Dec 4, 2024
11:00am - 11:15am
Sheraton, Second Floor, Independence West
Daniela Rana1,2,Chi-hyeong Kim3,Meijing Wang3,Fabio Cicoira3,Francesca Santoro1,2
Forschungszentrum Jülich GmbH1,RWTH Aachen University2,Polytechnique Montréal3
Daniela Rana1,2,Chi-hyeong Kim3,Meijing Wang3,Fabio Cicoira3,Francesca Santoro1,2
Forschungszentrum Jülich GmbH1,RWTH Aachen University2,Polytechnique Montréal3
Neural information processing has been taken into consideration for the development of systems able to perform learning and decision tasks, while overcoming the von Neumann bottleneck in hardware system which use parallel computing for the optimization of the computation in terms of energetic efficiency. Non-volatile memory is one of the main requirements for the devices at the basis of neuromorphic hardware technologies responsible for the elaboration of signals<sup>1</sup>. Among the different applications, neurohybrid platforms attracts interest from the utility for the building of brain-machine interfaces. Conductive polymers have been largely used for their advantages in the operability with biological environments<sup>2,3</sup>. Electrochemical organic neuromorphic devices (ENODes) are rapidly developing for the mimic of neural information processing and the resembling of volatile and non-volatile memories depending on the tuning of some device features<sup>4–6</sup>. Short- and long-term synaptic plasticity is a key characteristic in creating functional neuromorphic interfaces that showcase spiking activity and learning capabilities<sup>7</sup>.<br/>With the goal to couple ENODes with the neurons and cerebral environment, it is worth investigating the neuromorphic behavior of ENODEs when they interface with electrolytes that have a consistency like brain tissue in mechanical properties, as this can affect the modulation of ion and neurotransmitter diffusion.<br/>Here, we present ENODEs with two different planar geometries, based on printed PEDOT:PSS SV3 and on spin coated PEDOT:PSS PH1000 gate and transistor channel. These were interfacing with liquid (water-glycerol mixtures) and solid electrolytes (agarose gel) to investigate the steady-state and the transient behaviour in different conditions. The last one also reflects the short-term plasticity, and particularly the paired-pulse facilitation patterning. Moreover, the electrolytes were loaded with a neurotransmitter and a gate pulse stimulation that oxidizing allowed for a long-term modulation and represented a non-volatile memory.<br/>We found that both the composition of the electrolyte and the PEDOT:PSS formulation used as gate and channel material play a crucial role in the diffusion and trapping of cations that ultimately modulate the conductance of the transistor channels. It was shown that short-term plasticity can be achieved in both devices and the agarose gel allows for performances similar to the liquid electrolyte in terms of diffusivity of cations during the stimulation. On the other hand, the long-term plasticity can be achieved only with spin-coated ENODes and it can be performed with the tissue-like electrolyte made of agarose gel.<br/>Our work on ENODe-gel coupling could pave the way for effective brain interfacing for computing and neuroelectronic applications.<br/><br/><br/>References<br/>1. Gkoupidenis, P. et al. Organic mixed conductors for bioinspired electronics. Nat Rev Mater 1–16 (2023).<br/>2. Bianchi, M. et al. Conductive Polymers for Bidirectional Neural Interfaces: Fundamentals Aspects and in Vivo Applications. https://www.techrxiv.org/doi/full/10.36227/techrxiv.16938844.v1 (2021).<br/>3. Azimi, M., Kim, C., Fan, J. & Cicoira, F. Effect of ionic conductivity of electrolyte on printed planar and vertical organic electrochemical transistors. Faraday Discuss. 246, 540–555 (2023).<br/>4. Van De Burgt, Y. et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nature Mater 16, 414–418 (2017).<br/>5. Keene, S. T. et al. A biohybrid synapse with neurotransmitter-mediated plasticity. Nat. Mater. 19, 969–973 (2020).<br/>6. Bruno, U. et al. From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks. Neuromorphic Computing and Engineering 3, 023002 (2023).<br/>7. Bruno, U. et al. An organic brain-inspired platform with neurotransmitter closed-loop control, actuation and reinforcement learning. Mater. Horiz. 11, 2865–2874 (2024).