Branch-specific dendritic Ca2+ spikes cause persistent synaptic plasticity

By Joseph Cichon, Wen-Biao Gan. 2015

483 citations recorded by the source corpus.

The brain has an extraordinary capacity for memory storage, but how it stores new information without disrupting previously acquired memories remains unknown. Here we show that different motor learning tasks induce dendritic Ca2+ spikes on different apical tuft branches of individual layer V pyramidal neurons in the mouse motor cortex. These task-related, branch-specific Ca2+ spikes cause long-lasting potentiation of postsynaptic dendritic spines active at the time of spike generation. When somatostatin-expressing interneurons are inactivated, different motor tasks frequently induce Ca2+ spikes on the same branches. On those branches, spines potentiated during one task are depotentiated when they are active seconds before Ca2+ spikes induced by another task. Concomitantly, increased neuronal activity and performance improvement after learning one task are disrupted when another task is learned. These findings indicate that dendritic-branch-specific generation of Ca2+ spikes is crucial for establishing long-lasting synaptic plasticity, thereby facilitating information storage associated with different learning experiences.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

In Natural Language Processing

Citations in the corpus

Overcoming catastrophic forgetting in neural networks (2016, background): “In marked contrast to artiﬁcial neural networks, humans and other animals appear to be able to learn in a continual fashion [Cichon and Gan, 2015].”
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies (2018, background): “A critical feature of biological intelligence is its capacity for life-long learning [10] – the ability to acquire new knowledge from a sequence of experiences to solve progressively more tasks, while maintaining performance on previous ones.”
Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization (2018, background): “In the brain, switching between tasks can disinhibit sparse, highly non-overlapping sets of dendritic branches (Cichon and Gan, 2015).”
Toward an Integration of Deep Learning and Neuroscience (2016, background): “19 Localized activity in dendrites drives localized plasticity, with inhibitory interneurons, and interactions between inputs at diﬀerent parts of the dendritic tree, controlling the local sign and spatial distribution of this plasticity (Sjöström and Häusser, 2006; Cichon and Gan, 2015).”
SupportNet: solving catastrophic forgetting in class incremental learning with support data (2018, background): “After those critical periods, although a certain degree of plasticity would be preserved for the brain reorganization, the rate of synaptic plasticity would decrease so that the previously learned information would be protected against the interference when learning new tasks (Cichon and Gan, 2015).”
Effects of Auxiliary Knowledge on Continual Learning (2022, background): “Human beings and animals are naturally able to memorize information presented in a sequence [1]; on the contrary, Artificial Neural Networks (ANNs) learning from a noni.”
Lifelong Twin Generative Adversarial Networks (2021, background): “An essential characteristic of human beings intelligence is that of being able to continually learn and acquire new skills and concepts from the world throughout their lifespan [1].”
Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex (2015, background): “. We will explain this mechanism more fully in a later section. Apical Synapses Invoke a Top-down Expectation The apical dendrites of a neuron also generate NMDA spikes when they recognize a pattern (Cichon and Gan, 2015). An apical NMDA spike does not directly affect the soma. Instead it can lead t”
Neuroscience-Inspired Artificial Intelligence. (2017, methodology): “This approach can be used to study (Cichon and Gan, 2015; Hayashi-Takagi et al., 2015; Yang et al., 2009).”
MemNet: A Persistent Memory Network for Image Restoration (2017, background): “In essence, recent evidence suggests that mammalian brain may protect previously-acquired knowledge in neocortical circuits [4].”
Continuous Learning of Context-dependent Processing in Neural Networks (2018, background): “In the brain, it has been reported that separation of synapses utilized for different tasks are essential for sequential learning [46], which inspired the algorithms to protect the important weights involved in previously learned tasks while training the network for new ones [31, 33].”