Associative STDP-like learning of neuromorphic circuits based on polyaniline memristive microdevices

Abstract

Spiking neuromorphic networks (SNNs) are bio-inspired artificial systems capable of unsupervised learning and promising candidates to mimic biological neural systems in efficient solution of cognitive tasks. Most SNNs are based on local learning rules, such as bio-like spike-time-dependent plasticity (STDP). In this paper, we report a significantly improved timescale of STDP for polyaniline-based memristive microdevices. We have used this result to show the possibility of associative learning with an unsupervised STDP-like mechanism of a simple SNN. The dependence of the required number of learning cycles on the pulse length was found: the longer the training pulse, the smaller the number of epochs the system needs to learn the associative rule. But the total training time remained nearly constant regardless of the pulse length. This study will be helpful in designing more sophisticated bio-plausible neuromorphic systems based on organic memristors.

The Role of the Internal Capacitance in Organic Memristive Device for Neuromorphic and Sensing Applications

“…More in detail, macrodevices have demonstrated to be able to implement activity dependent functions [45] while solid microdevices successfully demonstrated the implementation of STDP. [46] Liquid microdevices with the already mentioned faster switching, are predicted to exhibit the same neuromorphic functions of previously reported devices, closing the gap between devices and synapses temporal scales. To evaluate OMDs neuromorphic capabilities, we tested them following already adopted protocols [45] for the synaptic activity dependent functions (i.e., long term and short term potentiation 4a).…”

Neuropunk Revolution. Hacking Cognitive Systems towards Cyborgs 3.0

“…The system has effectively demonstrated a frequency driven long-and short-term potentiation and depression (Battistoni, Erokhin, & Iannotta, 2019). The use of these devices as synapse mimicking electronic elements has demonstrated the possibility of unsupervised learning according to the STDP algorithm, what has allowed to realize electronic circuits, demonstrating classic conditioning (Prudnikov et al, 2020). However, the main advantage of such systems is the capability of organic molecules to self-organization into 3D systems (Erokhin, Berzina, et al, 2012).…”

Designing a bidirectional, adaptive neural interface incorporating machine learning capabilities and memristor-enhanced hardware

“…In recent years, significant progress has been made in fabricating large RRAM crossbar arrays integrated with mixed signal peripheral circuits for hardware implementation of basic vector-matrix multiplication (VMM) operations [12], as well as more complex artificial neural network (ANN) circuits [21][22] [23]. Furthermore, there has been work towards coupling RRAM-based systems with biological neurons in order to form neurohybrid systems [24], most recently demonstrated through a series of experiments where electrophysiological signals [25][26] and processed spiking events [27] were fed into RRAM devices for further refinement.…”