Abstract: Neuromorphic computing and engineering is capitalizing heavily on the new physical properties offered by nantechnologies to engineer biological processes. At the frontiers in between bio-mimetism and bio-inspiration, various solutions have been proposed for synaptic plasticity or neuronal features based on discrete memory elements, bistable switches or transistors circuits. One missing element that has been missing in the neuromorphic toolbox is the ability to reproduce the complex 3D interconnections o bserved in biological neural networks. Here, we propose to take advantage of bipolar electropolymerization of PEDOT dendritic fibers to reproduce the ability of neural networks to generate complex topologies. The electropolymerization mechanism is used t o realize structural plasticity based on Hebbian-like plasticity rules. We explore how such bottom-up process can find optimal topologies for specific computing tasks. We demonstrate that such optimal topologies results in a drastic reduction of intercon nects for classification and reconstruction tasks, thus offering an interesting option for neural network design.

Abstract: During recent years, neuromorphic engineering has gained significant attraction due to its endeavor to reproduce attractive brain features such as high computational efficiency, low power consumption, functional and structural adaptability onto hardware devices for competitive computing development. Currently, most of these implementations are achieved with either standard silicon-based technologies (complementary metal-oxide-semiconductor) or more emerging material and devices (iono-electronic material s and memristor devices). Mainly, these technologies are produced by means of top-down approaches. In contrast, brain computing largely relies on self-assembling processes to interconnect cells and form pathways for neural networks communication. Thus, t o benefit more from neuromorphic features, there is a strong need to explore such devices that can rely on the same bottom-up approach as in the brain. Promising solutions for this question are gathered in organic electronic materials, more precisely in organic mixed ionic electronic conductors. This study is focused on the development of bottom-up fashioned 3D organic devices that are able to mimic biological neural network branching. By employing Alternating-Current (AC) bipolar electropolymerization, we show how one can synthesize polymer dendritic structures and tune its morphologies depending on various applied AC signals . Additionally, we show how dendritic devices may exhibit synaptic plasticity properties (short-term and long-term memory effec t). Finally, we demonstrate implementation of structural plasticity through spike-event activation AC-electropolymerization and the possibility to modify the weight of obtained dendritic connections with respect to spike rate intensity of the applied sig nals.

Abstract: Iono-electronic materials and devices are suscitating lots of interest from both bio-electronics and neuromorphic research communities. In the one hand, iono-electronic materials are offering attractive features such as bio-compatibility, water environme nt operation and efficient ionic to electronic signals transduction. In the other hand, functional devices based on such materials (organic electrochemical transistors, for instance) have shown multiple neuromorphic features from synaptic plasticity to d endritic integration. This talk will present how electropolymerization of PEDOT:PSS materials can be used in both a bio-electronic and a neuromorphic perspective. Electropolymerization of OECT sensors can indeed be advantageously used for optimizing / tu ning the iono-electronic responses of organic electrochemical transistors, thus paving the way to plastic electrophysiological sensors. Notably, we will show how transconductance and volumetric capacitance are evolving with potenstiostatic electropolymer ization. Secondly, bipolar AC electropolymerization can be used to engineer dendritic-like fibers of PEDOT:PSS. Such unconventional structures can implement various neuromorphic concept such as structural plasticity and synaptic plasticity. Finally, comp uting task taking advantage of both electropolymerized sensors and neuromorphic computing will present temporal patterns classification with reservoir computing approach.

Abstract: Most of today's strategies to interface biology with electronic hardware are based on layered architectures where the front-end of sensing is optimized separately from the back-end for processing/computing signals. Alternatively, biological systems are c apitalizing on distributed architecture where both sensing and computing are mix together and co-optimized. In this talk, we will present our strategy to implement bio-sensing of electroactive cells in a neuromorphic perspective. We will present how orga nic electrochemical transistors can be used to record electrical signals from neural cells. We will show various strategies capitalizing on the versatility of organic materials synthesis and organic device fabrication to tune and adapt the functionalitie s of such bio-sensors. We will then present how these strategies can be efficiently used to realize computing functions directly at the interface with biology. Notably, we will illustrate how a network of ionic sensors can implement the reservoir computi ng concept, a powerful neuromorphic computing approach of particular interest for dynamical signal processing.

