SpikeFun v0.91 - Voltage Based STDP

SpikeFun v0.91 is out and brings support for more advanced long-term plasticity - Voltage-based STDP (based on the model of Clopath et. al). Voltage-based STDP is in agreement with more experimental data than conventional STDP models that consider spike timing only.

SpikeFun v0.90 - More Complex Pyramidal Neurons, Demo with 3.52 Billion Synapses and more...

SpikeFun v0.90 is out and brings support for more advanced pyramidal neuron morphologies such as apical tufts and dendritic tree branching. In addition, new milestone has been reached - simulation with 3.52 billion synapses, which is just some 0.5 billion synapses shy of the current built-in simulation limits. Click on the title to find out more!

New Milestone - 16.7 Million Neurons, 2.1 Billion Synapses

SpikeFun and its DigiCortex engine have been scaled to accommodate simulation of 16.7 million multi-compartment neurons with 2.1 billion synapses. To my best knowledge, this is the largest-scale simulation of cortical neurons on a home PC. Click on the title to find out more!

Happy First Birthday, SpikeFun

SpikeFun is now 1 years old! This article goes through the progress of the DigiCortex library and SpikeFun demo simulator in the last year and also describes some of the ideas that will be implemented in the future. Click on the title to find out more!

SpikeFun 0.82 - Adding Some Brainstem into the Mix...

SpikeFun v0.82 brings support for modeling of brainstem nuclei - in this early phase, only one nuclei is supported (Pedunculopontine Tegmental Nucleus - PPTN). PPTN nuclei was chosen as the first because it sends strongs projections to Thalamus and represents one of the key hubs of the ascending arousal system and, as such, is implicated in core conciousness mechanisms such as awareness. Click on the title to find out more!

SpikeFun 0.80 - The Road Ahead

SpikeFun v0.80 comes with the first (pre-alpha) preview of the future simulation-modelling GUI (DigiCortexIDE binary). At the moment, new GUI is not offering much of the planned functionality but even to get to this point, big changes were made under-the-hood so DigiCortex can become usable outside simple "demo app" environment. Another change is the new algorithm for tracking subcortical white matter connectivity, allowing for identification of thalamic nuclei (still in very early phase - and error prone). Click on the title to find out more!

SpikeFun 0.77 - Unlocking the DigiCortex Power...

SpikeFun v0.77 brings the open and extensible XML-based project configuration system, allowing deep access to the neuronal simulation configuration as well as possibilities of extending and/or changing the simulation behavior. Furthermore, entire neuron library is now open and defined in XML models, allowing for many interesting experiments... click on the title to find out more!

SpikeFun 0.72 - Improved Connectome Mapping

SpikeFun v0.72 contains significantly improved connectome-mapping code, which is greatly improved in precision compared to the earlier versions of SpikeFun.

First of all, the DSI tract tracking library (DigiCortexTractoLib*.dll) has been updated with the latest DSI Studio code base (dated June 4th). Older versions of SpikeFun were using more than a year old code. New code base contains improved tracking algorithms and also adds support for GQI (Generalized Q-Sampling Imaging).

Super-Fast Pseudo-Random Number Generator (PRNG) Implementation using SSE/SSE4

SpikeFun 0.71 contains several speed improvements that bring simulation-generation time down by 10-20%. One of the improvements is super-fast pseudorandom number generator (PRNG), written to use Intel's SSE4 instruction set (where possible). New PRNG is based on MWC1616 algorithm, which is a class of "Multiply-with-carry" (MWC) pseudorandom number generators. Author of this algorithm is George Marsaglia.

SpikeFun Scales... 8 Million Neurons, 1.45 Billion Synapses

Video below shows SpikeFun in action simulating 8 million neurons with 1.45 billion (yes, that's right - billion) synapses. This simulation takes approx ~130 GB of RAM + 20 more for additional data (visualization, DSI tracts, etc...).

Check the video out (it is also available in Full HD and 2560x1440):

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