A map of spatial navigation for neuroscience

Parra-Barrero, Eloy; Vijayabaskaran, Sandhiya; Seabrook, Eddie; Wiskott, Laurenz; Cheng, Sen

doi:10.1016/j.neubiorev.2023.105200

Cited by 6 publications

(3 citation statements)

References 486 publications

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“…Animals must make use of several available sources of information and take into account various sensory modalities to navigate: visual, self-motion, odor, tactile, magnetosensory perception, and so on could all be used by animals to navigate their surroundings (Parra-Barrero et al, 2023). The redundancy of information encoded by these input signals enables them to navigate effectively in the face of uncertainty and in changing environments.…”

Section: Introductionmentioning

confidence: 99%

“…While it is clear that these two signals interact, the precise nature of this interaction remains unknown. One question is whether the two signals cooperate or compete — are both signals somehow integrated to determine which direction to move, or is one signal selected over the other based on some criteria (Parra-Barrero et al, 2023)? In the case of the former, it is also unclear how and to what extent each of the signals contributes to the integration, and in the latter, which signal is chosen and how.…”

Section: Introductionmentioning

confidence: 99%

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Competition and Integration of Visual and Goal Vector Signals for Spatial Navigation

Vijayabaskaran,

Cheng

2024

Preprint

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Integrating different sources of information is essential to successful spatial navigation. For instance, animals must often rely on a combination of visual impressions, self-motion, olfaction, and other signals to navigate to a goal. This is especially important when navigating in uncertain environments, where switching from one source of information to another or integrating multiple sources of information may be required to make navigation decisions. We propose a computational model of the interaction of visual and goal-vector signals based on reinforcement learning and use it to study behavior and spatial representations. Our model demonstrates that the ability to navigate using each information source independently, in addition to integrating them, is crucial to successfully navigating in uncertain environments. Counterintuitively, our model also shows that when one of the signals is removed, navigation may be improved if the remaining signal is reliable and sufficient to navigate, however, this improvement comes at the expense of robustness.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Competition and Integration of Visual and Goal Vector Signals for Spatial Navigation

Vijayabaskaran,

Cheng

2024

Preprint

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“…Spatial navigation and reversal learning (RL) are fundamental skills for mobile animals. Spatial navigation involves identifying and maintaining a path between two locations [15]. Research over the past 50 years has identified key brain regions for spatial navigation in the medial temporal lobe and spatially selective cells like place cell (PC) [14], boundary cells (BC) [10], and head direction cells [17].…”

Section: Introductionmentioning

confidence: 99%

The cost of behavioral flexibility: reversal learning driven by a spiking neural network

Ghazinouri,

Cheng

2024

Preprint

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To survive in a changing world, animals often need to suppress an obsolete behavior and acquire a new one. This process is known as reversal learning (RL). The neural mechanisms underlying RL in spatial navigation have received limited attention and it remains unclear what neural mechanisms maintain behavioral flexibility. We extended an existing closed-loop simulator of spatial navigation and learning, based on spiking neural networks (Ghazinouri et al. 2023). The activity of place cells and boundary cells were fed as inputs to action selection neurons, which drove the movement of the agent. When the agent reached the goal, behavior was reinforced with spike-timing-dependent plasticity (STDP) coupled with an eligibility trace which marks synaptic connections for future reward-based updates. The modeled RL task had an ABA design, where the goal was switched between two locations A and B every 10 trials. Agents using symmetric STDP excel initially on finding target A, but fail to find target B after the goal switch, persevering on target A. Using asymmetric STDP, using many small place fields, and injecting short noise pulses to action selection neurons were effective in driving spatial exploration in the absence of rewards, which ultimately led to finding target B. However, this flexibility came at the price of slower learning and lower performance. Our work shows three examples of neural mechanisms that achieve flexibility at the behavioral level, each with different characteristic costs.

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Intelligent Inspection Guidance of Urethral Endoscopy Based on SLAM with Blood Vessel Attentional Features

Lin,

Zeng,

Pan

et al. 2024

Cogn Comput

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A map of spatial navigation for neuroscience

Cited by 6 publications

References 486 publications

Competition and Integration of Visual and Goal Vector Signals for Spatial Navigation

Competition and Integration of Visual and Goal Vector Signals for Spatial Navigation

The cost of behavioral flexibility: reversal learning driven by a spiking neural network

Intelligent Inspection Guidance of Urethral Endoscopy Based on SLAM with Blood Vessel Attentional Features

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