Abstract
In this Review, we describe the singular success of attractor neural network models in describing how the brain maintains persistent activity states for working memory, corrects errors and integrates noisy cues. We consider the mechanisms by which simple and forgetful units can organize to collectively generate dynamics on the long timescales required for such computations. We discuss the myriad potential uses of attractor dynamics for computation in the brain, and showcase notable examples of brain systems in which inherently low-dimensional continuous-attractor dynamics have been concretely and rigorously identified. Thus, it is now possible to conclusively state that the brain constructs and uses such systems for computation. Finally, we highlight recent theoretical advances in understanding how the fundamental trade-offs between robustness and capacity and between structure and flexibility can be overcome by reusing and recombining the same set of modular attractors for multiple functions, so they together produce representations that are structurally constrained and robust but exhibit high capacity and are flexible.
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Acknowledgements
I.R.F. acknowledges funding from the Simons Foundation, the Office of Naval Research, the Howard Hughes Medical Institute (HHMI) through the Faculty Scholars Program, the Department of Brain and Cognitive Sciences, MIT, and the McGovern Institute, MIT. M.K. is supported by a Friends of the McGovern Institute Fellowship, a MathWorks Fellowship and the Department of Physics, MIT. The authors thank X. J. Wang for helpful discussion on short-term memory and persistent activity, and K. Daie, the anonymous reviewers, S. Chandra and other members of the Fiete laboratory for helpful comments on the manuscript.
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Glossary
- Associative memory
-
The ability to remember and recall the relationship (association) between arbitrary items or concepts.
- Autonomous
-
Characterized by time evolution through internal dynamics, without external driving forces.
- Eccentricity
-
The degree of deflection of the gaze in the horizontal plane relative to a neutral centred position.
- Error backpropagation
-
A procedure for updating the weights of all layers in artificial neural networks (ANNs) based on gradients of an objective function.
- Euclidean
-
A space where it is possible to construct an orthogonal coordinate system and define a particular metric structure.
- Hippocampal replay
-
Ordered sequences of place cell activity during rest or sleep, typically corresponding to sequences that occurred during normal behaviour or their time-reversed counterparts.
- Homeostatic plasticity
-
Plasticity mechanisms that maintain the state of a system by counteracting induced changes.
- Hopfield networks
-
Content-addressable associative memory networks, in which distributed activity states are stabilized as attractor states by synaptic weights using Hebbian learning.
- Nearest-neighbour computation
-
Identifying the closest target out of a set of target states from any starting state, where closest is usually defined by a standard distance metric (for example, Euclidean or Hamming).
- Nonlinear neurons
-
Neurons with input–output response relationships that are nonlinear; that is, the change in the output is not directly proportional to the change of the input.
- Non-trivial attractor states
-
Any attractor states other than the null activity state.
- Persistent activity
-
Maintenance of the firing rate of a neuron about a non-trivial value after removal of the stimulus that induced elevated firing, for durations that exceed the membrane time constant.
- Positive feedback
-
Interactions between elements in which increasing the level of one element increases the level of the other. Positive feedback includes mutual excitation and disinhibition or inhibition of one’s inhibitor.
- Presynaptic facilitation
-
A form of short-term synaptic plasticity where the effect of presynaptic activity on the post-synaptic response is enhanced following recent presynaptic activity.
- Simple cells
-
Neurons in the primary visual cortex (V1) of many vertebrate species that respond strongly to oriented edges and gratings of a particular spatial phase.
- State space
-
The coordinate system in which each dimension corresponds to one of the variables of the dynamical system; often, the space is approximated by the spike counts of single neurons.
- Synaptic hypothesis
-
The hypothesis that synaptic change is the substrate of learning and memory in the brain.
- Symmetric weight matrices
-
Weight matrices W that satisfy WT = W; that is, that are invariant to reflection of their entries about their diagonal.
- Turing pattern formation
-
A dynamic process dependent on positive feedback in which a spatial pattern of a particular wavelength is amplified whereas others are suppressed.
- Unsupervised
-
Characterization of the structure in data without any prior training data that contains information about the relationship between the data and external variables.
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Khona, M., Fiete, I.R. Attractor and integrator networks in the brain. Nat Rev Neurosci 23, 744–766 (2022). https://doi.org/10.1038/s41583-022-00642-0
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DOI: https://doi.org/10.1038/s41583-022-00642-0
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