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torch has supported a native bool dtype since 1.2 (2019), but Nodes.s and its downstream consumers were still allocated as uint8. This meant spike checks relied on comparison hacks like (s > 0) instead of direct boolean semantics, and precluded using logical ops (&, |, ~, .any(), .all()) directly on spike tensors. Changes: - Nodes.__init__ / reset_state_variables: self.s now allocated with dtype=torch.bool - Learning rules (learning.py): explicit .float() casts added where spikes are used in arithmetic weight updates - Encoders (encoding.py): output dtype aligned to bool at the spike-train boundary Closes BindsNET#318
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torch has supported a native bool dtype since 1.2 (2019), but Nodes.s and its downstream consumers were still allocated as uint8. This meant spike checks relied on comparison hacks like (s > 0) instead of direct boolean semantics, and precluded using logical ops (&, |, ~, .any(), .all()) directly on spike tensors.
Changes:
Closes #318