Bump version to 0.2.0

README.md

@@ -10,7 +10,7 @@
   While it is possible to work around this limitation, Nengo usually does not explicitly mark neurons as excitatory or inhibitory. This means that a single can connect to post-neurons both excitatorily and inhibitorily, depending on the sign of the weights computed by of the weight solver. *NengoBio* marks neurons as either excitatory or inhibitory and accounts for this while solving for connection weights.
 * **Bias current elimination** (:ballot_box_with_check: *Fully implemented*)<br>
   The Neural Engineering Framework assumes that each neuron is connected to a constant bias current source. This bias current is used to diversify the avilable neuron tuning curves, yet is a little unrealistic from a biological perspective. *NengoBio* eliminates the bias current by solving for synaptic weights in current space, effectively decoding the bias current from the pre-population state.
-* **Support for dendritic computation** (*Partially implemented*)<br>
+* **Support for dendritic computation** (:ballot_box_with_check: *Fully implemented*)<br>
   Dendritic nonlinearities play a key role in information processing in central nervous systems and can be systematically exploited to perfrom nonlinear, multivariate computations. *NengoBio* adds support for dendritic computation to Nengo by allowing an arbitrary number of neuron ensembles as pre-objects in a connection.
 * **Support for conductance-based synapses as well as neurons with arbitrary passive dendritic trees** (*Planned*)
   Dendritic computation relies on nonlinear effects in the dendritic tree and the specific tree topology. *NengoBio* adds support for arbitrary passive multicompartment neuron models to Nengo.

setup.py

@@ -26,7 +26,7 @@
         'nengo_bio.internal':
         ['multi_compartment_lif.hpp']
     },
-    version='0.1.1',
+    version='0.2.0',
     author='Andreas Stöckel',
     author_email='[email protected]',
     description='Dendritic Computation Primitives for Nengo',