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Spine dynamics in the brain, mental disorders and artificial neural networks

Abstract

In the brain, most synapses are formed on minute protrusions known as dendritic spines. Unlike their artificial intelligence counterparts, spines are not merely tuneable memory elements: they also embody algorithms that implement the brain’s ability to learn from experience and cope with new challenges. Importantly, they exhibit structural dynamics that depend on activity, excitatory input and inhibitory input (synaptic plasticity or ‘extrinsic’ dynamics) and dynamics independent of activity (‘intrinsic’ dynamics), both of which are subject to neuromodulatory influences and reinforcers such as dopamine. Here we succinctly review extrinsic and intrinsic dynamics, compare these with parallels in machine learning where they exist, describe the importance of intrinsic dynamics for memory management and adaptation, and speculate on how disruption of extrinsic and intrinsic dynamics may give rise to mental disorders. Throughout, we also highlight algorithmic features of spine dynamics that may be relevant to future artificial intelligence developments.

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Fig. 1: Enlargement and shrinkage of dendritic spines.
Fig. 2: Reinforcement plasticity of dendritic spines in D1 and D2 neurons.
Fig. 3: Intrinsic dynamics and rewiring of dendritic spines.

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Acknowledgements

The authors thank D. Soudry, N. Brenner, R. Meir, O. Barak, Y. Loewenstein, S. Rumpel, S. Ishii and S. Koike for helpful discussions. This work was supported by Grants-in-Aid (20H05685 and 26221001 to H.K., JP18H05432 to T.T. and 19K16249, 16H06395, 16H06396 461 and 16K21720 to S.Y.) from the Japan Society for the Promotion of Science; the World Premier International Research Center Initiative from the Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT); Core Research for Evolutional Science and Technology (JPMJCR1652 to H.K.) from the Japan Science and Technology Agency; the Strategic Research Program for Brain Sciences (JP20dm0107120 to H.K.); Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) (21dm0207069h0001 to S.Y. and JP21dm0207001 to TT) from the Japan Agency for Medical Research and Development; and the Israel Science Foundation (1470/18) and the State of Lower-Saxony and the Volkswagen Foundation (N.E.Z).

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All authors researched data for the article, contributed substantially to discussions concerning its contents and reviewed and/or edited the manuscript before submission. H.K., N.E.Z. and T.T. wrote the article.

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Correspondence to Haruo Kasai.

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Glossary

Spike timing-dependent plasticity

(STDP). Adjustments of connection strengths based on the relative timing of the output of a particular neuron and input spikes.

Filopodia

Thin transient protrusions that act as ‘feelers’ that allow cells to probe their surrounding environment. Can occasionally give rise to dendritic spines.

Integrins

Transmembrane molecules that facilitate cell–cell and cell–extracellular matrix adhesion by connecting stress fibres and other intracellular actin structures to the extracellular matrix.

Metaplasticity

The plasticity of synaptic plasticity.

Nanodomains

The cytosolic domains within about 10 nm of the open pore of Ca2+ channels or NMDA receptors where Ca2+ concentrations can readily exceed 10 μM.

Shunting inhibition

A predominant form of GABAergic inhibition that depends on increases in the membrane conductance but not necessarily on hyperpolarization.

Critical periods

Periods during development in which a particular skill or characteristic is believed to be most readily acquired.

Operant conditioning

A form of learning that uses rewards and punishments for enforcing behaviour. Sometimes called ‘instrumental conditioning’.

Eligibility trace

A temporary record of the occurrence of an event which marks the memory parameters associated with the event as eligible for undergoing learning changes.

Salience

The quality of being particularly noticeable or important.

Drift

The averaged change of a parameter in a certain period. In the general case, the drift, μ(w), is dependent on the current value of the parameter w.

Diffusion

The standard deviation of a parameter in a certain period. In the general case, the diffusion, σ(w), is dependent on the current value of the parameter w.

Bit

A binary digit. The smallest unit of measurement used to quantify computer data.

Working memories

Information stored in an accessible state for use in complex mental tasks.

Brownian motion

Random movement of microscopic particles suspended in liquids resulting from the effect of molecules of the surrounding medium.

Ornstein–Uhlenbeck process

A type of stochastic process whose stationary distribution is normal (Gaussian).

Black–Scholes model

The most popular stochastic differential equation in financial economics to estimate the changing value of an option over time.

Gradient descent

An optimization algorithm for finding a local minimum of a differentiable function.

Overfitting

The fitting that corresponds too closely to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably.

Search space

The space of all feasible solutions, among which the desired solution resides.

Bayesian network inference

Use of a Bayesian network to estimate the probability that a hypothesis is true based on evidence.

Initialization

The assignment of initial values to parameters, such as synaptic weights in the context of artificial neural networks.

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Kasai, H., Ziv, N.E., Okazaki, H. et al. Spine dynamics in the brain, mental disorders and artificial neural networks. Nat Rev Neurosci 22, 407–422 (2021). https://doi.org/10.1038/s41583-021-00467-3

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