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tempcoding fix decay rate in tested model Sep 17, 2019
LICENSE Update Aug 1, 2019


This repository contains code for project Ihmehimmeli. The model is described in the paper:

I.M. Comsa, K. Potempa, L. Versari, T. Fischbacher, A. Gesmundo, J. Alakuijala (2019). “Temporal coding in spiking neural networks with alpha synaptic function”, arXiv:1907.13223, July 2019.

The objective of Ihmehimmeli is to build recurrent architectures for state-based spiking neural networks that encode information in the timing of individual neuron spikes. Spike-based temporal coding allows a natural and energy-efficient solution for the encoding and processing of real-world analog signals. This approach can potentially evolve into native interfaces between artficial and biological neural networks. Similar to the way that biological brains have evolved to use temporal coding for the rapid processing of sensory information, we expect that equivalent develpments in spiking networks will be a key future step in the advancement of general artificial intelligence.

Build instructions

Compiling this project requires CMake and a C++11 compliant compiler. It was tested with CMake 3.12.1 and g++ 7.3.0, though it will likely work with other versions and compilers.

git clone
cd ihmehimmeli
git clone
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j 12

Train a spiking network on MNIST

Download the MNIST dataset


Train a spiking network on MNIST.

cd build
tempcoding/tempcoding_main -problem=mnist -n_train=54000 -n_validation=6000 -n_test=10000  -batch_size=32 -clip_derivative=539.69973904211679 -decay_rate=0.18176949150701854 -fire_threshold=1.1673205005788956 -learning_rate=0.0010186407877494507 -learning_rate_pulses=0.09537534860701444 -n_hidden=340 -n_pulses=10 -nonpulse_weight_mean_multiplier=-0.2754188425913906 -penalty_no_spike=48.374830659132556 -pulse_weight_mean_multiplier=7.8391245503824578 -update_all_datapoints=true -use_adam=true -n_epochs=100 -mnist_data_path=../data/mnist

Test a spiking network on MNIST

Two networks reported in the paper are available under tempcoding/networks/: a slow_network that achieves the best accuracy but is slow, and a fast_network that is less accurate but makes decisions very fast.

cd build
tempcoding/tempcoding_main -model_to_test=tempcoding/networks/slow_network -problem=mnist -n_test=10000 -n_train=60000 -n_validation=0 -decay_rate=0.181769 -mnist_data_path=../data/mnist


Apache 2.0; see LICENSE for details.


This project is not an official Google project. It is not supported by Google and Google specifically disclaims all warranties as to its quality, merchantability, or fitness for a particular purpose.

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