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CARLSIM RELEASE NOTES
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CARLsim 5.0
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- Add python interface PyCARL
- Transform CARLsim4 to CARLsim5
- Update user guide


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CARLsim 4.1
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- Bugfixes for Save and Load
- Bugfixes for NeuronMonitor
- Updates in user guide 
- Support for CUDA 10

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CARLsim 4.0
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Highlights:
    Multi-GPU support
    Hybrid CPU/GPU mode
    Multi-compartment and LIF point neurons


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CARLsim 3.1
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tbd


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CARLsim 3.0
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Highlights:
	New user interface.
	Platform compatibility (Linux, Windows, and Mac OS X).
	Shared library build. CUDA6 support.
	E-STDP, I-STDP, DA-STDP.
	Plugin for Evolutionary Computations in Java (ECJ).
	Improved SpikeMonitor, ConnectionMonitor, and GroupMonitor.
	3D Topography.
	Current injection.
	On-line weight tuning.
	MATLAB Offline Analysis Toolbox.
	MATLAB Visual Stimulus Toolbox.
	Regression suite.
	User Guide and Tutorial.
	Bugfixes.

Description:
	tbd

Publication:
	tbd

Tested platforms and devices:
	* Platforms:
	   - Linux: Ubuntu 10.04, 12.04, 12.10, 13.04, 13.10, 14.04, Arch Linux,
	     CentOS 6, OpenSUSE 13.1
	   - Windows: 7
	   - Mac OS: X
	* NVIDIA Toolkits: CUDA 5.0, 5.5, 6.0, 6.5
	* CUDA compute capabilities: 2.0, 2.1, 3.0, 3.5
	* CUDA devices: Tesla C1060, Tesla M2090, GTX 460, GTX 780, Quadro 600,
	  NVS 4200M, TITAN Z
	* Doxygen: 1.8

Known bugs & issues:
	* Currently, short-term plasticity (STP) can only be run in GPU mode if
	  there are no synaptic delays greater than 1 ms. This is to prevent a
	  known issue in kernel_globalConductanceUpdate, which will be fixed as
	  soon as possible in a future release.
	* Mistakingly calling CARLsim::setSTDP on a fixed connection will result
	  in an unspecified launch failure in GPU mode.

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CARLsim 2.2
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Highlights:
	Included model for homeostatic synaptic scaling.
	Included support for parameter tuning interface (PTI) library, which
	enables the automated parameter tuning of SNNs using evolutionary
	algorithms.
	Added CUDA5 support.

Description:
	As the desire for biologically realistic spiking neural networks (SNNs)
	increases, tuning the enormous number of open parameters in these 
	models becomes a difficult challenge. SNNs have been used to 
	successfully model complex neural circuits that explore various neural 
	phenomena such as neural plasticity, vision systems, auditory systems, 
	neural oscillations, and many other important topics of neural 
	function. Additionally, SNNs are particularly well-adapted to run on 
	neuromorphic hardware that will support biological brain-scale 
	architectures. Although the inclusion of realistic plasticity 
	equations, neural dynamics, and recurrent topologies has increased the
	descriptive power of SNNs, it has also made the task of tuning these 
	biologically realistic SNNs difficult. To meet this challenge, we 
	present an automated parameter tuning framework capable of tuning SNNs 
	quickly and efficiently using evolutionary algorithms (EA) and 
	inexpensive, readily accessible graphics processing units (GPUs). A 
	sample SNN with 4104 neurons was tuned to give V1 simple cell-like 
	tuning curve responses and produce self-organizing receptive fields 
	(SORFs) when presented with a random sequence of counterphase 
	sinusoidal grating stimuli. A performance analysis comparing the 
	GPU-accelerated implementation to a single-threaded central 
	processing unit (CPU) implementation was carried out and showed a 
	speedup of 65× of the GPU implementation over the CPU implementation, 
	or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. 
	Additionally, the parameter value solutions found in the tuned SNN 
	were studied and found to be stable and repeatable. The automated 
	parameter tuning framework presented here will be of use to both the 
	computational neuroscience and neuromorphic engineering communities, 
	making the process of constructing and tuning large-scale SNNs much 
	quicker and easier.

Publications:
	Parameter tuning interface:
	Carlson KD, Nageswaran JM, Dutt N and Krichmar JL (2014) An efficient 
	automated parameter tuning framework for spiking neural networks. 
	Front. Neurosci. 8:10. doi: 10.3389/fnins.2014.00010

    Homeostatic synaptic scaling model:
	Carlson, K. D., Richert, M., Dutt, N., and Krichmar, J. L. (2013). 
	“Biologically plausible models of homeostasis and STDP: stability and 
	learning in spiking neural networks,” in Proceedings of the 2013 
	International Joint Conference on Neural Networks (IJCNN) (Dallas, TX). 
	doi: 10.1109/IJCNN.2013.6706961


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CARLsim 2.1
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Highlights:
	Introduced "CARLsim" branding.
	Efficient SNN model of pattern motion selectivity in visual cortex 
	(V1, MT, LIP). Improved GPU memory management. Bugfixes.

