🌻
Models
  • Step by step intro
  • Bash
  • Git
    • Remove folder
  • Embedding
    • Normalize Input
    • One-hot
  • Hyperparameter tuning
    • Test vs Validation
    • Bias vs Variance
    • Input
      • Normalize input
      • Initialize weight
    • Hidden Layer
      • Hidden layer size
    • Learning Rate
      • Oscillate learning rate
      • Learning rate finder
    • Batch Size
    • Epoch
    • Gradient
      • Vanishing / Exploding Gradients
      • Gradient Checking
    • Cost Function
      • Loss Binary Cross Entropy
    • Regularization
      • Lā‚‚ regularization
      • L₁ regularization
      • Dropout regularization
      • Data augmentation
      • Early stopping
  • Fine-tuning
    • Re-train on new data
    • Freeze layer/weight
  • Common Graphing Stats
    • Confidence interval (CI) and error bar
    • Confusion matrix and type I type II error
    • Effect size
  • Models
    • Inverted Pendulum Model
    • Recurrent Neural Networks
      • GRU and LSTM
      • Neural Turing Machines
    • Hopfield
    • Attention
      • Re-attention
      • Enformer
    • Differential Equations
      • Ordinary Differential Equations
        • Language Ordinary Differential Equations (ODE)
        • Neural Ordinary Differential Equations (ODE)
          • Adjoint Sensitive Method
          • Continuous Backpropagation
          • Adjoint ODE
      • Partial Differential Equations
      • Stochastic Differential Equations
    • Knowledge Tracing Models
      • Bayesian Knowledge Tracing
    • trRosetta
    • Curve up grades
  • deeplearning.ai
    • Neural Networks and Deep Learning
      • Wk2 - Python Basics with Numpy
      • Wk2 - Logistic Regression with a Neural Network mindset
      • Wk3 - Planar data classification with a hidden layer
      • Wk4 - Building your Deep Neural Network: Step by Step
      • Wk4 - Deep Neural Network - Application
    • Hyperparameter Tuning, Regularization and Optimization
      • Wk1 - Initialization
      • Wk1 - Regularization
      • Wk1 - Gradient Checking
    • Structuring Machine Learning Projects
    • Convolutional Neural Networks
    • Sequence Models
  • Neuroscience Paper
    • Rotation and Head Direction
    • Computational Models of Memory Search
    • Bayesian Delta-Rule Model Explains the Dynamics of Belief Updating
    • Sensory uncertainty and spatial decisions
    • A Neural Implementation of the Kalman Filter
    • Place cells, spatial maps and the population code for memory (Hopfield)
    • Spatial Cognitive Map
    • Event Perception and Memory
    • Interplay of Hippocampus and Prefrontal Cortex in Memory
    • The Molecular and Systems Biology of Memory
    • Reconsidering the Evidence for Learning in Single Cells
    • Single Cortical Neurons as Deep Artificial Neural Networks
    • Magnetic resonance-based eye tracking using deep neural networks
Powered by GitBook
On this page
  • Stimulus:
  • Maps Are Modality Dependent or Independent
  • Remaining Questions

Was this helpful?

  1. Neuroscience Paper

Spatial Cognitive Map

PreviousPlace cells, spatial maps and the population code for memory (Hopfield)NextEvent Perception and Memory

Last updated 3 years ago

Was this helpful?

Real world navigation requires movement of the body through space, produced a continuous stream of visual and self-motion signals, including proprioceptive, vestibular and motor efference cues. These multimodal cues are integrated to form a spatial cognitive map.

visual + idiothetic (body-based / self-motion / internal cues vs external landmarks)

vestibular cues in rodent require

  1. place cell

  2. grid cell

  3. head direction cell

Stimulus:

paradigm type
stimulus
input
brain scan

real world

just real world navigation

visual, self-movement

no fMRI

naturalistic

video of real world

visual

fMRI

naturalistic

head-mounted VR

visual, self-movement

fMRI

lab

desktop computer VR

visual

fMRI

PS. orientation task (not moving body thru space) vs navigation task (moving body thru space)

Tasks should design to encourage the use of body-based cue to dissociate the tasks on behavioural level

Neural representation of the map can only be inferred during recall not encoding as participant as immobile during fMRI scan -> how do body-based cues affect formation of cognitive map

Maps Are Modality Dependent or Independent

modality independent spatial representation during judging relative direction (JRD) task

Spatial navigation tasks

  1. perceived spatial orientation

  2. spatial manipulation of 3D object

  3. distance estimation

  4. navigation

Spatial navigation process:

  1. perception of one's spatial orientation relative to the surrounding environment

  2. computation of a route to a goal

  3. implementation of that route based on one's current location and directional heading

Remaining Questions

  1. how are body-based cues integrate with visual cues

  2. which cue to encode when there is a conflict

  3. how do body-based cues and visual cues update to correct errors, what brain region signal that an error has occurred such as misorientation

  4. how to measure head direction cell activation in scanner

  5. how are multiple reference frames maintained using VR vs real world cue (eg., the task room)

Huffman and Ekstrom
with / without moving body thru space