import numpy as np class FruitFlyNeuralNetwork: def __init__(self, num_neurons): self.num_neurons = num_neurons self.synaptic_weights = np.random.uniform(0.0, 0.5, (num_neurons, num_neurons)) self.v_rest = -70.0 self.v_threshold = -50.0 self.v_reset = -75.0 self.leak_factor = 0.9 self.voltages = np.full(num_neurons, self.v_rest) def simulate_step(self, sensory_input): self.voltages += sensory_input spikes = self.voltages >= self.v_threshold if np.any(spikes): incoming_signals = np.dot(spikes, self.synaptic_weights) self.voltages += incoming_signals self.voltages[spikes] = self.v_reset self.voltages = self.v_rest + (self.voltages - self.v_rest) * self.leak_factor return spikes fly_brain = FruitFlyNeuralNetwork(num_neurons=10) for step in range(5): mock_stimulus = np.zeros(10) mock_stimulus[0:2] = 25.0
Smiles while speaking in clipped, ledger-like phrases; overly calm and very exact.
