Random-telegraph-noise-enabled true random number generator for hardware security.

The future security of Internet of Things is a key concern in the cyber-security field. One of the key issues is the ability to generate random numbers with strict power and area constrains. "True Random Number Generators" have been presented as a potential solution to this problem but improvements in output bit rate, power consumption, and design complexity must be made. In this work we present a novel and experimentally verified "True Random Number Generator" that uses exclusively conventional CMOS technology as well as offering key improvements over previous designs in complexity, output bitrate, and power consumption. It uses the inherent randomness of telegraph noise in the channel current of a single CMOS transistor as an entropy source. For the first time multi-level and abnormal telegraph noise can be utilised, which greatly reduces device selectivity and offers much greater bitrates. The design is verified using a breadboard and FPGA proof of concept circuit and passes all 15 of the NIST randomness tests without any need for post-processing of the generated bitstream. The design also shows resilience against machine learning attacks performed by the LSTM neural network.

On the role of astroglial syncytia in self-repairing spiking neural networks.

It has been shown that brain-like self-repair can arise from the interactions between neurons and astrocytes where endocannabinoids are synthesized and released from active neurons. This retrograde messenger feeds back to local synapses directly and indirectly to distant synapses via astrocytes. This direct/indirect feedback of the endocannabinoid retrograde messenger results in the modulation of the probability of release (PR) at synaptic sites. When synapses fail, there is a corresponding falloff in the firing activity of the associated neurons, and hence the strength of the direct feedback messenger diminishes. This triggers an increase in PR of healthy synapses, due to the indirect messenger from other active neurons, which is the catalyst for the repair process. In this paper, the repair process is implemented by developing a new learning rule that captures the spike-timing-dependent plasticity and Bienenstock, Cooper, and Munro learning rules. The rule is activated by the increase in PR and results in a potentiation of the weight values, which reestablishes the firing activity of neurons. In addition, this self-repairing mechanism is extended to network-level repair where astrocyte to astrocyte communications are implemented using a linear gap junction model. This facilitates the implementation of an astroglial syncytium involving multiple astrocytes, which relays the indirect feedback messenger to distant neurons: each astrocyte is bidirectionally coupled to neurons. A detailed and comprehensive set of results with analysis is presented demonstrating repair at both cellular and network levels.