How should I practice Stateful Neuron Matrix Update?

Start by reading the full statement, solve the visible examples, then compare the pattern against related Scale AI onsite interview problems in FastPrep.

Is Stateful Neuron Matrix Update associated with Scale AI?

FastPrep tags this as a Scale AI onsite interview coding practice problem when the problem catalog has that company and stage context.

Stateful Neuron Matrix Update

Scale AI● MediumNEW GRADONSITE INTERVIEW

Learn

Problem statement

A matrix cell with value 0 is a firing neuron; a positive value is non-firing. Update every cell simultaneously using its up to eight surrounding neighbors.

A firing neuron becomes 6 when exactly three neighbors are firing; otherwise it stays 0.
A non-firing neuron loses 2 when zero or one neighbor is firing.
It loses 1 when more than three neighbors are firing.
With exactly two or three firing neighbors, a non-firing neuron is unchanged.
Values never fall below zero.

Return the matrix after one simultaneous update.

Function

updateNeuronMatrix(grid: int[][]) → int[][]

Examples

Example 1

grid = [[1,0,1],[0,5,0],[1,1,1]]return = [[1,0,1],[0,5,0],[0,1,0]]

The center has exactly three firing neighbors and, as a non-firing value, remains 5. Each bottom corner has one firing neighbor and decreases to 0.

Example 2

grid = [[5,5],[5,5]]return = [[3,3],[3,3]]

Every neuron has zero firing neighbors, so each value decreases by 2.

Example 3

grid = [[0,0],[0,0]]return = [[6,6],[6,6]]

Each corner has exactly three neighbors and all three are firing.

Constraints

1 <= grid.length, grid[i].length <= 1000.
All rows have equal length.
0 <= grid[i][j] <= 1000000000.