ξ (noisy version of the training pattern) activates a specific inhibitory circuit in the GABAergic network depicted in (A). The first and second PNs are associated to active bits in the input pattern ξ and their outgoing connections are thus activated. On the contrary, the third PN is associated to an inactive bit in the input pattern and its outgoing connections are turned off. PNs synchronize according to the balance between their GABAA and GABAB inputs (GABAA/GABAB ratio). Here, the first, second and fourth PNs synchronize (GABAA/GABAB≥1) whereas the third PN desynchronizes (GABAA/GABAB<1) and the training pattern is retrieved (synchronized PNs are black).
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pcbi-1000139-g006: Storage and recall in inhibitory sub-circuits.(A) Trained GABAA connectivity. The spiking associative memory consists of oscillatory PNs (one PN per input component) coupled with GABAA and GABAB synapses. Following clipped Hebbian learning (Equation 7), GABAA connections are created between the first, second and fourth PNs (neurons associated to active bits in the training pattern ξμ). For simplicity, we consider that the GABAB network is global. (B) Hypothetical input-dependent gating of lateral inhibition in the AL. Two PNs (PN i and j) are represented as large circles. Lateral inhibition between PNs is gated by inhibitory LNs (small circles) receiving glomerular input. In the presence of an odor, the active glomerulus (black square) turns on the LN (black circle) associated to the connection j → i. The LN releases GABA that binds to GABAA and GABAB receptors onto the postsynaptic cell (PN i). On the contrary, the inactive glomerulus (white square) turns off the LN (white circle) thereby keeping silent the connection i → j. (C) Input-dependent gating of lateral inhibition in the spiking associative memory. The input pattern ξ (noisy version of the training pattern) activates a specific inhibitory circuit in the GABAergic network depicted in (A). The first and second PNs are associated to active bits in the input pattern ξ and their outgoing connections are thus activated. On the contrary, the third PN is associated to an inactive bit in the input pattern and its outgoing connections are turned off. PNs synchronize according to the balance between their GABAA and GABAB inputs (GABAA/GABAB ratio). Here, the first, second and fourth PNs synchronize (GABAA/GABAB≥1) whereas the third PN desynchronizes (GABAA/GABAB<1) and the training pattern is retrieved (synchronized PNs are black).

Mentions: In the previous sections, we have shown that synchronized neural assemblies are triggered by GABAA and GABAB connectivity. In the AL of the honeybee, the GABAergic network is functionnally organized to reflect correlations between glomeruli [37]. In Drosophila, inhibitory LNs present specificity in their odor responses [14], and this specificity results from repeated exposure to an odor [38]. Therefore, it seems plausible that the GABAergic network exhibits some form of Hebbian synaptic plasticity to store odor stimuli (e.g. [39]). To investigate the problem of learning in inhibitory networks, we use our model to store and recall representations of different input patterns. To store M binary patterns ξiμ ∈ {0,1}(μ = 1···M, i = 1···N), we consider, for simplicity, that the GABAB network is global and that the GABAA network is trained using clipped Hebbian learning :(7)where Jij = 1 if presynaptic neuron j is connected to postsynaptic neuron i with a fast GABAA type synapse and Jij = 0 otherwise. Figure 6A provides an example of GABAA connectivity trained from a single pattern. The PNs in the antennal lobe do not inhibit each other directly but they do so via local neurons. Inhibitory LNs receive direct synaptic input from olfactory receptors [40] and show specificities in their response to odors [14],[38]. Consequently, only a sub-network of the trained connectivity may be activated by the olfactory stimulus. Figure 6B depicts a hypothetical input-dependent gating of lateral inhibition between PNs. To develop this idea further, a GABAA connection in our model is functionally active between neurons j and i when both Jij = 1 (connection set by Equation 7) and ξj = 1 (reflecting the fact that a putative LN associated with this connection is activated by input ξj). A GABAB connection is functionally active between neurons j and i only when ξj = 1 (GABAB connectivity is global in the assumptions derived from our model). Figure 6C depicts the sub-network of GABAA and GABAB connections activated by input pattern ξ (noisy version of training pattern ξμ). As seen previously, the relative number of GABAA and GABAB inputs modulate the degree of synchrony. In Figure 6C, the third PN desynchronizes because it only receives GABAB inhibition whereas the other PNs synchronize. If state 1 or 0 is assigned to synchronized or desynchronized neurons respectively, then the original training pattern ξμ is retrieved.


