Spatial navigation

Predicting neural activity in higher visual areas (HVAs) in mice during spatial navigation is challenging due to the complex interaction between spatial and temporal processing. One recent two-stream hypothesis for rodents proposed by Saleem (2020) suggests that form-based goals and detailed landmark signals are processed by cortical areas biased towards the central visual field, while self-motion signals are processed in the periphery. Inspired by this, we developed a deep learning model that splits visual input into two streams: a convolutional neural network (CNN) processed the central visu

…in episodic memory processing and spatial navigation. Especially, the egocentric neural representation of space, which is important for constructing an egocentric cognitive map, is essential for spatial navigation. However, how spatial navigation deficits relate to egocentric neural representation of space remains unclear. The retrosplenial cortex (RSC) is one of the regions affected by amyloidosis in the early stages of AD and is well known for its role in egocentric spatial representation. Specifically, it cont

Navigation is inherently dynamic. It involves learning the environment, as well as positions in and trajectories through it, and then executing a path to reach a target. Spatial navigation skills vary significantly among individuals. But what differentiates a good navigator from a bad one, or an easy-to-navigate path from a hard one? Studies have analysed exploration and navigation using static quantitative measures, e.g., location tallies or distance traveled. However, static metrics are inherently limited for characterisation of dynamic behaviours. To fill this gap, we introduce the Flow Tre

…To understand how encoding spatial information of multiple agents shapes neural representations, we train

…of memory via the paradigm of spatial navigation. To interpret neural responses, it is common practice to first build encoding models using position during locomotion as the label, and then decode the position using this model. Of particular interest is the mismatch between the decoded and the actual position, for instance, replay of non-local trajectories during rest. However, it is unclear whether a priori chosen labels explain neural data the best. By imposing spatial structure to times of immobility, we may be systematically mis

…neural circuit necessary for effective spatial orientation, and includes regions such as the anterodorsal thalamic nucleus (ADN). It maintains a sense of orientation by integrating self-motion with local cues, which serve as anchors. A recent study in mice showed that network gain, a measure of population activity in the system, decreases during reorientations induced by changes in visual cues. The extent of this reduction in gain influences the dynamics of reorientation, with lowe

High-dimensional neural activity in the brain is known to encode low-dimensional, time-evolving, behaviour-related latent variables. A fundamental goal of neural data analysis consists of identifying such variables and their mapping to neural activity. The canonical approach is to assume the latent variables are behaviour (e.g. the measured location of the animal) and visualise the subsequent tuning curves. However, significant mismatches between behaviour and the encoded variables may still exist---the agent may be thinking of another location, or be uncertain of its own---distorting the tuni

Spatial navigation relies on external cues, such as visual objects, to anchor internal representations of space. In rodents, moving a visual landmark results in a corresponding shift in the tuning of head direction (HD) cells1. However, little is known about where visual objects are encoded in the mouse brain2 or how visual objects modulate the firing properties of HD cells3. Using functional ultrasound (fUS) imaging4, we conducted a brain-wide screen to identify areas preferentially activated by visual objects compared to scrambled versions of the same stimuli. Our results showed that while v

The brain needs to process high-dimensional sensory information in order to control the non-linear dynamics of body movements. One view posits that it does so modularly with different brain regions solving specific and distinct objectives that can be studied quasi-independently (e.g. visual classification in the ventral visual stream or path integration in entorhinal-hippocampal circuit). However, it is unclear how these modular abstract objectives would emerge and how they give rise to coherent behavior executed through concrete actions. An alternative paradigm proposes that the brain holisti

In this webinar on spatial navigation circuits, three researchers—Ann Hermundstad, Ila Fiete, and Barbara Webb—discussed how diverse species solve navigation problems using specialized yet evolutionarily conserved brain structures. Hermundstad illustrated the fruit fly’s central complex, focusing on how hardwired circuit motifs (e.g., sinusoidal steering curves) enable rapid, flexible learning of goal-directed navigation. This framework combines internal heading representations with modifiable goal signals, leveraging activity-dependent plasticity to adapt to new environments. Fiete explored t

…The neural mechanisms underlying RL in spatial navigation has received limited attention and it remains unclear what neural mechanisms maintain behavioral flexibility. To address this issue, we extended an existing closed-loop simulator of spatial navigation and learning [1], based on spiking neural networks. The activity of place cells and boundary cells were fed as inputs to action selection neurons, which

The hippocampus is presumably involved in spatial navigation, the formation of long-term memories in mammals, and the construction of a cognitive map of space using a prominent feature – place cells [1] (PCs). It is unclear how PCs’ neural codes are related to memory: turnover of neural representations could be involved in learning, while codes’ stability may support long-term storage and retrieval of memories [2]. In this work, we focus on the analysis of one-photon (1P) calcium imaging data recorded from mouse CA1 during free foraging in a two-dimensional arena with the help of a miniature

