Angular and Linear Speed Cells in the Parahippocampal Circuits

An essential role of the hippocampal region is to integrate information to compute and update representations. How this transpires is highly debated. Many theories hinge on the integration of self-motion signals and the existence of continuous attractor networks (CAN). CAN models hypothesise that neurons coding for navigational correlates – such as position and direction – receive inputs from cells conjunctively coding for position, direction and self-motion. As yet, such conjunctive coding had not been found in the hippocampal region. Here, we report neurons coding for angular and linear velocity, distributed across the medial entorhinal cortex, the presubiculum and the parasubiculum. These self-motion neurons often conjunctively encoded position and/or direction, yet lacked a structured organisation, calling for the revision of current CAN models. These results offer insights as to how linear/angular speed – derivative in time of position/direction – may allow the updating of spatial representations, possibly uncovering a generalised algorithm to update any representation.

Angular and Linear Speed Cells in the Parahippocampal Circuits

An essential role of the hippocampal region is to integrate information to compute and update representations. How this transpires is highly debated. Many theories hinge on the integration of self-motion signals and the existence of continuous attractor networks (CAN). CAN models hypothesise that neurons coding for navigational correlates – such as position and direction – receive inputs from cells conjunctively coding for position, direction and self-motion. As yet, such conjunctive coding had not been found in the hippocampal region. Here, we report neurons coding for angular and linear velocity, distributed across the medial entorhinal cortex, the presubiculum and the parasubiculum. These self-motion neurons often conjunctively encoded position and/or direction, yet lacked a structured organisation, calling for the revision of current CAN models. These results offer insights as to how linear/angular speed – derivative in time of position/direction – may allow the updating of spatial representations, possibly uncovering a generalised algorithm to update any representation.

Design of Continuous Attractor Networks with Monotonic Tuning Using a Symmetry Principle

Neurons that sustain elevated firing in the absence of stimuli have been found in many neural systems. In graded persistent activity, neurons can sustain firing at many levels, suggesting a widely found type of network dynamics in which networks can relax to any one of a continuum of stationary states. The reproduction of these findings in model networks of nonlinear neurons has turned out to be nontrivial. A particularly insightful model has been the “bump attractor,” in which a continuous attractor emerges through an underlying symmetry in the network connectivity matrix. This model, however, cannot account for data in which the persistent firing of neurons is a monotonic—rather than a bell-shaped—function of a stored variable. Here, we show that the symmetry used in the bump attractor network can be employed to create a whole family of continuous attractor networks, including those with monotonic tuning. Our design is based on tuning the external inputs to networks that have a connectivity matrix with Toeplitz symmetry. In particular, we provide a complete analytical solution of a line attractor network with monotonic tuning and show that for many other networks, the numerical tuning of synaptic weights reduces to the computation of a single parameter.