Angular and Linear Speed Cells in the Parahippocampal Circuits

Abstract

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.

Parahippocampal neurons encode task-relevant information for goal-directed navigation

“…We further found a relationship between navigation variables after clustering that revealed an axis of separation along the head-direction and speed coefficients, such that these variables were negatively related. This anti-correlation between speed and head-direction coding replicated recent observations across parahippocampal regions (MEC, PaS and PrS) [15]. Thus, as a function of speed, the head-direction signal monotonically decreases at faster speeds, consistent with the observation that subjects are less likely to turn when they run fast.…”

“…Although there are differences in the proportion of cells that encode position, head direction and speed between MEC, PrS and PaS (e.g. PrS and PaS have higher proportions of head-direction tuned cells), these regions share a common substrate of spatial/navigational coding (position, head-direction, speed) [6, 15, 16]. We further found a relationship between navigation variables after clustering that revealed an axis of separation along the head-direction and speed coefficients, such that these variables were negatively related.…”

“…Both the MEC and PaS contain large populations of neurons that encode navigationally relevant variables for computing an animals spatial position, including: body position, head direction and running speed. Individual MEC neurons often exhibit a mixture in their selectivity for encoding these variables and, while 50 % of MEC neurons can be classified using tuning curve based metrics, a larger proportion of neurons can be classified using more unbiased statistical approaches [13][14][15]. In the PaS, 50 % of neurons code for heading direction [6,16], with coding for position or running speed present in about 40% neurons [15,16].…”

“…Individual MEC neurons often exhibit a mixture in their selectivity for encoding these variables and, while 50 % of MEC neurons can be classified using tuning curve based metrics, a larger proportion of neurons can be classified using more unbiased statistical approaches [13][14][15]. In the PaS, 50 % of neurons code for heading direction [6,16], with coding for position or running speed present in about 40% neurons [15,16]. MEC has also been shown to contain neurons that encode variables related to non-spatial features of a task or behavior.…”

A Map of Spatial Navigation for Neuroscience

“…Speed cells with a positive linear response to linear and angular speed have also been found throughout the hippocampal formation (Kropff et al, 2015, Spalla et al, 2022. The combination of head direction cells and speed cells could provide a complete representation of the animal's movement through space.…”