the analog and digital domains at various scales of time and distance, [1] exhibit manyto-one-to-many connectivity, [2] implement global as well as local control of neuronal signaling, [3] and can reorganize connectivity based on necessity. [4] Taken together, these properties allow the nervous system to operate efficiently, multitask, and retain information for years at a time.Several of the computational paradigms used by the brain have been implemented in the constantly evolving technological field of neuromorphic computing. For example, architecture-based approaches include the design of systems that replicate neural spiking and complex connectivity in the form of silicon-based spiking array processors. [5] Memristor arrays have been widely implemented in neuromorphic applications, as they feature a high degree of interconnectivity, the capacity for 3D integration, and access to multiple conductance states. [6] Three-terminal devices have also been shown to exhibit behaviors mimicking synaptic plasticity at the level of a single transistor and have been assembled into networks to accomplish classification tasks. [7] Organic electrochemical transistors (OECTs) have proven to be particularly attractive for neuromorphic applications, in part due to the recent advances reported by Keene et al. to increase the density and reduce the volatility of conductance states. [8] Additionally, the low voltages required to switch between conductance states in OECTs translate into low energy consumption. [7f-i,9] Higher order effects, like functional connectivity and the incorporation of sensors and actuators into neuromorphic circuits, have also been explored. [7h,10] Heretofore reported implementations of neuromorphic analog devices and circuits have proven very effective in executing a panel of biomimetic behaviors like spatiotemporal signal processing, pattern recognition, and global regulation. [11] However, in sharp contrast to biological neuronal networks, the systems investigated to date are fundamentally static in that the interconnectivity and conductance range of each component is predetermined during fabrication. Since the wiring and rewiring of neuronal connectivity is an integral mechanism of neuroplasticity, [12] we were motivated to introduce a comparable process at an analog neuromorphic device. We thus developed the first evolvable OECT (EOECT) that is capable of in situ channel formation through electropolymerization of a conductive polymer between metal source and drain contacts in addition to short-term and long-term learning behaviors. [13] The dynamic nature of the EOECT affords additional degrees of freedom in constructing neuromorphic circuits by making Biomimicry at the hardware level is expected to overcome at least some of the challenges, including high power consumption, large footprint, twodimensionality, and limited functionality, which arise as the field of artificial intelligence matures. One of the main attributes that allow biological systems to thrive is the successful interpretation...