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Exploring the limits of learning: segregation of information integration and response selection is required for learning a serial reversal task

View ORCID ProfileCamilo J. Mininni, B. Silvano Zanutto
doi: https://doi.org/10.1101/163725
Camilo J. Mininni
1 Instituto de Ingeniería Biomédica–Universidad de Buenos Aires, Buenos Aires, Argentina
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  • For correspondence: mininni@dna.uba.ar
B. Silvano Zanutto
1 Instituto de Ingeniería Biomédica–Universidad de Buenos Aires, Buenos Aires, Argentina
2 Instituto de Biología y Medicina Experimental–Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
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Abstract

Animals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can lead to situations in which the same stimulus/response pairing is positively and negatively rewarded in the long run, depending on variables that are not accessible to the animal. This fact rises questions on how neural systems are capable of reinforcement learning in environments where the reinforcement is inconsistent. Here we address this issue by asking about which aspects of connectivity, neural excitability and synaptic plasticity are key for a very general, stochastic spiking neural network model to solve a task in which rules change without being cued, taking the serial reversal task (SRT) as paradigm. Contrary to what could be expected, we found strong limitations for biologically plausible networks to solve the SRT. Especially, we proved that no network of neurons can learn a SRT if it is a single neural population that integrates stimuli information and at the same time is responsible of choosing the behavioural response. This limitation is independent of the number of neurons, neuronal dynamics or plasticity rules, and arises from the fact that plasticity is locally computed at each synapse, and that synaptic changes and neuronal activity are mutually dependent processes. We propose and characterize a spiking neural network model that solves the SRT, which relies on separating the functions of stimuli integration and response selection. The model suggests that experimental efforts to understand neural function should focus on the characterization of neural circuits according to their connectivity, neural dynamics, and the degree of modulation of synaptic plasticity with reward.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 28, 2017.
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Exploring the limits of learning: segregation of information integration and response selection is required for learning a serial reversal task
Camilo J. Mininni, B. Silvano Zanutto
bioRxiv 163725; doi: https://doi.org/10.1101/163725
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Exploring the limits of learning: segregation of information integration and response selection is required for learning a serial reversal task
Camilo J. Mininni, B. Silvano Zanutto
bioRxiv 163725; doi: https://doi.org/10.1101/163725

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