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

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

Storage and recall in inhibitory sub-circuits.(A) Trained GABAA connectivity. The spiking associative memory consists of oscillatory PNs (one PN per input component) coupled with GABAA and GABAB synapses. Following clipped Hebbian learning (Equation 7), GABAA connections are created between the first, second and fourth PNs (neurons associated to active bits in the training pattern ξμ). For simplicity, we consider that the GABAB network is global. (B) Hypothetical input-dependent gating of lateral inhibition in the AL. Two PNs (PN i and j) are represented as large circles. Lateral inhibition between PNs is gated by inhibitory LNs (small circles) receiving glomerular input. In the presence of an odor, the active glomerulus (black square) turns on the LN (black circle) associated to the connection j → i. The LN releases GABA that binds to GABAA and GABAB receptors onto the postsynaptic cell (PN i). On the contrary, the inactive glomerulus (white square) turns off the LN (white circle) thereby keeping silent the connection i → j. (C) Input-dependent gating of lateral inhibition in the spiking associative memory. The input pattern ξ (noisy version of the training pattern) activates a specific inhibitory circuit in the GABAergic network depicted in (A). The first and second PNs are associated to active bits in the input pattern ξ and their outgoing connections are thus activated. On the contrary, the third PN is associated to an inactive bit in the input pattern and its outgoing connections are turned off. PNs synchronize according to the balance between their GABAA and GABAB inputs (GABAA/GABAB ratio). Here, the first, second and fourth PNs synchronize (GABAA/GABAB≥1) whereas the third PN desynchronizes (GABAA/GABAB<1) and the training pattern is retrieved (synchronized PNs are black).
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Related In: Results  -  Collection

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pcbi-1000139-g006: Storage and recall in inhibitory sub-circuits.(A) Trained GABAA connectivity. The spiking associative memory consists of oscillatory PNs (one PN per input component) coupled with GABAA and GABAB synapses. Following clipped Hebbian learning (Equation 7), GABAA connections are created between the first, second and fourth PNs (neurons associated to active bits in the training pattern ξμ). For simplicity, we consider that the GABAB network is global. (B) Hypothetical input-dependent gating of lateral inhibition in the AL. Two PNs (PN i and j) are represented as large circles. Lateral inhibition between PNs is gated by inhibitory LNs (small circles) receiving glomerular input. In the presence of an odor, the active glomerulus (black square) turns on the LN (black circle) associated to the connection j → i. The LN releases GABA that binds to GABAA and GABAB receptors onto the postsynaptic cell (PN i). On the contrary, the inactive glomerulus (white square) turns off the LN (white circle) thereby keeping silent the connection i → j. (C) Input-dependent gating of lateral inhibition in the spiking associative memory. The input pattern ξ (noisy version of the training pattern) activates a specific inhibitory circuit in the GABAergic network depicted in (A). The first and second PNs are associated to active bits in the input pattern ξ and their outgoing connections are thus activated. On the contrary, the third PN is associated to an inactive bit in the input pattern and its outgoing connections are turned off. PNs synchronize according to the balance between their GABAA and GABAB inputs (GABAA/GABAB ratio). Here, the first, second and fourth PNs synchronize (GABAA/GABAB≥1) whereas the third PN desynchronizes (GABAA/GABAB<1) and the training pattern is retrieved (synchronized PNs are black).
Mentions: In the previous sections, we have shown that synchronized neural assemblies are triggered by GABAA and GABAB connectivity. In the AL of the honeybee, the GABAergic network is functionnally organized to reflect correlations between glomeruli [37]. In Drosophila, inhibitory LNs present specificity in their odor responses [14], and this specificity results from repeated exposure to an odor [38]. Therefore, it seems plausible that the GABAergic network exhibits some form of Hebbian synaptic plasticity to store odor stimuli (e.g. [39]). To investigate the problem of learning in inhibitory networks, we use our model to store and recall representations of different input patterns. To store M binary patterns ξiμ ∈ {0,1}(μ = 1···M, i = 1···N), we consider, for simplicity, that the GABAB network is global and that the GABAA network is trained using clipped Hebbian learning :(7)where Jij = 1 if presynaptic neuron j is connected to postsynaptic neuron i with a fast GABAA type synapse and Jij = 0 otherwise. Figure 6A provides an example of GABAA connectivity trained from a single pattern. The PNs in the antennal lobe do not inhibit each other directly but they do so via local neurons. Inhibitory LNs receive direct synaptic input from olfactory receptors [40] and show specificities in their response to odors [14],[38]. Consequently, only a sub-network of the trained connectivity may be activated by the olfactory stimulus. Figure 6B depicts a hypothetical input-dependent gating of lateral inhibition between PNs. To develop this idea further, a GABAA connection in our model is functionally active between neurons j and i when both Jij = 1 (connection set by Equation 7) and ξj = 1 (reflecting the fact that a putative LN associated with this connection is activated by input ξj). A GABAB connection is functionally active between neurons j and i only when ξj = 1 (GABAB connectivity is global in the assumptions derived from our model). Figure 6C depicts the sub-network of GABAA and GABAB connections activated by input pattern ξ (noisy version of training pattern ξμ). As seen previously, the relative number of GABAA and GABAB inputs modulate the degree of synchrony. In Figure 6C, the third PN desynchronizes because it only receives GABAB inhibition whereas the other PNs synchronize. If state 1 or 0 is assigned to synchronized or desynchronized neurons respectively, then the original training pattern ξμ is retrieved.

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