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Behav Brain Res. 2019 Dec 30;376:112180. doi: 10.1016/j.bbr.2019.112180. Epub 2019 Aug 28.

Forgetting at biologically realistic levels of neurogenesis in a large-scale hippocampal model.

Author information

1
Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Dept. of Physiology, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada.
2
Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Dept. of Physiology, University of Toronto, Toronto, ON, Canada; Dept. of Psychology, University of Toronto, Toronto, ON, Canada; Institute of Medical Sciences, University of TorontoToronto, ON, Canada; Brain, Mind and Consciousness Program, Canadian Institute for Advanced Research, Toronto, ON, Canada.
3
Mila, Montreal, QC, Canada; School of Computer Science, McGill University, Montreal, QC, Canada; Dept. of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Canadian Institute for Advanced Research, Toronto, ON, Canada.
4
Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Dept. of Physiology, University of Toronto, Toronto, ON, Canada; Dept. of Psychology, University of Toronto, Toronto, ON, Canada; Institute of Medical Sciences, University of TorontoToronto, ON, Canada; Child and Brain Development Program, Canadian Institute for Advanced Research, Toronto, ON, Canada. Electronic address: paul.frankland@sickkids.ca.

Abstract

Neurogenesis persists throughout life in the dentate gyrus region of the mammalian hippocampus. Computational models have established that the addition of neurons degrades existing memories (i.e., produces forgetting). These predictions are supported by empirical observations in rodents, where post-training increases in neurogenesis also promote forgetting of hippocampus-dependent memories. However, in these computational models which use 10-1,000 neurons to represent the dentate gyrus, forgetting is only observed at rates of new neuron addition that greatly exceed adult neurogenesis rates observed in vivo. In order to address this, here we generated an artificial neural network which incorporated more realistic features of the hippocampus - including increased network size (with up to 20,000 dentate gyrus neurons), sparse activity, and sparse connectivity - features that were not present in earlier models. In addition, we explored how properties of new neurons - their connectivity, excitability, and plasticity - impact forgetting using a pattern categorization task. Our results revealed that neurogenic networks forget previously learned input-output pattern associations. This forgetting predicted a performance enhancement in subsequent conflictual learning, compared to static networks (with no added neurons). These effects were especially sensitive to changes in increased output connectivity and excitability of new neurons. Crucially, forgetting was observed at much lower rates of neurogenesis in larger networks, with the addition of as little as 0.2% of the total DG population sufficient to induce forgetting.

KEYWORDS:

Artificial neural network; Forgetting; Neurogenesis

PMID:
31472193
DOI:
10.1016/j.bbr.2019.112180

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