Abstract: Electropolymerization is an interesting bottom-up strategy to structure conducting materials at the micro/nano-scale in liquid phase that offers a wide morphological versatility. Since the geometry of these structures governs their electrochemical proper ties, it is fundamental to decipher the mechanisms that rule the polymer assembling upon the electrically-programmed growth to use this phenomenon as a neuro-inspired building block for unconventional information processing. Herein, we investigate variou s electrical parameters of electropolymerization affecting the conducting polymer network geometry. We find that various structures such as dendrites, trees, fractals as well as low-fractality cables can be obtained between the two-wire electrodes, based on applied voltage amplitude, biasing symmetry, bias frequency, the concentration of monomers and electrode configurations. We qualitatively and quantitatively study the relationship between the electrical parameters affecting geometrical parameters of the conducting polymer network as well as electropolymerization dynamics through video and image processing. The systematic analysis shows that an increase in applied voltage leads to higher growth rate, more branches, and lower surface to bulk ratio. At the other hand, an increase in bias frequency leads to higher growth rate, smaller number of branches, and higher surface to volume ratio. In order to model the experimental phenomena, we simulate a simplified version of the problem with two bipolar met al electrodes and an electrolyte filled with dilute moving particles at random positions, reflecting the monomers in the liquid phase. The wire electrodes are biased with AC frequency and the spacio-temporal potential map is evaluated by solving the Lapl ace equation. The motion of the monomer particles is controlled by the electric field and by random Brownian motion. The particles that happen to touch the electrodes are stuck to the electrodes, with a certain probability of sticking. The stuck particle s are incorporated into the electrode, and the potential is recalculated for the motion of the particles;. The simulations are tested for different AC applied voltages, frequency, duty cycles, and voltage offsets. The increase in applied voltage leads to higher sticking probability; resulting in a higher growth rate, multiple branches, and higher density, which goes well with the experiments. Further, the effect of frequency, which is not so intuitive, shows that higher AC frequency, favors linear cable like growth, while lower frequency leads to more isotropic growth, while voltage offset and non-equal duty cycle lead to asymmetrical growth, in accordance with experiments. In addition, the effect of electrode spacing, different electrode designs, elec trical pulse shapes and the concentration of particles, are also studied. The study helps in visualizing the motion of particles in different electrical conditions, which is not possible to probe experimentally. Some subtle experimental features, such as the effect of preferential growth on the tips, and broadening of the electrode before touching, are observed in the modeling studies. Thus, we find that the different network architectures are associated with different Laplace end diffusion fields gover ning the monomers motion and in turn electropolymerized network geometry. Such unconventional engineering route could have a variety of applications from neuromorphic engineering to bottom-up computing strategies.

Abstract: Most of today's strategies to interface biology with electronic hardware are based on layered architectures where the front-end of sensing is optimized separately from the back-end for processing/computing signals. Alternatively, biological systems are c apitalizing on distributed architecture where both sensing and computing are mix together and co-optimized. In this talk, we will present our strategy to implement bio-sensing of electroactive cells in a neuromorphic perspective. We will present how orga nic electrochemical transistors can be used to record electrical signals from neural cells. We will show various strategies capitalizing on the versatility of organic materials synthesis and organic device fabrication to tune and adapt the functionalitie s of such bio-sensors. We will then present how these strategies can be efficiently used to realize computing functions directly at the interface with biology. Notably, we will illustrate how a network of ionic sensors can implement the reservoir computi ng concept, a powerful neuromorphic computing approach of particular interest for dynamical signal processing.

Abstract: One of the neuromorphic engineering aims is using nanoelectronics' materials and devices to reproduce key features that are used by the brain for computing. Currently, neuromorphic engineering has explored standard silicon-based technologies (i.e. such a s complementary metal-oxide-semiconductor ) or more emerging material and devices (iono-electronic materials and resistive memory devices, for example). Most of these technologies are still bounded to a top down approach. However, brain computing largely rely on bottom-up processes. For instance, interconnectivity between cells and formation of communication pathway in neural networks result principaly from bottom-up organization. Here, we show how dendritic growth of organic conductive polymers (PEDOT) can be used to mimic structural branching observed in neural network. Conducting-polymer based dendritic structures with different morphology are synthesized in a two-electrode setup by pulsed voltage-driven electropolymerization derived from state-of-t he-art bipolar AC-electrochemical synthetic methods. We show how various AC signals can lead to a large variety of dendritic structures and PEDOT morphologies. In a second part, such dendritic structures are used to implement functionnal OECTs. More impo rtantly, we focus on the transconductance and memory effects that can be obtained in such dendritic OECTs such as short tem plasticity. We report on the relationship between dendrites morphologies and STP time constant. This work paves the way to new app roaches for neuromorphic engineering, such as structural plasticity and neural network topology exploration.