removed flag from bernoulli/latency-cells for now; minor edit to doc

Author: Alexander Ororbia

docs/installation.md

@@ -2,7 +2,7 @@
 
 **ngc-learn** officially supports Linux on Python 3. It can be run with or without a GPU.
 
-<i>Setup:</i> <a href="https://github.com/NACLab/ngc-learn">NGC-Learn</a>, in its entirety (including its supporting utilities), requires that you ensure that you have installed the following base dependencies in your system. Note that this library was developed and tested on Ubuntu 22.04 (with much earlier versions on Ubuntu 18.04/20.04). 
+<i>Setup:</i> <a href="https://github.com/NACLab/ngc-learn">NGC-Learn</a>, in its entirety (including its supporting utility sub-packages), requires that you ensure that you have installed the following base dependencies in your system. Note that this library was developed and tested on Ubuntu 22.04 (with much earlier versions on Ubuntu 18.04/20.04). 
 Specifically, NGC-Learn requires:
 * Python (>=3.10)
 * ngcsimlib (>=2.0.0), (<a href="https://github.com/NACLab/ngc-sim-lib">official page</a>)

ngclearn/components/input_encoders/bernoulliCell.py

@@ -30,8 +30,8 @@ def __init__(self, name: str, n_units: int, batch_size: int = 1, key: Union[jax.
         super().__init__(name=name, key=key)
 
         ## Layer Size Setup
-        self.batch_size = Compartment(batch_size, fixed=True)
-        self.n_units = Compartment(n_units, fixed=True)
+        self.batch_size = Compartment(batch_size)
+        self.n_units = Compartment(n_units)
 
         restVals = jnp.zeros((batch_size, n_units))
         self.inputs = Compartment(restVals, display_name="Input Stimulus") # input compartment

ngclearn/components/input_encoders/latencyCell.py

@@ -151,18 +151,18 @@ def __init__(
         super().__init__(name=name, key=key)
 
         ## latency meta-parameters
-        self.first_spike_time = Compartment(first_spike_time, fixed=True)
-        self.tau = Compartment(tau, fixed=True)
-        self.threshold = Compartment(threshold, fixed=True)
-        self.linearize = Compartment(linearize, fixed=True)
-        self.clip_spikes = Compartment(clip_spikes, fixed=True)
+        self.first_spike_time = Compartment(first_spike_time)
+        self.tau = Compartment(tau)
+        self.threshold = Compartment(threshold)
+        self.linearize = Compartment(linearize)
+        self.clip_spikes = Compartment(clip_spikes)
         ## normalize latency code s.t. final spike(s) occur w/in num_steps
-        self.normalize = Compartment(normalize, fixed=True)
-        self.num_steps = Compartment(num_steps, fixed=True)
+        self.normalize = Compartment(normalize)
+        self.num_steps = Compartment(num_steps)
 
         ## Layer Size Setup
-        self.batch_size = Compartment(batch_size, fixed=True)
-        self.n_units = Compartment(n_units, fixed=True)
+        self.batch_size = Compartment(batch_size)
+        self.n_units = Compartment(n_units)
 
         ## Compartment setup
         restVals = jnp.zeros((batch_size, n_units))