Updates to paper.md

paper.md

@@ -1,39 +1,38 @@
 ---
-title: 'Lava Optimization: a Python package for neuromorphic constrained optimization'
+title: 'Lava Optimization: a Python package for neuromorphic mathematical optimization'
 tags:
-  - Python
-  - Optimization
   - Neuromorphic computing
+  - Mathematical Optimization
   - Spiking Neural Networks
-  - QUBO
-  - QP
+  - Operations Research
+  - Python
 authors:
-  - name: Gabriel Fonseca Guerra
-    orcid: 
+  - name: Gabriel A. Fonseca Guerra
+    orcid: 0000-0001-5403-4634
     equal-contrib: false
     affiliation: 1
   - name: Alessandro Pierro
-    orcid: 
+    orcid: 0000-0002-5682-627X
     equal-contrib: false
     affiliation: 1
   - name: Philipp Stratmann
-    orcid: 
+    orcid: 0000-0001-6791-9159
     equal-contrib: false
     affiliation: 1
-  - name: Sumedh Risbud
-    orcid: 
+  - name: Sumedh R. Risbud
+    orcid: 0000-0003-4777-1139
     equal-contrib: false
     affiliation: 1
   - name: Ashish Rao Mangalore
-    orcid: 
+    orcid: 0000-0002-8496-7678
     equal-contrib: false
     affiliation: 1
   - name: Timothy Shea
-    orcid: 
+    orcid: 0000-0001-5705-0904
     equal-contrib: false
     affiliation: 1
   - name: Andreas Wild
-    orcid: 
+    orcid: 0000-0003-0380-5675
     equal-contrib: false
     affiliation: 1
 affiliations:
@@ -58,23 +57,42 @@ This collaborative effort showcases the potential of `Lava Optimization` in adva
 
 # Statement of need
 
-- Difficulty of programmability of neuromorphic
-  - SpynNaker: more suited for neuroscience, hard to use to program algorithms [@rhodes2018spynnaker]
-  - NENGO [@bekolay2014nengo], NESTML [@plotnikov2016nestml], BrainScaleS supports PyNN [@davison2009pynn], Brian [@goodman2009brian]
-  - Previous implementations of neuromorhic optimization algorithms were mostly standalone solvers, with primitive user experience and exposing low-level implementation details to the user-facing APIs
+Constrained optimization searches for the values of input variables that minimize or maximize a given objective function, while the variables are subject to constraints. 
+This kind of problem is ubiquitous throughout scientific domains and industries.
+Constrained optimization is a promising application for neuromorphic computing as it naturally aligns with the dynamics of spiking neural networks [@davies2021advancing]. 
+When individual neurons represent states of variables, the neuronal connections can directly encode constraints between the variables: in its simplest form, recurrent inhibitory synapses connect neurons that represent mutually exclusive variable states, while recurrent excitatory synapses link neurons representing reinforcing states. 
+Implemented on massively parallel neuromorphic hardware, such a spiking neural network can simultaneously evaluate conflicts and cost functions involving many variables, and update all variables accordingly. 
+This allows a quick convergence towards an optimal state. 
+In addition, the fine-scale timing dynamics of SNNs allow them to readily escape from local minima.
+
+Several libraries are available for modeling spiking neural netowkrs:
+- SpynNaker [@rhodes2018spynnaker]
+- NENGO [@bekolay2014nengo]
+- NESTML [@plotnikov2016nestml]
+- PyNN [@davison2009pynn]
+- Brian [@goodman2009brian]
+
+Limitations
+- more suited for neuroscience, hard to use to program general algorithms
+- Difficulty of programmability / lack of abstraction
 
-- Lava offers: 
 
-- Lava Optimization introduce a clear separation of abstraction layers, modularity/composability/ortogonality/DRY, and easy to change
-- Lava Optim: on-chip online tracking of best ongoing solutions instead of post-processing recorded activity (improve performance, reduce memory/bandwidth requirements)
-- Lava Optimization includes the mathematical primitives needed for optimization problems/algorithms
+# Overview of the package
+
+- Lava Optimization introduce a clear separation of abstraction layers, modularity / composability / ortogonality / DRY, and easy to change
+- Lava Optim: on-chip online tracking of best ongoing solutions instead of post-processing recorded activity (improve performance, reduce memory / bandwidth requirements)
+- Lava Optimization includes the mathematical primitives needed for optimization problems / algorithms
 - Solver tuner / visualization / reporting / profiling analysis tools specifically for optimization
 - Easy to extend / possible future directions: regularization terms and higher-order optimization
 - Lava Optimization also includes applications specific solvers / interfaces (scheduling, tsp, clustering, vrp)
 - Modern software practices, for DevOps / automated testing, distributed with pip
 
 - Mention (if applicable) a representative set of past or ongoing research projects using the software and recent scholarly publications enabled by it.: lava BO and lava MPC
 
+# Documentation
+
+The package is extensively documented at [https://lava-nc.org/optimization.html](https://lava-nc.org/optimization.html).
+
 # Acknowledgements
 
 We acknowledge contributions from Chinonso Onah, Gavin Parpart, and Shay Snyder.