In current sensing-computing systems, sensors are used to acquire information from environments, such data are normally analogue, unstructured and even redundant. After the analogue-to-digital conversion (ADC), the data are transferred into digital computers for processing. In computers with the von Neumann architecture, memories and central processing units (CPUs) are physically separated. Such a separation of sensing terminals, memories and CPUs yields serious problems, such as high energy consumption, long response time, huge data storage, and stringent requirements for the communication bandwidth and security. However, time- and energy-efficient ways are urgently required to process information at where data are generated. On the other hand, biological sensory organs respond to external stimuli in real-time with high efficiency due to the integrated capabilities of sensing, memory and computing. Therefore, the problem of separated sensing units, memories and processing units can be solved by emulating biological sensory organs.

In this work, we propose bio-inspired sensory systems with integrated capabilities of sensing, data storage and processing. In such a system, different sensors are used to capture the environmental signals from e.g. gases, light, audio and pressure, then the sensory signals are processed by an analogue signal processor, so that the energy-consuming ADC is avoided, afterwards the sensory signals are processed by a brain-inspired chip which consists of neuron-synapse cores based on memristors. In the neuron-synapse cores, leaky integrate-and-fire (LIF) neurons can be implemented by memristors and capacitors, and adaptive LIF neurons are developed from the LIF neurons to realize unsupervised learning algorithms. The synapses are realized by memristor arrays which can also perform the in-memory computing. By changing the connection between the neurons, the brain-inspired chip can realize different spiking neural networks (SNNs), such as fully connected SNN, convolutional SNN, and recurrent SNN. The synaptic weight in SNNs can be updated according to the spike-timing dependent plasticity (STDP) or the spike-rate dependent plasticity (SRDP). As an example, a bio-inspired olfactory system is demonstrated. In a artificial olfactory system, a sensor array detects and transforms the chemical information about gas molecules into electrical sensory signals. Then the sensory signals are processed by the analogue signal processing unit. After pre-processing, the brain-inspired chip classifies gases by constructing a fully connected SNN with two layers. Such a bio-inspired olfactory system emulates the function of a biological nose, overcoming the low efficiency caused by the frequent sampling, data conversion, transfer and storage under the current sensing-computing architecture. More importantly, the approach of this work can be used to emulate almost all the biological perceptions, such as touch, sight, hearing and taste, through the integration with different types of sensors., Therefore, this work offers a brand new approach to realizing the artificial intelligence (AI).