fix(dyn): PoissonGroup never spikes due to boolean rand_like dtype

[chaoming0625]

Problem

PoissonGroup never emitted spikes. In PoissonGroup.update, the random draw used
rand_like(self.spike.value). Since spike is boolean-typed and rand_like inherits the
input dtype, the draw was boolean (≈ all True), so the comparison True <= prob was always
False and the group never fired.

Because both examples/dynamics_simulation/decision_making_network.py and
examples/dynamics_simulation/stdp.py are driven entirely by PoissonGroup, the decision
network was silent (0 Hz in every population) and STDP weights stayed frozen.

Fix

Force a real uniform draw with dtype=float:

spikes = bm.random.rand_like(self.spike.value, dtype=float) <= (self.freqs * share['dt'] / 1000.)

Tests

The existing test_PoissonGroup only asserted output shape, which is why the regression
slipped through. Added:

• test_PoissonGroup_fires_at_expected_rate — asserts empirical rate ≈ freqs (0 Hz on old code).
• test_PoissonGroup_fires_in_batching_mode — covers batched shape preservation + rate.

Verification

• input_test.py + STDP_test.py: 139 passed.
• Decision network: Group A wins at 41.8 Hz (persistent delay activity), Group B suppressed to 0.4 Hz.
• STDP: max|ΔW| over 10 s = 0.0032 (weights now evolve).

Summary by Sourcery

Ensure PoissonGroup emits spikes by fixing its random draw dtype and strengthen tests to verify firing rates, including in batching mode.

Bug Fixes:

• Fix PoissonGroup spike generation by forcing uniform random draws to use a floating-point dtype instead of inheriting the boolean spike dtype.

Tests:

• Add tests asserting PoissonGroup fires at the expected rate over time.
• Add a batching-mode PoissonGroup test verifying both batched spike shape and firing rate.

[sourcery-ai]

Reviewer's Guide

Fixes PoissonGroup so it actually emits spikes by forcing the random draw to use a float dtype, and adds regression tests that verify both firing rate and batched behavior.

File-Level Changes

Change	Details	Files
Ensure PoissonGroup uses a float uniform random draw so spike probability comparison behaves correctly.	Change PoissonGroup.update() to call bm.random.rand_like with dtype=float instead of inheriting the boolean spike dtype. Document in a comment why dtype=float is required and how the previous boolean draw suppressed spikes. Simplify update() by removing commented-out jax.debug.print lines.	brainpy/dyn/others/input.py
Add regression tests that verify PoissonGroup actually fires at the configured rate, including in batching mode.	Import numpy and brainpy.math in the PoissonGroup test module to compute empirical firing rates and control RNG seeding. Add test_PoissonGroup_fires_at_expected_rate to assert empirical firing rate is close to freqs over a 1 s simulation. Add test_PoissonGroup_fires_in_batching_mode to verify batched PoissonGroup preserves shape and achieves the expected rate across batch and neuron dimensions.	brainpy/dyn/others/input_test.py

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[codecov]

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✅ All modified and coverable lines are covered by tests.

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