Happy First Birthday, SpikeFun

Little more than a year ago, first public version of SpikeFun (0.24) was released after approx. 6 months of development in my spare time. It started as a small toy project of mine, aimed at learning how to simulate large-scale biological neural networks. For myself it was, and still is, a fascinating journey - two years ago I did not know almost anything past basic "Discovery Channel" knowledge of what neurons and synapses do...

Today, two years later, I think I know a bit better how little I know and how vast this subject actually is; but I fell completely in love with computational neuroscience and its endless, staggering, but yet beautiful complexity. When I started being interested in this exciting subject, terms like action potentials, membrane potentials, Ca conductance, STDP, dorsal thalamus, spiny stellate cells etc. were just bunch of meaningless random domain-specific terms... now at least I know how to model some of them, albeit very roughly :-)

I would say, a bit... This is how the first public version of SpikeFun looked like:

SpikeFun 0.24 was modelling randomly wired single-compartment neurons (with single, myelinated, axonal projections) - it was slow compared to today's version (it was not able to use Intel's 256-bit AVX instruction set, for example), it could only model randomly wired "cubical" networks and its visualization capacity was very limited, using ancient OpenGL 1.1 feature set. Yet, it was able to model major synaptic receptor kinetics and it allowed me to start learning how to model certain aspects of mammalian central nervous systems...

This is how the latest public version of SpikeFun looks like now:

Today's SpikeFun is modelling anatomical regions of the mammalian central nervous system: Cortex, Thalamus (Dorsal Thalamus and Reticular Thalamic Nucleus including individual dorsal thalamus nuclei) and parts of the Brainstem (Pedunculopontine Tegmental Nucleus).

To achieve that, it models more than 20 subtypes of cortical, thalamic and brainstem neurons (fully extendible and configurable in XML format) demonstrating different spiking behaviors (regular spiking, low threshold spiking, bursting, fast spiking, late spiking, bursting/tonic dual-mode, etc.) with multiple axonal projections per neuron, myelinated and non-myelinated axonal collaterals, multiple compartments per neuron, topographical axonal projections (Connectomes) guided by anatomical recordings of the human CNS white matter (Diffusion Spectrum Imaging - DSI), miniature post-synaptic potentials (mPSPs), Spike-timing-dependent plasticity (STDP), short-term synaptic facilitation/depression, and many more...

Locally, neurons are connected statistically using experimental data (Binzegger et. al, 2004) collected from the primary visual cortex of the cat. Visualization is done using OpenGL shaders for the maximum performance, with two levels of precision ("boxes" indicating neuron somas or complete dendritic+axonal network). Finally, steps have been carried out to make the underlying DigiCortex engine extendible - with configurable simulation parameters in XML format.

Also, during this time, some (in my opinion) interesting milestones have been achieved:

This is, to the best of my knowledge, largest-scale cortical simulation ever achieved on the single home PC up to date. It did require rather unconventional amounts of RAM memory for a home PC of the year 2012 (~130 GB), but nothing that can't be purchased in the local PC store (for those interested: Samsung 16 GB DDR3 sticks, times 8, part# M393B2G70BH0CK0):

While support for the brainstem nuclei is in its early stage, the video below shows the influence of the Ascending Reticular Activating System (ARAS), in particular - Brainstem Pedunculopontine Tegmental Nuclei (PPTN), on the overall thalamocirtical activity:

So, some progress has been made definitely...

... and one more thing!