Topic: continuous attractor networks

Continuous attractor networks support various cognitive functions yet the neuronal dynamics and circuits supporting these dynamics in vivo remain unclear. The HD circuit is a canonical example of such network and HD cells each fire for a specific direction of the animal’s head. In the anterodorsal nucleus of the thalamus (ADn), HD cells maintain their mutual coordination during sleep, when sensory inputs are virtually absent, supporting the view of an attractor-driven system. The rigid organization of HD cell activity in the ADN begs the question of the origin of these structured patterns. Th

A central function of continuous attractor networks is encoding coordinates and accurately updating their values through path integration. To do so, these networks produce localized bumps of activity that move coherently in response to velocity inputs. In the brain, continuous attractors are believed to underlie grid cells and head direction cells that maintain periodic representations of position and orientation, respectively. However, these representations can be achieved with any number of activity bumps, and the consequences of having more or fewer bumps are unclear. We construct 1D ring a

Continuous attractor networks are believed to support various cognitive functions, from working memory to spatial representations, yet the neuronal dynamics and circuits supporting these dynamics in vivo remain unclear. One example of such networks is the head-direction (HD) circuit, a crucial signal for navigation. It is represented by HD cells, which each fire for a specific direction of the animal’s head, and is transmitted to the cortex by the anterodorsal nucleus (ADN) of the thalamus where a vast majority of neurons are modulated by HD. ADn HD cells maintain their mutual coordination dur

Grid cells in the medial entorhinal cortex (MEC) of mammalian brains show hexagonal firing fields and are thought to encode the animal’s current location in the environment. As the animal moves, grid cells integrate self-motion cues to track the current position relative to a starting reference location. Recent experiments have identified a class of predictive grid cells that encode the animal’s upcoming location 1,2. Continuous attractor networks (CAN) explain how grid cells develop their characteristic hexagonal firing pattern and enable path integration. But how does predictive coding arise

Grid cells are believed to be key neurons in spatial navigation. Their mechanisms and operation have been described via the theoretical framework of continuous attractor networks (CANs). While increasing evidence points to a CAN mechanism for grid cells, one fundamental aspect of grid cell CAN models remains untested, namely the prediction that population activity is determined by specific patterns of recurrent connectivity. In particular, many CAN models require a Mexican hat connectivity profile with excitatory connections between functionally similar neurons and inhibition between more diss

The entorhinal cortex is a crucial hub for the coding of animals’ experiences in space. Grid cells, key components of this network, exhibit multiple firing fields that tessellate the environment as a function of the animals’ location. At the population level, the dynamics of the grid cell network reside on a toroidal manifold, irrespective of the sensory inputs or behavioral state, in agreement with continuous attractor networks (CAN) theories.However, the developmental origin of toroidal topology remains to be determined. Using Neuropixels 2.0 we obtained high-density population recordings in