Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal to the beautiful recordings of today, neuroscientists have been striving to unravel the mysteries of these structures. Theoretical work in the 1960s predicted important dendritic effects on neuronal processing, establishing computational modelling as a powerful technique for their investigation. Since then, modelling of dendrites has been instrumental in driving neuroscience research in a targeted manner, providing experimentally testable predictions that range from the subcellular level to the systems level, and their relevance extends to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modelling predictions often requires — and drives — new technological advances, thus closing the loop with theory-driven experimentation that moves the field forward. This Review features the most important, to our understanding, contributions of modelling of dendritic computations, including those pending experimental verification, and highlights studies of successful interactions between the modelling and experimental neuroscience communities.
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The authors thank A. Losonczy, N. Takahashi, E. Froudarakis and members of the Poirazi laboratory for critical reading of the manuscript. This work was supported by the Alexander von Humboldt-Stiftung (P.P.), the European Commission FET Open grant (NEUREKA, 863245) (P.P.), and the Brain & Behavior Research Foundation NARSAD Young Investigator Award (27606) (A.P.).
The authors declare no competing interests.
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- Coincidence detectors
Parts of a neuron and/or neural circuit that show a supralinear increase of response upon coincident arrival of different input pathways.
- Artificial neural networks
(ANNs). Versatile networks with weighted, directed connections organized in layers. ANNs are mathematical models capable of learning and are used mostly for classification tasks.
- Ionic channels
Protein structures that span the cell membrane, enabling the (selective) passage of ions from one side of the membrane to the other through the channel pore.
In neurophysiology, the active regeneration of somatic action potentials travelling backwards into the dendrites.
An inward current generated by the opening of hyperpolarization-activated cyclic nucleotide-gated cation channels; critical for synaptic integration and plasticity.
- Point neuron view
Consideration of a neuron as a summation unit with a non-linear activation function and no internal (dendritic) morphology.
- Active dendrites
Dendrites equipped with voltage-dependent ionic conductances.
- Fast-spiking (FS) basket cells
Inhibitory neurons characterized by brief and high-frequency action potentials. Usually, FS basket cells innervate the perisomatic region of pyramidal neurons and other interneurons.
- Synaptic democracy
Location independence of the efficacy of synaptic inputs in evoking somatic depolarization and/or action potentials.
- Place cell
A type of neuron mostly found in the hippocampus that fires at a high rate whenever an animal enters a particular location (place field) within its environment.
- Global dendritic spikes
Non-linear depolarizations generated en masse in the dendrites of neurons, usually in response to dispersed input.
- AP initiation zones
Specialized domains of a neuron enriched with sodium and potassium channels, where propagated synaptic potentials are summated and an action potential (AP) is initiated.
- Inhibitory shunt
Activation of an inhibitory synapse that adds a conductance value to the membrane. This reduces input resistance and thus has a divisive effect on excitatory inputs.
- Associative memory engrams
Memory traces that consist of different types of information that become bound together, possibly through their storage in common neurons.
- Linear–non-linear (LN) models
Phenomenological models in which the outputs are estimated by successively applying linear temporal filters to the inputs, followed by static non-linear transformations.
Binary classification algorithms each consisting of weighted inputs, a bias and a thresholding function that generates an output decision.
- Predictive coding
The comparison of sensory inputs with prior expectations (to create a ‘prediction’), and propagation of an ‘error’ signal to the brain areas responsible for the expectations.
- Short-term depression
(STD). A negative change in postsynaptic potentials following repetitive stimulation of a synapse.
- Short-term facilitation
(STF). A positive change in postsynaptic potentials following repetitive stimulation of a synapse.
- Field potentials
Extracellular measurements of the activity of a population of neurons, reflecting neuronal transmembrane currents that are mainly due to synaptic activity.
- Pattern separation
The process that minimizes the overlap between the neuronal populations that encode for similar input patterns.
- Pattern completion
The process during which a learned pattern is recalled upon presentation of a degraded or partial version of the original stimulus.
- Theta-cycle phase precession
The advancement of spike timing of a particular place cell to earlier phases of the theta cycle as the animal passes through its place field.
- Spike timing-dependent plasticity
A type of Hebbian learning where plasticity is regulated by the relative timing of the presynaptic and postsynaptic action potentials.
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Poirazi, P., Papoutsi, A. Illuminating dendritic function with computational models. Nat Rev Neurosci 21, 303–321 (2020). https://doi.org/10.1038/s41583-020-0301-7
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