SUPS

In computational neuroscience, SUPS (for Synaptic Updates Per Second) is a measure of a neuronal network performance, useful in fields of neuroscience, cognitive science, artificial intelligence, and computer science.

Computing

For a processor or computer designed to simulate a neural network SUPS is measured as the product of simulated neurons $N$ and average connectivity $c$(synapses) per neuron per second:

$SUPS=c\times N$

Depending on the type of simulation it is usual equal to the total number of synapses simulated.

In an "asynchronous" dynamic simulation if a neuron spikes at $\upsilon$ Hz, the average rate of synaptic updates provoked by the activity of that neuron is $\upsilon cN$. In a synchronous simulation with step $\Delta t$ the number of synaptic updates per second would be ${\frac {cN}{\Delta t}}$. As $\Delta t$ has to be chosen much smaller than the average interval between two successive afferent spikes, which implies $\Delta t<{\frac {1}{\upsilon N}}$, giving an average of synaptic updates equal to $\upsilon cN^{2}$. Therefore, spike-driven synaptic dynamics leads to a linear scaling of computational complexity O(N) per neuron, compared with the O(N²) in the "synchronous" case.

Records

In 2013, the K computer was used to simulate a neural network of 1.73 billion neurons with a total of 10.4 trillion synapses (1% of the human brain). The simulation ran for 40 minutes to simulate 1 s of brain activity at a normal activity level (4.4 on average). The simulation required 1 Petabyte of storage.

See also

• SyNAPSE