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ago109 merged 2 commits into from
Dec 8, 2025
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Move over minor update to main docs to release docs #132

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b300492
Sync up of main with release (#131)
ago109 Dec 8, 2025
63a4e98
added pointer/stub for ei-rnn song-et-al in museum doc
Dec 8, 2025
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12 changes: 12 additions & 0 deletions docs/museum/eirnn_song_etal2016.md
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# Excitatory-Inhibitory Recurrent Neural Network (Song et al.; 2016)

In this exhibit, we create, simulate, and visualize the dynamics and adaptation of the excitatory-inhibitory
recurrent neural network (EI-RNN) originally proposed in (Song et al., 2016) [1].

The model code for this exhibit can be found
[here](https://github.com/NACLab/ngc-museum/tree/main/exhibits/ei_rnn).

<!-- references -->
## References
<b>[1]</b> Song, H. F., Yang, G. R., & Wang, X. J. Training excitatory-inhibitory recurrent neural networks
for cognitive tasks: a simple and flexible framework. PLoS computational biology, 12(2), e1004792 (2016).
1 change: 1 addition & 0 deletions docs/museum/index.rst
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Expand Up @@ -16,6 +16,7 @@ of detailed walkthroughs presented in the table of contents below.)

sparse_coding
pc_rao_ballard1999
eirnn_song_etal2016
snn_dc
event_stdp_patches
rl_snn
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9 changes: 5 additions & 4 deletions ngclearn/components/neurons/graded/leakyNoiseCell.py
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Expand Up @@ -71,7 +71,6 @@ def __init__(
):
super().__init__(name, **kwargs)


self.tau_x = tau_x
self.sigma_pre = sigma_pre ## a pre-rectification scaling factor
self.sigma_post = sigma_post ## a post-rectification scaling factor
Expand Down Expand Up @@ -110,7 +109,6 @@ def advance_state(self, t, dt):
key, skey = random.split(self.key.get(), 2)
eps_post = random.normal(skey, shape=self.x.get().shape) ## post-rectifier distributional noise

#x = _run_cell(dt, self.j_input.get(), self.j_recurrent.get(), self.x.get(), eps, self.tau_x, self.sigma_rec, integType=self.intgFlag)
_step_fns = {
0: step_euler,
1: step_rk2,
Expand Down Expand Up @@ -152,13 +150,16 @@ def help(cls): ## component help function
"states":
{"x": "Update to continuous noisy, leaky dynamics; value at time t"},
"outputs":
{"r": "A linear rectifier applied to rate-coded dynamics; f(z)"},
{"r": "A linear rectifier applied to rate-coded dynamics; f(z)",
"r_prime": "Temporal derivative applied to rate-coded dynamics; f'(z)"},
}
hyperparams = {
"n_units": "Number of neuronal cells to model in this layer",
"batch_size": "Batch size dimension of this component",
"tau_x": "State time constant",
"sigma_pre": "The non-zero degree/scale of (pre-rectification) noise to inject into this neuron"
"act_fx": "Type of rectification function to use",
"sigma_pre": "The non-zero degree/scale of pre-rectification noise to inject into this neuron",
"sigma_post": "The non-zero degree/scale of post-rectification noise to inject into this neuron"
}
info = {cls.__name__: properties,
"compartments": compartment_props,
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