Inter-synaptic learning of combination rules in a cortical network model
Selecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the res...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-08-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.00842/full |
| _version_ | 1828517705352740864 |
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| author | Frédéric eLavigne Francis eAvnaïm Laurent eDumercy |
| author_facet | Frédéric eLavigne Francis eAvnaïm Laurent eDumercy |
| author_sort | Frédéric eLavigne |
| collection | DOAJ |
| description | Selecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the responses within the space of stimuli. One proposed solution is to add neurons that perform a stage of non-linear processing between the stimuli and responses, at the cost of increasing the network size. Based on the non-linear integration of synaptic inputs within dendritic compartments, we propose here an inter-synaptic (IS) learning algorithm that determines the probability of potentiating/depressing each synapse as a function of the co-activity of the other synapses within the same dendrite. The IS learning is effective with random connectivity and without either a priori wiring or additional neurons.Our results show that IS learning generates efficacy values that are sufficient for the processing of XOR-like combinations, on the basis of the sole correlational structure of the stimuli and responses. We analyze the types of dendrites involved in terms of the number of synapses from pre-synaptic neurons coding for the stimuli and responses. The synaptic efficacy values obtained show that different dendrites specialize in the detection of different combinations of stimuli. The resulting behavior of the cortical network model is analyzed as a function of inter-synaptic vs. Hebbian learning. Combinatorial priming effects show that the retrospective activity of neurons coding for the stimuli trigger XOR-like combination-selective prospective activity of neurons coding for the expected response. The synergistic effects of inter-synaptic learning and of mixed-coding neurons are simulated. The results show that, although each mechanism is sufficient by itself, their combined effects improve the performance of the network. |
| first_indexed | 2024-12-11T18:45:07Z |
| format | Article |
| id | doaj.art-797e3d9712d340e09896f3711d8cab71 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-11T18:45:07Z |
| publishDate | 2014-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-797e3d9712d340e09896f3711d8cab712022-12-22T00:54:29ZengFrontiers Media S.A.Frontiers in Psychology1664-10782014-08-01510.3389/fpsyg.2014.0084280730Inter-synaptic learning of combination rules in a cortical network modelFrédéric eLavigne0Francis eAvnaïm1Laurent eDumercy2Université de Nice - Sophia antipolisUniversité de Nice - Sophia antipolisUniversité de Nice - Sophia antipolisSelecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the responses within the space of stimuli. One proposed solution is to add neurons that perform a stage of non-linear processing between the stimuli and responses, at the cost of increasing the network size. Based on the non-linear integration of synaptic inputs within dendritic compartments, we propose here an inter-synaptic (IS) learning algorithm that determines the probability of potentiating/depressing each synapse as a function of the co-activity of the other synapses within the same dendrite. The IS learning is effective with random connectivity and without either a priori wiring or additional neurons.Our results show that IS learning generates efficacy values that are sufficient for the processing of XOR-like combinations, on the basis of the sole correlational structure of the stimuli and responses. We analyze the types of dendrites involved in terms of the number of synapses from pre-synaptic neurons coding for the stimuli and responses. The synaptic efficacy values obtained show that different dendrites specialize in the detection of different combinations of stimuli. The resulting behavior of the cortical network model is analyzed as a function of inter-synaptic vs. Hebbian learning. Combinatorial priming effects show that the retrospective activity of neurons coding for the stimuli trigger XOR-like combination-selective prospective activity of neurons coding for the expected response. The synergistic effects of inter-synaptic learning and of mixed-coding neurons are simulated. The results show that, although each mechanism is sufficient by itself, their combined effects improve the performance of the network.http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.00842/fullDendritesprimingcortical networkssynaptic clusteringcombination ruleinter-synaptic learning |
| spellingShingle | Frédéric eLavigne Francis eAvnaïm Laurent eDumercy Inter-synaptic learning of combination rules in a cortical network model Frontiers in Psychology Dendrites priming cortical networks synaptic clustering combination rule inter-synaptic learning |
| title | Inter-synaptic learning of combination rules in a cortical network model |
| title_full | Inter-synaptic learning of combination rules in a cortical network model |
| title_fullStr | Inter-synaptic learning of combination rules in a cortical network model |
| title_full_unstemmed | Inter-synaptic learning of combination rules in a cortical network model |
| title_short | Inter-synaptic learning of combination rules in a cortical network model |
| title_sort | inter synaptic learning of combination rules in a cortical network model |
| topic | Dendrites priming cortical networks synaptic clustering combination rule inter-synaptic learning |
| url | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.00842/full |
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