Description:
	We present a two-stage model of visual area MT that we believe to be 
	the first large-scale spiking network to demonstrate pattern direction 
	selectivity. In this model, componentdirection-selective (CDS) cells in
	MT linearly combine inputs from V1 cells that have spatiotemporal 
	receptive fields according to the motion energy model of Simoncelli 
	and Heeger. Pattern-direction-selective (PDS) cells in MT are 
	constructed by pooling over MT CDS cells with a wide range of preferred
	directions. Responses of our model neurons are comparable to 
	electrophysiological results for grating and plaid stimuli as well as 
	speed tuning. The behavioral response of the network in a motion 
	discrimination task is in agreement with psychophysical data. Moreover,
	our implementation outperforms a previous implementation of the motion
	energy model by orders of magnitude in terms of computational speed and
	memory usage. The full network, which comprises 153,216 neurons and 
	approximately 40 million synapses, processes 20 frames per second of a 
	32×32 input video in real-time using a single off-the-shelf GPU. To 
	promote the use of this algorithm among neuroscientists and computer 
	vision researchers, the source code for the simulator, the network, 
	and analysis scripts are publicly available.

Publication:
	M Beyeler, M Richert, ND Dutt, JL Krichmar (2014). "Efficient spiking
	neural network model of pattern motion selectivity in visual cortex",
	Neuroinformatics.


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CARLsim 2.0
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Highlights:
	Added COBA mode, STDP, and STP. Cortical model of color selectivity 
	(color opponency). Cortical model of motion selectivity (V1, MT) and 
	orientation selectivity (V1, V4).

Description:
	We have developed a spiking neural network simulator, which is both 
	easy to use and computationally efficient, for the generation of 
	large-scale computational neuroscience models. The simulator implements
	current or conductance based Izhikevich neuron networks, having 
	spike-timing dependent plasticity and short-term plasticity. It uses a
	standard network construction interface. The simulator allows for 
	execution on either GPUs or CPUs. The simulator, which is written in 
	C/C++, allows for both fine grain and coarse grain specificity of a 
	host of parameters. We demonstrate the ease of use and computational 
	efficiency of this model by implementing a large-scale model of \
	cortical areas V1, V4, and area MT. The complete model, which has 
	138,240 neurons and approximately 30 million synapses, runs in 
	real-time on an off-the-shelf GPU. The simulator source code, as well 
	as the source code for the cortical model examples is publicly 
	available.

Publication:
	M Richert, JM Nageswaran, N Dutt, JL Krichmar (2011). "An efficient 
	simulation environment for modeling large-scale cortical processing", 
	Frontiers in Neuroinformatics 5(19):1-15.


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CARLsim 1.0
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Highlights:
	Initial release. CUBA mode. Demonstration of GPU speedup.

Description:
	We demonstrate an efficient, biologically realistic, large-scale SNN 
	simulator that runs on a single GPU. The SNN model includes Izhikevich 
	spiking neurons, detailed models of synaptic plasticity and variable 
	axonal delay. We allow user-defined configuration of the GPU-SNN model 
	by means of a high-level programming interface written in C++ but 
	similar to the PyNN programming interface specification. The GPU 
	implementation (on NVIDIA GTX-280 with 1 GB of memory) is up to 26 
	times faster than a CPU version for the simulation of 100K neurons with
	50 Million synaptic connections, firing at an average rate of 7 Hz. 
	For simulation of 10 Million synaptic connections and 100K neurons, the
	GPU SNN model is only 1.5 times slower than real-time. Further, we 
	present a collection of new techniques related to parallelism 
	extraction, mapping of irregular communication, and network 
	representation for effective simulation of SNNs on GPUs. The fidelity 
	of the simulation results was validated on CPU simulations using firing
	rate, synaptic weight distribution, and inter-spike interval analysis. 
	Our simulator is publicly available to the modeling community so
	that researchers will have easy access to large-scale SNN simulations.

Publication:
	JM Nageswaran, N Dutt, JL Krichmar, A Nicolau, AV Veidenbaum (2009). 
	"A configurable simulation environment for the efficient simulation of
	large-scale spiking neural networks on graphics processors", 
	Neural Networks 22:791-800.