Attractor and integrator networks in the brain (2112.03978v3)

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, error-corrects, and integrates noisy cues. We consider the mechanisms by which simple and forgetful units can organize to collectively generate dynamics on the long time-scales 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 look ahead by highlighting recent theoretical advances in understanding how the fundamental tradeoffs 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.

Overview of "Attractor and Integrator Networks in the Brain"

The review paper titled "Attractor and Integrator Networks in the Brain" by Mikail Khona and Ila R. Fiete explores the conceptual framework and empirical evidence supporting the use of attractor neural network models as fundamental components of cognitive computation in the brain. These models are noted for their ability to sustain persistent activity states crucial for functions such as working memory, error correction, and the integration of noisy cues. The paper systematically investigates how relatively simple neuronal units can collectively produce dynamics necessary for complex brain computations over extended timescales.

Defining Attractor Networks

Attractor networks are core constructs of dynamical systems where a minimal set of stable states exist to which nearby states are eventually drawn. Applied to neuroscience, an attractor is realized by stabilized neural activity patterns capable of maintaining these states for computation. The definition adapts mathematical concepts to the neuronal context, emphasizing self-contained systems with autonomous dynamics.

Mechanisms for Attractor Formation

The formation of attractor states in neural circuits is predicated on recurrent positive feedback. The attractor states emerge from structured neural connectivity and synaptic weights determined by associative processes. Examples discussed include Hopfield networks, continuous attractor networks, and nonlinear dynamics generating diverse attractor states such as fixed points, limit cycles, and chaotic attractors.

Computational Utility of Attractors

Attractor networks provide multiple computational benefits essential for brain function:

• Robust Representation and Memory: Attractor states offer stable memory storage by mapping external inputs to internal representations while denoising these representations.
• Classification and Integration: Attractors allow robust decision-making and longer-time scale integration, relevant for sensory processing and evidence accumulation.
• Sequence Generation: Periodic attractor dynamics are leveraged for generating temporal sequences, important in both motor control and cognitive tasks.

Empirical Evidence of Attractors in Neural Systems

Evidence supporting the presence of attractor dynamics in neural circuits has been derived from studies utilizing various recording methodologies:

• Discrete Attractors: Observed in phenomena such as cortical up/down states, perceptual bistability, and neural decision-making processes in areas like the anterior lateral motor cortex.
• Continuous Attractors: Exemplified by the oculomotor integrator, rodent head-direction circuits, and grid cell networks, these systems demonstrate the theoretical predictions of spatially organized and invariant attractor states.

Future Insights and Flexibility

The review emphasizes the need for understanding how attractor networks can maintain robust, low-dimensional representations while allowing for flexibility and adaptation to novel tasks. It suggests that integrator functionality provides a means for rapid representation learning, allowing brain networks to generalize and adapt across different contexts and tasks.

Implications and Future Directions

The implications of this work are multifaceted: Attractor dynamics are established as crucial for cognitive operations in both biological and artificial neural networks. Future research might focus on elucidating how synchronicity in neural firing can coexist with attractor dynamics and exploring the developmental mechanisms that enforce the formation and maintenance of attractors in the brain.

The paper highlights the integral role of attractor networks in bridging theoretical neuroscience with experimental findings and their potential applications to improve our understanding of neural computations underpinning intelligence.