One of the major limitations of standard top‐down technologies used in today's neuromorphic engineering is their inability to map the 3D nature of biological brains. Here, it is shown how bipolar electropolymerization can be used to engineer 3D networks of PEDOT:PSS dendritic fibers. By controlling the growth conditions of the electropolymerized material, it is investigated how dendritic fibers can reproduce structural plasticity by creating structures of controllable shape. Gradual topologies evolution is demonstrated in a multielectrode configuration. A detailed electrical characterization of the PEDOT:PSS dendrites is conducted through DC and impedance spectroscopy measurements and it is shown how organic electrochemical transistors (OECT) can be realized with these structures. These measurements reveal that quasi‐static and transient response of OECTs can be adjusted by controlling dendrites’ morphologies. The unique properties of organic dendrites are used to demonstrate short‐term, long‐term, and structural plasticity, which are essential features required for future neuromorphic hardware development.

Although materials and processes are different from biological cells’, brain mimicries led to tremendous achievements in parallel information processing via neuromorphic engineering. Inexistent in electronics, we emulate dendritic morphogenesis by electropolymerization in water, aiming in operando material modification for hardware learning. Systematic study of applied voltage-pulse parameters details on tuning independently morphological aspects of micrometric dendrites’: fractal number, branching degree, asymmetry, density or length. Growths time-lapse image processing shows spatial features to be dynamically dependent, and expand distinctively before and after conductive bridging with two electro-generated dendrites. Circuit-element analysis and impedance spectroscopy confirms their morphological control in temporal windows where growth kinetics is finely perturbed by the input frequency and duty cycle. By the emulation of one’s most preponderant mechanisms for brain’s long-term memory, its implementation in vicinity of sensing arrays, neural probes or biochips shall greatly optimize computational costs and recognition required to classify high-dimensional patterns from complex environments.

for converting ionic signals into electronic ones thanks to the unique property of organic mixed ionic-electronic conductors (OMIECs). [4] Ionic concentration from an analyte or ionic currents from electroactive cells can be efficiently sensed/probed and amplified, thus making OECTs attractive sensors. [5] In the perspective of neuromorphic engineering, the same devices are capitalizing on the possibility to engineer devices where ion-electron coupling can be used to implement various synaptic plasticities, from short-term to long-term memory effects. [3,[6][7][8][9] These two aspects have been so far mostly developed independently from each other. In contrast, synapses in biology are combining sensing capabilities with plastic properties to provide some essential aspects of biocomputing. Through their adaptation properties, synapses are enhancing/depressing relevant/irrelevant signals from neurons. They also provide a rich set of non-linear operations to process the spike signals from neural cells. [10] As sensors, synapses are converting chemical signals from sensed neurotransmitters into transduced post-synaptic electric signals as ionic concentration modulation. Such ambivalence existing in biology is the natural example of a non-Von Neumann computing architecture that embeds highly complex biochemical sensing at all nodes in its network, and demonstrates reciprocally the power of the local adaptation of a sensing array that programs according to its environment.In this paper, we show how OECTs can combine these two important features for bio-signal sensing and processing. The corner stone of OECTs behavior is the transconductance, which couples ionic signals to electronic ones. [11] Transconductance can be well described by the coupling between: i) volumetric ionic capacitance allowing for a very large effective surface of interaction between the analyte and the polymer; and ii) efficient electronic transport along the π-conjugated organic chains. Several works have demonstrated routes for optimizing transconductance through either volumetric capacitance or electronic mobility tuning. [12,13] Notably, side-chain engineering on the conductive backbone of the polymer have been recently proposed as a promising chemical engineering route. [14] Here, we show how electropolymerization can be used to adapt post-fabrication of these two intrinsic parameters of OECTs (i.e., volumetric capacitance and electronic mobility) and how this technique Organic electrochemical transistors are considered today as a key technology to interact with a biological medium through their intrinsic ionic-electronic coupling. In this paper, the authors show how this coupling can be finely tuned (in operando) post-microfabrication via the electropolymerization technique. This strategy exploits the concept of adaptive sensing where both transconductance and impedance are tunable and can be modified on-demand to match different sensing requirements. Material investigation through Raman spectroscopy, atomic force microscopy, and scanning ele...