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From the neuron doctrine to neural networks

Abstract

For over a century, the neuron doctrine — which states that the neuron is the structural and functional unit of the nervous system — has provided a conceptual foundation for neuroscience. This viewpoint reflects its origins in a time when the use of single-neuron anatomical and physiological techniques was prominent. However, newer multineuronal recording methods have revealed that ensembles of neurons, rather than individual cells, can form physiological units and generate emergent functional properties and states. As a new paradigm for neuroscience, neural network models have the potential to incorporate knowledge acquired with single-neuron approaches to help us understand how emergent functional states generate behaviour, cognition and mental disease.

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Figure 1: Historical evolution of the neuron doctrine and neural network models.
Figure 2: Anatomical and physiological examples of the neuron doctrine.
Figure 3: Spontaneous cortical activity.
Figure 4: Neural networks.
Figure 5: Emergent functional states in multineuronal dynamics during virtual navigation.

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Acknowledgements

The author thanks L. Abbott, J. Cunningham, A. Fairhall and members of the laboratory for their comments, and G.M. Shepherd for long lasting inspiration. Supported by DP1EY024503 and DARPA contract N66001-15-C-4032. This material is based on work fully or partly supported by the US Army Research Laboratory and the US Army Research Office under contract number W911NF-12-1-0594 (MURI).

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Glossary

Attractors

Stable or semi-stable states in the temporal dynamics of the activity of a neuronal population. They arise naturally in neural networks that have a recurrent (feedback) architecture with symmetric connections.

Boolean logic

A form of algebra in which all values are reduced to either true or false. Boolean logic is especially important for computer science because it fits nicely with its binary numbering system. Boolean logic depends on the use of three logical operators: AND, OR and NOT.

BRAIN initiative

The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative is a decade-long large-scale scientific project, sponsored by the White House, to accelerate the development and application of innovative neurotechnologies to revolutionize the understanding of the brain.

Activity map

In a neural network context, the activity map is a three-dimensional representation of all the activity states of the network, where the depth dimension corresponds to the energy function of the activity, which captures the propensity of the network activity to change. This topological representation provides an intuition of how the activity of the circuit evolves in time, as it progresses through this energy landscape to find its lower-energy (attractor) points.

Ensembles

A group of neurons that show spatiotemporal co-activation. Ensembles provide an example of an emergent state of the circuit.

Gap junctions

Cellular specializations that allow the non-selective passage of small molecules between the cytoplasm of adjacent cells. They are formed by channels termed connexons, which are multimeric complexes of proteins known as connexins. Gap junctions are structural elements of electrical synapses.

Golgi stain

A staining technique introduced by Camillo Golgi in 1873 that involves impregnating the tissue with silver nitrate. This labels a random subset of neurons, allowing the entire cell and its processes to be visualized.

Grid cells

Neurons in the rodent entorhinal cortex that fire when the animal is at one of several specific locations in an environment; these locations are organized in a grid-like manner.

Learning rule

The alteration of the strength of a synaptic connection in a neural network, as a consequence of the pattern of activity experienced by that synapse (or the network).

Neuronal assemblies

Originally proposed by Hebb; groups of neurons that become bound together owing to synaptic plasticity, and whose coordinated activity progresses through the circuits, often in a closed loop.

Pattern completion

A process by which a stored neural representation is reactivated by a cue that consists of a subset of that representation.

Pattern separation

A process by which overlapping neural representations are separated to keep episodes independent of each other in memory.

Perceptrons

Multilayer feedforward artificial neural networks in which activity flows unidirectonally from one layer to the next. Multilayer perceptrons are often used to implement classification problems.

Place cells

Hippocampal neurons that specifically respond to stimuli in certain spatial locations. Their firing rate increases when an animal or subject approaches the respective location.

Recurrent connectivity

The concept that neurons within a class connect with one another, implying feedback communication within the network.

Replay

Recapitulation of experience-dependent patterns of neural activity previously observed during awake periods.

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Yuste, R. From the neuron doctrine to neural networks. Nat Rev Neurosci 16, 487–497 (2015). https://doi.org/10.1038/nrn3962

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