Abstract
The brain uses recurrent spiking neural networks for higher cognitive functions such as symbolic computations, in particular, mathematical computations. We review the current state of research on spike-based symbolic computations of this type. In addition, we present new results which show that surprisingly small spiking neural networks can perform symbolic computations on bit sequences and numbers and even learn such computations using a biologically plausible learning rule. The resulting networks operate in a rather low firing rate regime, where they could not simply emulate artificial neural networks by encoding continuous values through firing rates. Thus, we propose here a new paradigm for symbolic computation in neural networks that provides concrete hypotheses about the organization of symbolic computations in the brain. The employed spike-based network models are the basis for drastically more energy-efficient computer hardware – neuromorphic hardware. Hence, our results can be seen as creating a bridge from symbolic artificial intelligence to energy-efficient implementation in spike-based neuromorphic hardware.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵* Joint last authors, E-mail: ceca{at}igi.tugraz.at, maass{at}igi.tugraz.at, legi{at}igi.tugraz.at
Sections about the e-prop learning rule (Section 2.3) and population coding (Section 3.1) are extended, and other minor changes are implemented (e.g., replaced "classification error" with "accuracy" in Section 5.1, and the performance results for the experiments described as prior work (Section 4) are added).
