Competition model of pheromone ratio detection (Zavada et al. 2011)

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Accession:143753
For some closely related sympatric moth species, recognizing a specific pheromone component concentration ratio is essential for mating success. We propose and test a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which. (3) longer durations of the competition process between LNs did not result in higher recognition accuracy.
Reference:
1 . Zavada A, Buckley CL, Martinez D, Rospars JP, Nowotny T (2011) Competition-based model of pheromone component ratio detection in the moth. PLoS One 6:e16308 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Olfactory receptor GLU cell; Hodgkin-Huxley neuron;
Channel(s):
Gap Junctions:
Receptor(s): Gaba;
Gene(s):
Transmitter(s): Acetylcholine; Gaba;
Simulation Environment: C or C++ program; CNrun; neuroConstruct;
Model Concept(s): Simplified Models; Sensory processing; Olfaction;
Implementer(s): Nowotny, Thomas [t.nowotny at sussex.ac.uk]; Zavada, Andrei [johnhommer at gmail.com];
Search NeuronDB for information about:  Olfactory receptor GLU cell; Gaba; Acetylcholine; Gaba;
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zavada.etal2011-model
readme.txt
model.nml
neuroConstruct-src.xml
                            
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<meta:notes>

Network structure for project: x1 saved with neuroConstruct v1.2.2 on: 19:14:32, 15-May-09

Cell Group: LN1 contains 1 cells
Cell Group: PN contains 1 cells
Cell Group: LNz contains 1 cells
Cell Group: PNi contains 1 cells
Cell Group: ORNb contains 1 cells
Cell Group: LN2 contains 1 cells
Cell Group: ORNa contains 1 cells

Network connection: LNz-LN1 contains 1 individual synaptic connections
Network connection: LN2-LNz contains 1 individual synaptic connections
Network connection: ORNb-LNz contains 1 individual synaptic connections
Network connection: ORNa-LNz contains 1 individual synaptic connections
Network connection: LN1-LNz contains 1 individual synaptic connections
Network connection: ORNb-LN2 contains 1 individual synaptic connections
Network connection: LN2-LN1 contains 1 individual synaptic connections
Network connection: LN1-LN2 contains 1 individual synaptic connections
Network connection: ORNa-LN1 contains 1 individual synaptic connections
Network connection: LNz-PNi contains 1 individual synaptic connections
Network connection: PNi-PN contains 1 individual synaptic connections
Network connection: LNz-LN2 contains 1 individual synaptic connections

</meta:notes>

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	<meta:tag>neuroConstruct_random_seed</meta:tag>
	<meta:value>-2095241054</meta:value>
    </meta:property>
    <meta:property>
	<meta:tag>neuroConstruct_sim_config</meta:tag>
	<meta:value>Default Simulation Configuration</meta:value>
    </meta:property>
</meta:properties>

<populations>
    <population name="LN1" cell_type="HH">
	<instances size="1">
	    <instance id="0">
		<location x="6.0" y="-73.61524" z="37.5"/>
	    </instance>
	</instances>
    </population>
    <population name="PN" cell_type="HH">
	<instances size="1">
	    <instance id="0">
		<location x="-16.0" y="-16.0" z="-127.0"/>
	    </instance>
	</instances>
    </population>
    <population name="LNz" cell_type="HH">
	<instances size="1">
	    <instance id="0">
		<location x="31.437284" y="-0.35245514" z="57.219738"/>
	    </instance>
	</instances>
    </population>
    <population name="PNi" cell_type="HH">
	<instances size="1">
	    <instance id="0">
		<location x="-16.0" y="-16.0" z="-97.0"/>
	    </instance>
	</instances>
    </population>
    <population name="ORNb" cell_type="DotPoisson">
	<instances size="1">
	    <instance id="0">
		<location x="30.84151" y="111.14973" z="324.96512"/>
	    </instance>
	</instances>
    </population>
    <population name="LN2" cell_type="HH">
	<instances size="1">
	    <instance id="0">
		<location x="6.0" y="56.38476" z="37.5"/>
	    </instance>
	</instances>
    </population>
    <population name="ORNa" cell_type="DotPoisson">
	<instances size="1">
	    <instance id="0">
		<location x="57.5488" y="-135.62527" z="246.17108"/>
	    </instance>
	</instances>
    </population>
</populations>