The hippocampus (HC) plays a pivotal role in spatial navigation and memory in the human brain. Place cells, specialised neurons residing within the hippocampus, exhibit selective firing patterns corresponding to specific spatial locations, collectively contributing to the cognitive mapping of the environment. In healthy state, the place fields in the CA1 region exhibits continuous representational turnover [1]. Spatial navigation is disrupted in Alzheimer’s Disease (AD). AD, a neurodegenerative disorder characterised by progressive cognitive decline, is associated with deficits in learning a

Experimental studies have demonstrated that spontaneous task-independent replay of sequences encoding a learned route during sharp-wave–ripple (SWR) events can be beneficial for consolidating path memory [1] and discovering new behavioral strategies, such as forming novel space-action representations, finding shortcuts, and integrating and associating different routes[2]. Additionally, It has been shown experimentally that hippocampal theta sequences, generated during active exploration of the environment, play a crucial role in selecting appropriate actions in goal-directed tasks[3,4]. Howeve

Coherent oscillations of neuronal activity are ubiquitous across brain spatial and temporal scales. Gamma oscillations (30-120Hz), detectable with Local Field Potentials (LFPs), are classically analyzed through massive trial averages. This approach leaves us with one average oscillation shape, one mean power and a view based on rigid frequency bands. Consistently, mechanisms based on biophysical models of neural networks typically predict periodic oscillations (e.g. PING models [1]). The functional role of oscillations have been largely investigated in the hippocampus, where, different gamma-f

…to perform computations in support of spatial navigation. As direct recordings of grid cells from the human brain are only rarely possible, functional magnetic resonance imaging (fMRI) studies proposed and described an indirect measure of entorhinal grid-cell activity, which is quantified as a hexadirectional modulation (HM) of fMRI activity as a function of the subject’s movement direction through a virtual environment. However, the contributing role of the aggregated activity of grid cells to this modulation remains unclear. Ou

…confirms such strategic use during spatial navigation. Despite this, leading neural models for navigation ignore such uncertainty. I will focus on two examples for revising these models. First, I will show that common ring attractor architectures are able to track uncertainty in heading direction only if they are tuned to feature slow dynamics. Second, I will show that approximate computations that only partially track uncertainty are sufficient to perform cl

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

…thought of as instrumental in spatial navigation because of their periodic firing characteristics as the animal traverses its surroundings. In recent years, converging bodies of evidence from etiological and teleological investigations of grid cells indicate that they arise under efficient coding schemes, with metabolic, sparsity, and nonnegativity constraints supporting the formation of regular lattices that approximate what has been observed in animal experiments. Here we import the notion of local a

Cognitive decline after multiple brain-wide microstrokes is reported regularly in patients diagnosed with vascular dementia. However, the neuropathological mechanisms linking microstrokes to cognitive decline remain unclear, with no specific treatment options available. We present a mouse model to study hippocampal memory circuits in the healthy condition and after induction of cerebral microstrokes via microsphere injections. Mice are trained to navigate a virtual corridor while recording the same hippocampal neurons via chronic 2-photon calcium imaging before and after stroke for up to six w

Spatial navigation is normally yoked to the motor actions responsible for travel. Neural populations in CA1 exert influence on navigational outcomes, but this coupling makes it challenging to isolate abstract representations of travel from concrete motor actions. In this work, we present an experimental paradigm that uses a hippocampal closed-loop Brain-Machine Interface (BMI) to decouple running locomotion from the cognitive map. This BMI associates neural activity with locations on a 1D Virtual Reality (VR) track, and then translates those locations into contextually appropriate actions (Fig

The hippocampus is essential for spatial navigation and memory processes. Hippocampal pyramidal cells (PCs) often exhibit complex spike bursts (CSBs) that are driven by regenerative dendritic Ca2+ plateau potentials. These events can induce rapid synaptic plasticity and new place field (PF) formation in CA1PCs. CA3PCs also fire CSBs, and their PF-inducing CSBs were shown to be particularly prolonged (Li et al, bioRxiv). However, the dendritic mechanisms underlying such long-lasting events in CA3PCs remain unclear. In acute rat brain slices, we observed large heterogeneity among CA3PCs in CSB p

Decision-making occurring​ during navigation and learning is widely studied in choice behaviors, but less understood in natural and more continuous settings, especially under stress and uncertainty. This process could be investigated in rodent spatial navigation, which has been modeled with place-cell-based models. However, traditional models usually ignore detailed trajectories or kinematics. Here we extended a place cell-based reinforcement learning model to include detailed kinematics and used it to investigate the role of motivational stress. We performed experiments with two strains of mi

Mice are territorial animals whose spatial navigation ability relies on integrating multiple stimuli from various sensory modalities. Among all these types of information, the pattern of major urinary proteins contained in the urine landmarks provide the identity of the conspecific owner of each territory, constituting the “who” component of episodic memory. We identified a neuroanatomical pathway responsible for integrating individual recognition information into the complex socio-spatial memories stored within the hippocampus. Within this pathway, the posteromedial cortical amygdaloid nucleu