TY - JOUR

T1 - From altered synaptic plasticity to atypical learning

T2 - a computational model of Down syndrome

AU - Tovar Y Romo, Angel Eugenio

AU - Westermann, Gert

AU - Torres, Alvaro

N1 - This is the author’s version of a work that was accepted for publication in Cognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Cognition, 171, 2018 DOI: 10.1016/j.cognition.2017.10.021

PY - 2018/2/1

Y1 - 2018/2/1

N2 - Learning and memory rely on the adaptation of synaptic connections. Research on the neurophysiology of Down syndrome has characterized an atypical pattern of synaptic plasticity with limited long-term potentiation (LTP) and increased long-term depression (LTD). Here we present a neurocomputational model that instantiates this LTP/LTD imbalance to explore its impact on tasks of associative learning. In Study 1, we ran a series of computational simulations to analyze the learning of simple and overlapping stimulus associations in a model of Down syndrome compared with a model of typical development. Learning in the Down syndrome model was slower and more susceptible to interference effects. We found that interference effects could be overcome with dedicated stimulation schedules. In Study 2, we ran a second set of simulations and an empirical study with participants with Down syndrome and typically developing children to test the predictions of our model. The model adequately predicted the performance of the human participants in a serial reaction time task, an implicit learning task that relies on associative learning mechanisms. Critically, typical and atypical behavior was explained by the interactions between neural plasticity constraints and the stimulation schedule. Our model provides a mechanistic account of learning impairments based on these interactions, and a causal link between atypical synaptic plasticity and associative learning.

AB - Learning and memory rely on the adaptation of synaptic connections. Research on the neurophysiology of Down syndrome has characterized an atypical pattern of synaptic plasticity with limited long-term potentiation (LTP) and increased long-term depression (LTD). Here we present a neurocomputational model that instantiates this LTP/LTD imbalance to explore its impact on tasks of associative learning. In Study 1, we ran a series of computational simulations to analyze the learning of simple and overlapping stimulus associations in a model of Down syndrome compared with a model of typical development. Learning in the Down syndrome model was slower and more susceptible to interference effects. We found that interference effects could be overcome with dedicated stimulation schedules. In Study 2, we ran a second set of simulations and an empirical study with participants with Down syndrome and typically developing children to test the predictions of our model. The model adequately predicted the performance of the human participants in a serial reaction time task, an implicit learning task that relies on associative learning mechanisms. Critically, typical and atypical behavior was explained by the interactions between neural plasticity constraints and the stimulation schedule. Our model provides a mechanistic account of learning impairments based on these interactions, and a causal link between atypical synaptic plasticity and associative learning.

KW - Neurocomputational model

KW - Down syndrome

KW - LTP/LTD balance

KW - Associative learning

KW - Implicit learning

KW - Serial reaction time task

U2 - 10.1016/j.cognition.2017.10.021

DO - 10.1016/j.cognition.2017.10.021

M3 - Journal article

VL - 171

SP - 15

EP - 24

JO - Cognition

JF - Cognition

SN - 0010-0277

ER -