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    <projection name="LNz-LN1" source="LNz" target="LN1">
	<synapse_props synapse_type="AB" internal_delay="5.0" weight="1.0" threshold="-20.0">
	</synapse_props>
	<connections>
	    <connection id="0" pre_cell_id="0" pre_segment_id="7" pre_fraction_along="0.55582243" post_cell_id="0" post_segment_id="10" post_fraction_along="0.38091567">
	    </connection>
	</connections>
    </projection>
    <projection name="LN2-LNz" source="LN2" target="LNz">
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	</synapse_props>
	<connections>
	    <connection id="0" pre_cell_id="0" pre_segment_id="7" pre_fraction_along="0.8175385" post_cell_id="0" post_segment_id="10" post_fraction_along="0.08029305">
	    </connection>
	</connections>
    </projection>
    <projection name="ORNb-LNz" source="ORNb" target="LNz">
	<synapse_props synapse_type="AB" internal_delay="5.0" weight="1.0" threshold="-20.0">
	</synapse_props>
	<connections>
	    <connection id="0" pre_cell_id="0" pre_segment_id="1" pre_fraction_along="0.9915016" post_cell_id="0" post_segment_id="9" post_fraction_along="0.4251294">
	    </connection>
	</connections>
    </projection>
    <projection name="ORNa-LNz" source="ORNa" target="LNz">
	<synapse_props synapse_type="AB" internal_delay="5.0" weight="1.0" threshold="-20.0">
	</synapse_props>
	<connections>
	    <connection id="0" pre_cell_id="0" pre_segment_id="1" pre_fraction_along="0.4514249" post_cell_id="0" post_segment_id="9" post_fraction_along="0.023154497">
	    </connection>
	</connections>
    </projection>
    <projection name="LN1-LNz" source="LN1" target="LNz">
	<synapse_props synapse_type="AB" internal_delay="5.0" weight="1.0" threshold="-20.0">
	</synapse_props>
	<connections>
	    <connection id="0" pre_cell_id="0" pre_segment_id="7" pre_fraction_along="0.2117557" post_cell_id="0" post_segment_id="10" post_fraction_along="0.42112848">
	    </connection>
	</connections>
    </projection>
    <projection name="ORNb-LN2" source="ORNb" target="LN2">
	<synapse_props synapse_type="AB" internal_delay="5.0" weight="1.0" threshold="-20.0">
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	<connections>
	    <connection id="0" pre_cell_id="0" pre_segment_id="1" pre_fraction_along="0.96524084" post_cell_id="0" post_segment_id="10" post_fraction_along="0.14532664">
	    </connection>
	</connections>
    </projection>
    <projection name="LN2-LN1" source="LN2" target="LN1">
	<synapse_props synapse_type="AB" internal_delay="5.0" weight="1.0" threshold="-20.0">
	</synapse_props>
	<connections>
	    <connection id="0" pre_cell_id="0" pre_segment_id="7" pre_fraction_along="0.5863692" post_cell_id="0" post_segment_id="10" post_fraction_along="0.50665826">
	    </connection>
	</connections>
    </projection>
    <projection name="LN1-LN2" source="LN1" target="LN2">
	<synapse_props synapse_type="AB" internal_delay="5.0" weight="1.0" threshold="-20.0">
	</synapse_props>
	<connections>
	    <connection id="0" pre_cell_id="0" pre_segment_id="7" pre_fraction_along="0.6822715" post_cell_id="0" post_segment_id="10" post_fraction_along="0.44029188">
	    </connection>
	</connections>
    </projection>
    <projection name="ORNa-LN1" source="ORNa" target="LN1">
	<synapse_props synapse_type="AB" internal_delay="5.0" weight="1.0" threshold="-20.0">
	</synapse_props>
	<connections>
	    <connection id="0" pre_cell_id="0" pre_segment_id="1" pre_fraction_along="0.82270634" post_cell_id="0" post_segment_id="10" post_fraction_along="0.58317965">
	    </connection>
	</connections>
    </projection>
    <projection name="LNz-PNi" source="LNz" target="PNi">
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	</synapse_props>
	<connections>
	    <connection id="0" pre_cell_id="0" pre_segment_id="7" pre_fraction_along="0.8898634" post_cell_id="0" post_segment_id="10" post_fraction_along="0.30973306">
	    </connection>
	</connections>
    </projection>
    <projection name="PNi-PN" source="PNi" target="PN">
	<synapse_props synapse_type="AB" internal_delay="5.0" weight="1.0" threshold="-20.0">
	</synapse_props>
	<connections>
	    <connection id="0" pre_cell_id="0" pre_segment_id="7" pre_fraction_along="0.23782128" post_cell_id="0" post_segment_id="9" post_fraction_along="0.69209504">
	    </connection>
	</connections>
    </projection>
    <projection name="LNz-LN2" source="LNz" target="LN2">
	<synapse_props synapse_type="AB" internal_delay="5.0" weight="1.0" threshold="-20.0">
	</synapse_props>
	<connections>
	    <connection id="0" pre_cell_id="0" pre_segment_id="7" pre_fraction_along="0.9626454" post_cell_id="0" post_segment_id="9" post_fraction_along="0.18079913">
	    </connection>
	</connections>
    </projection>
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    <!--There are no electrical inputs present in the network-->


</networkml>

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