## Round 1 - Coding ## Problem You are given a directed acyclic graph representing a neural network. Each node (neuron) has an initial activation state of 0 or 1. A neuron fires (state = 1) if the sum of its active inputs meets or exceeds its threshold. Given the graph and thresholds, determine the final state of every neuron after full propagation. ```python def propagate_neurons( n: int, edges: list[tuple[int, int]], initial_states: list[int], thresholds: list[int] ) -> list[int]: # n: number of neurons (0-indexed) # edges: list of (src, dst) directed edges # initial_states: initial 0/1 activation per neuron # thresholds: firing threshold per neuron # returns: final activation state of each neuron ... ``` ``` Example: n = 4 edges = [(0,2), (1,2), (2,3)] initial_states = [1, 1, 0, 0] thresholds = [1, 1, 2, 1] Neuron 2 receives input from 0 and 1 (both active) -> sum=2 >= threshold=2 -> fires Neuron 3 receives input from 2 (now active) -> sum=1 >= threshold=1 -> fires propagete_neurons(...) -> [1, 1, 1, 1] ``` ## Follow-ups 1. How does your solution handle neurons that have no incoming edges? 2. What if the graph had cycles - how would you detect and handle them? 3. Can a neuron's activation change more than once? Does order matter? 4. How would you extend this to support weighted edges?

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Neuron States - Simulate Neural Network Activation Propagation

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Round 1 - Coding Problem You are given a directed acyclic graph representing a neural network. Each node (neuron) has an initial activation state of 0 or 1. A neuron fires (state = 1) if the sum of its active inputs meets or exceeds its threshold. Given the graph and thresholds, determine the final state of every neuron after full propagation. Follow-ups How does your solution handle neurons that have no incoming edges? What if the graph had cycles - how would you detect and handle them? Can a n…

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Neuron States - Simulate Neural Network Activation Propagation

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