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Rescorla-Wagner (RW) model how does it handle the concept of time within a single trial
RW treats the entire trial as a single event, making one update at the end of the trial by comparing the final outcome to the total prediction. It has no concept of time within the trial.
Temporal Difference (TD) learning how does it handle the concept of time within a single trial
TD learning slices the trial into small time steps, computing a prediction error δt at every step.
Explain the “credit assignment problem” in operant learning
The reward arrives seconds after the action was chosen and executed. By the time the dopaminergic teaching signal arrives, the neural activity that caused the action has already passed, making it difficult to know which synapse to update.
How does the biological mechanism of an eligibility trace, combined with a dopaminergic teaching signal, resolve the credit assignment problem
Eligibility Trace: When a cortical input contributes to a striatal spike, the active synapse is temporarily chemically “tagged”. This tag slowly decays over about one second.
Resolution: If a dopamine burst δ(t) arrives while the eligibility tag is still active, only the tagged synapses undergo plasticity (weight change). Untagged synapses are unaffected, linking the delayed reward directly to the recent action.
egocentric spatial reference frames
Positions objects relative to the observer’s own body axes (e.g., ahead, to my right). Firing shifts as the observer moves or rotates
allocentric spatial reference frames
Positions objects relative to the external environment or fixed landmarks independent of the observer’s orientation (e.g., north, near the wall).
Egocentric neural cell populations
Egocentric boundary
item cells or parietal
retinotopic neurons (located in parietal cortex)
Allocentric neural cell populations
Place cells (hippocampus) or Grid cells
Boundary Vector Cells (medial entorhinal cortex) encode world-centered positions or metric layouts.
Spatial coding properties and functional roles of hippocampal Place Cells
Fire at single specific localized regions (place fields) in a given environment, serving as a specific cognitive map node.
Spatial coding properties and functional roles of Medial Entorhinal Grid Cells
Fire at periodic, triangular/hexagonal tessellating arrays across an environment, providing a metric coordinate system for path integration.
Spatial coding properties and functional roles of Boundary Vector Cells (BVCs)
Fire whenever an environmental barrier or boundary is located at a specific distance and allocentric direction from the animal.
Computational model by which input signals from Boundary Vector Cells (BVCs) interact to form and maintain a hippocampal Place Cell (PC) receptive field
Boundary Vector Cells supply sensory input regarding the distance and allocentric direction to physical environmental boundaries, anchoring the place field geometrically.
Computational model by which input signals from Grid Cells (GCs) interact to form and maintain a hippocampal Place Cell (PC) receptive field
Grid Cells provide metric spatial coordinate inputs derived from path integration (self-motion), supplying continuous distance and direction updates across open space.
Explain how the computational model by which input signals from Boundary Vector Cells (BVCs) and Grid Cells (GCs) interact to form and maintain a hippocampal Place Cell (PC) receptive field allow the visual/spatial representation of a place field to combine both environmental boundary information and self-motion path integration.
Summation & Thresholding: The hippocampal Place Cell combines the convergent inputs from BVCs and GCs; firing occurs when the combined signal crosses a activation threshold, defining a single localized place field.
Complementary Robustness: Combining environmental boundary cues with self-motion path integration allows the place field to maintain accurate spatial firing even in dark/degraded sensory conditions (via grid cells) while remaining bound to physical boundary changes (via BVCs).