Evolutionary 2D organic crystals for optoelectronic transistors and neuromorphic computing

Brain-inspired neuromorphic computing has been extensively researched, taking advantage of increased computer power, the acquisition of massive data, and algorithm optimization. Neuromorphic computing requires mimicking synaptic plasticity and enables near-in-sensor computing. In synaptic transistors, how to elaborate and examine the link between microstructure and characteristics is a major difficulty. Due to the absence of interlayer shielding effects, defect-free interfaces, and wide spectrum responses, reducing the thickness of organic crystals to the 2D limit has a lot of application possibilities in this computing paradigm. This paper presents an update on the progress of 2D organic crystal-based transistors for data storage and neuromorphic computing. The promises and synthesis methodologies of 2D organic crystals are summarized. Following that, applications of 2D organic crystals for ferroelectric nonvolatile memory, circuit-type optoelectronic synapses, and neuromorphic computing are addressed. Finally, new insights and challenges for the field's future prospects are presented, pushing the boundaries of neuromorphic computing even farther.

Encoding and retrieval of associative recognition memory engage different sub-networks within a hippocampal-thalamo-cortical memory circuit

Recognition of previously encountered stimuli and their associated spatial and temporal information depends on neural activity within a brain-wide network in which the CA1 region of the hippocampus, nucleus reuniens of the thalamus (NRe) and medial prefrontal cortex (mPFC) are key nodes. However, the pathways crucial for coordinating activity during memory encoding and/or retrieval phases have been little explored. Here we opto- or chemo associative recognition memory. We discovered that encoding, but not retrieval depended on the CA1 to mPFC and NRe to mPFC projections. In contrast, retrieval depended on the mPFC to NRe projection. Interestingly the NRe to CA1 pathway was required for both memory phases. Our findings therefore reveal that encoding and retrieval engage dissociable sub-networks within a hippocampal-thalamo-cortical recognition memory circuit in order to enable binding of recent and related information, whilst ensuring a separation of processing.

An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation

We propose a Double EXponential Adaptive Threshold (DEXAT) neuron model that improves the performance of neuromorphic Recurrent Spiking Neural Networks (RSNNs) by providing faster convergence, higher accuracy and a flexible long short-term memory. We present a hardware efficient methodology to realize the DEXAT neurons using tightly coupled circuit-device interactions and experimentally demonstrate the DEXAT neuron block using oxide based non-filamentary resistive switching devices. Using experimentally extracted parameters we simulate a full RSNN that achieves a classification accuracy of 96.1% on SMNIST dataset and 91% on Google Speech Commands (GSC) dataset. We also demonstrate full end-to-end real-time inference for speech recognition using real fabricated resistive memory circuit based DEXAT neurons. Finally, we investigate the impact of nanodevice variability and endurance illustrating the robustness of DEXAT based RSNNs.