How Brian works

In this section we will briefly cover some of the internals of how Brian works. This is included here to understand the general process that Brian goes through in running a simulation, but it will not be sufficient to understand the source code of Brian itself or to extend it to do new things. For a more detailed view of this, see the documentation in the Developer’s guide.

Clock-driven versus event-driven

Brian is a clock-driven simulator. This means that the simulation time is broken into an equally spaced time grid, 0, dt, 2*dt, 3*dt, …. At each time step t, the differential equations specifying the models are first integrated giving the values at time t+dt. Spikes are generated when a condition such as v>vt is satisfied, and spikes can only occur on the time grid.

The advantage of clock driven simulation is that it is very flexible (arbitrary differential equations can be used) and computationally efficient. However, the time grid approximation can lead to an overestimate of the amount of synchrony that is present in a network. This is usually not a problem, and can be managed by reducing the time step dt, but it can be an issue for some models.

Note that the inaccuracy introduced by the spike time approximation is of order O(dt), so the total accuracy of the simulation is of order O(dt) per time step. This means that in many cases, there is no need to use a higher order numerical integration method than forward Euler, as it will not improve the order of the error beyond O(dt). See State update for more details of numerical integration methods.

Some simulators use an event-driven method. With this method, spikes can occur at arbitrary times instead of just on the grid. This method can be more accurate than a clock-driven simulation, but it is usually substantially more computationally expensive (especially for larger networks). In addition, they are usually more restrictive in terms of the class of differential equations that can be solved.

For a review of some of the simulation strategies that have been used, see Brette et al. 2007.

Code overview

The user-visible part of Brian consists of a number of objects such as NeuronGroup, Synapses, Network, etc. These are all written in pure Python and essentially work to translate the user specified model into the computational engine. The end state of this translation is a collection of short blocks of code operating on a namespace, which are called in a sequence by the Network. Examples of these short blocks of code are the “state updaters” which perform numerical integration, or the synaptic propagation step. The namespaces consist of a mapping from names to values, where the possible values can be scalar values, fixed-length or dynamically sized arrays, and functions.

Syntax layer

The syntax layer consists of everything that is independent of the way the final simulation is computed (i.e. the language and device it is running on). This includes things like NeuronGroup, Synapses, Network, Equations, etc.

The user-visible part of this is documented fully in the User’s guide and the Advanced guide. In particular, things such as the analysis of equations and assignment of numerical integrators. The end result of this process, which is passed to the computational engine, is a specification of the simulation consisting of the following data:

  • A collection of variables which are scalar values, fixed-length arrays, dynamically sized arrays, and functions. These are handled by Variable objects detailed in Variables and indices. Examples: each state variable of a NeuronGroup is assigned an ArrayVariable; the list of spike indices stored by a SpikeMonitor is assigned a DynamicArrayVariable; etc.

  • A collection of code blocks specified via an “abstract code block” and a template name. The “abstract code block” is a sequence of statements such as v = vr which are to be executed. In the case that say, v and vr are arrays, then the statement is to be executed for each element of the array. These abstract code blocks are either given directly by the user (in the case of neuron threshold and reset, and synaptic pre and post codes), or generated from differential equations combined with a numerical integrator. The template name is one of a small set (around 20 total) which give additional context. For example, the code block a = b when considered as part of a “state update” means execute that for each neuron index. In the context of a reset statement, it means execute it for each neuron index of a neuron that has spiked. Internally, these templates need to be implemented for each target language/device, but there are relatively few of them.

  • The order of execution of these code blocks, as defined by the Network.

Computational engine

The computational engine covers everything from generating to running code in a particular language or on a particular device. It starts with the abstract definition of the simulation resulting from the syntax layer described above.

The computational engine is described by a Device object. This is used for allocating memory, generating and running code. There are two types of device, “runtime” and “standalone”. In runtime mode, everything is managed by Python, even if individual code blocks are in a different language. Memory is managed using numpy arrays (which can be passed as pointers to use in other languages). In standalone mode, the output of the process (after calling is a complete source code project that handles everything, including memory management, and is independent of Python.

For both types of device, one of the key steps that works in the same way is code generation, the creation of a compilable and runnable block of code from an abstract code block and a collection of variables. This happens in two stages: first of all, the abstract code block is converted into a code snippet, which is a syntactically correct block of code in the target language, but not one that can run on its own (it doesn’t handle accessing the variables from memory, etc.). This code snippet typically represents the inner loop code. This step is handled by a CodeGenerator object. In some cases it will involve a syntax translation (e.g. the Python syntax x**y in C++ should be pow(x, y)). The next step is to insert this code snippet into a template to form a compilable code block. This code block is then passed to a runtime CodeObject. In the case of standalone mode, this doesn’t do anything, but for runtime devices it handles compiling the code and then running the compiled code block in the given namespace.