snnalgorithms 0.0.1

compares snn algorithms against neumann algorithms

Spiking Neural Network Algorithms

This is a library of Spiking Neural Network algorithms (SNNs), along with their regular/normal/Neumann implementation. to their default/Neumann implementations. The user can specify an SNN and "normal" algorithm which take as input a networkx graph, and compute some graph property as output. The output of the SNN is then compared to the "normal" algorithm as "ground truth", in terms of:

• Score: How many results the SNN algorithm computed correctly (from a set of input graphs).
• Runtime In theory, the score should always be 100% for the SNN, as it should be an exact SNN implementation of the ground truth algorithm. This comparison is mainly relevant for the additions of brain adaptation and simulated radiation.

Different SNN implementations may use different encoding schemes, such as sparse coding, population coding and/or rate coding.

Parent Repository

These algorithms can be analysed using this parent repository. Together, these repos can be used to investigate the effectivity of various brain-adaptation mechanisms applied to these algorithms, in order to increase their [radiation] robustness. You can run it on various backends, as well as on a custom LIF-neuron simulator.

Algorithms

An overview is included of the implemented SNN algorithms and their respective compatibilities with brain-adaptation, radiation and backends implementations:

Algorithm	Encoding	Adaptation	Radiation	Backend
Minimum Dominating Set Approximation	Sparse	Redundancy	Neuron Death	- networkx LIF - Lava LIF
Some Algorithm Approximation	Sparse	Redundancy	Neuron Death	- networkx LIF - Lava LIF

retry

Algorithm	Encoding	Adaptation	Radiation	Backend
Minimum Dominating	Sparse	Redundancy	Neuron Death	- networkx LIF
Set Approximation	Sparse	Redundancy	Neuron Death	- Lava LIF
Some Algorithm	Sparse	Redundancy	Neuron Death	- networkx LIF

Minimum Dominating Set Approximation

This is an implementation of the distributed algorithm presented by Alipour et al.

• Input: Non-triangle, planar Networkx graph. (Non triangle means there should not be any 3 nodes that are all connected with each other (forming a triangle)). Planar means that if you lay-out the graph on a piece of paper, no lines intersect (that you can roll it out on a 2D plane).
• Output: A set of nodes that form a dominating set in the graph.

Description: The algorithm basically consists of k rounds, where you can choose k based on how accurate you want the approximation to be, more rounds (generally) means more accuracy. At the start each node i gets 1 random number r_i. This is kept constant throughout the entire algorithm. Then for the first round:

• Each node i computes how many neighbours (degree) d_i it has.
• Then it adds r_i+d_i=w_i. In all consecutive rounds:
• Each node i "computes" which neighbour has the highest weight w_j, and gives that node 1 mark/point. Then each node i has some mark/score m_i. Next, the weight w_i=r_i+m_i is computed (again) and the next round starts. This last round is repeated until k rounds are completed. At the end, the nodes with a non-zero mark/score m_i are selected to form the dominating set.