fix mispelling of Crank-Nicolson (#3186)

docs/courses/state_and_parameter_discontinuities.rst

@@ -75,4 +75,4 @@ To work properly with variable time step methods, models that change states and/
 
         fih = h.FInitializeHandler(setup_discontinuities)
 
-    It will be helpful to use the Crank-Nicholson fixed step method and compare the variable step method with and without the ``cvode.re_init()``. Zoom in around the discontinuity at 2 ms.
+    It will be helpful to use the Crank-Nicolson fixed step method and compare the variable step method with and without the ``cvode.re_init()``. Zoom in around the discontinuity at 2 ms.

docs/guide/what_is_neuron.rst

@@ -41,7 +41,7 @@ Integrator-independent model specification
 NEURON offers several different, user-selectable numerical integration methods.
 
 * The default integration method is implicit Euler, which provides robust stability and first order accuracy in time (sufficient for most applications).
-* There is also a Crank-Nicholson method that provides second order accuracy at little additional computational cost. However, this is prone to numerical oscillations if dt is too long, voltage clamps are present, or system states are described by algebraic equations.
+* There is also a Crank-Nicolson method that provides second order accuracy at little additional computational cost. However, this is prone to numerical oscillations if dt is too long, voltage clamps are present, or system states are described by algebraic equations.
 * Increased accuracy, faster run times, and sometimes both, may be achieved by choosing adaptive integration, which adjusts integration order and time step as necessary to satisfy a local error criterion. For historical reasons, the adaptive integrators are genericallly called "CVODE" in NEURON; the actual method is either IDA (Hindmarsh and Taylor, 1999) or CVODES (Hindmarsh and Serban, 2002), a decision that is made automatically (i.e. without requiring user judgement) depending on whether or not a model involves states that are described by algebraic equations.
 
 Users can switch between these integration methods without having to rewrite the model specification because NEURON avoids computation-specific representations of biological properties. This convenience is essential because deciding which method is best in any particular situation is often an empirical question. Further details about numeric integration in NEURON are provided in chapter 4 of The NEURON Book (Carnevale and Hines, 2006).

docs/hoc/modelspec/programmatic/topology/geometry.rst

@@ -13,7 +13,7 @@ any tree-shaped structure but loops are not permitted. (You may, however,
 develop membrane mechanisms, such as electrical gap junctions 
 which do not have the loop restriction. But be aware that the electrical 
 current flows through such connections are calculated by a modified euler 
-method instead of the more numerically robust fully implicit/crank-nicholson 
+method instead of the more numerically robust fully implicit/crank-nicolson 
 methods) 
 
 Do not confuse sections with segments. Sections are divided into segments 

docs/hoc/simctrl/programmatic.rst

@@ -230,14 +230,14 @@ Functions
             are proportional to :hoc:data:`dt`.
 
         =1 
-            crank-nicholson Can give large (but damped) numerical error 
+            crank-nicolson Can give large (but damped) numerical error 
             oscillations. For small :hoc:data:`dt` the numerical errors are proportional
             to ``dt^2``. Cannot be used with voltage clamps. Ionic currents 
             are first order correct. Channel conductances are second order 
             correct when plotted at ``t+dt/2`` 
 
         =2 
-            crank-nicholson like 1 but in addition Ion currents (*ina*, *ik*, 
+            crank-nicolson like 1 but in addition Ion currents (*ina*, *ik*, 
             etc) are fixed up so that they are second order correct when 
             plotted at ``t-dt/2`` 
 

docs/python/modelspec/programmatic/topology/geometry.rst

@@ -11,7 +11,7 @@ any tree-shaped structure but loops are not permitted. (You may, however,
 develop membrane mechanisms, such as electrical gap junctions 
 which do not have the loop restriction. But be aware that the electrical 
 current flows through such connections are calculated by a modified euler 
-method instead of the more numerically robust fully implicit/crank-nicholson 
+method instead of the more numerically robust fully implicit/crank-nicolson 
 methods) 
 
 Do not confuse sections with segments. Sections are divided into segments 

docs/python/simctrl/programmatic.rst

@@ -231,14 +231,14 @@ Functions
             are proportional to :data:`dt`. 
 
         =1 
-            crank-nicholson Can give large (but damped) numerical error 
+            crank-nicolson Can give large (but damped) numerical error 
             oscillations. For small :data:`dt` the numerical errors are proportional 
             to ``dt^2``. Cannot be used with voltage clamps. Ionic currents 
             are first order correct. Channel conductances are second order 
             correct when plotted at ``t+dt/2`` 
 
         =2 
-            crank-nicholson like 1 but in addition Ion currents (*ina*, *ik*, 
+            crank-nicolson like 1 but in addition Ion currents (*ina*, *ik*, 
             etc) are fixed up so that they are second order correct when 
             plotted at ``t-dt/2`` 
 

docs/videos/neuron-course-2021.rst

@@ -149,7 +149,7 @@ Topics:
 
 - synapses, spike-triggered transmission, :class:`NetCon`
 - artificial spiking cells (:class:`IntFire1`, :class:`IntFire2`, :class:`IntFire4`)
-- Forward Euler vs Backward Euler vs Crank-Nicholson; fixed step vs variable step
+- Forward Euler vs Backward Euler vs Crank-Nicolson; fixed step vs variable step
 
 .. raw:: html
 

share/examples/nrniv/nrnoc/implic.hoc

@@ -1,4 +1,4 @@
-/* Comparison of implicit and crank-nicholson */
+/* Comparison of implicit and crank-nicolson */
 /* showing that C-N can have error oscillations */
 
 /* lambda = sqrt(1e4/4 * diam / (Ra * gl)) microns */