Hippocampal place cells of freely moving rodents display an intriguing temporal organization in their responses known as `theta phase precession', in which individual neurons fire at progressively earlier phases in successive theta cycles as the animal traverses the place fields. Recent experimental studies found that in addition to phase precession, many place cells also exhibit accompanied phase procession, but the underlying neural mechanism remains unclear. Here, we propose a neural circuit model to elucidate the generation of both kinds of phase shift in place cells' firing. Specifically, we consider a continuous attractor neural network (CANN) with feedback inhibition, which is inspired by the reciprocal interaction between the hippocampus and the medial septum. The feedback inhibition induces intrinsic mobility of the CANN which competes with the extrinsic mobility arising from the external drive.

Rodent studies have revealed two prominent firing features of hippocampal place cells during animals'

Hippocampal place cells of freely moving rodents display an intriguing temporal organization in their responses known as theta phase precession', in which individual neurons fire at progressively earlier phases in successive theta cycles as the animal traverses the place fields. Recent experimental studies found that in addition to phase precession, many place cells also exhibit accompanied phase procession, but the underlying neural mechanism remains unclear. Here, we propose a neural circuit model to elucidate the generation of both kinds of phase shift in place cells' firing. Specifically, we consider a continuous attractor neural network (CANN) with feedback inhibition, which is inspired by the reciprocal interaction between the hippocampus and the medial septum. The feedback inhibition induces intrinsic mobility of the CANN which competes with the extrinsic mobility arising from the external drive.

Spatial cognition in hippocampal formation is posited to play a crucial role in the development of self-localization techniques for robots. In this paper, we propose a self-localization approach, DEQ-MCL, based on the discrete event queue hypothesis associated with phase precession within the hippocampal formation. Our method effectively estimates the posterior distribution of states, encompassing both past, present, and future states that are organized as a queue. This approach enables the smoothing of the posterior distribution of past states using current observations and the weighting of the joint distribution by considering the feasibility of future states. Our findings indicate that the proposed method holds promise for augmenting self-localization performance in indoor environments.

We like to think of brains as computers: A physical system that processes inputs and spits out outputs. But, obviously, what's between your ears bears little resemblance to your laptop. Computer scientists know the intimate details of how computers store and process information because they design and build them. But neuroscientists didn't build brains, which makes them a bit like a piece of alien technology they've found and are trying to reverse engineer. At this point, researchers have catalogued the components fairly well.

Today, when researchers spend long hours in the lab performing tricky experiments, they might listen to music or podcasts to get them through the day. But in the early years of neuroscience, hearing was an essential part of the process. To figure out what neurons cared about, researchers would translate the near-instantaneous signals they send, called "spikes," into sound. The louder the sound, the more often the neuron was spiking--and the higher its firing rate. "You can just hear how many pops are coming out of the speaker, and if it's really loud or really quiet," says Joshua Jacobs, associate professor of biomedical engineering at Columbia University.