Transistor analogs of emergent iono‐neuronal dynamics

Abstract

Neuromorphic analog metal-oxide-silicon (MOS) transistor circuits promise compact, low-power, and high-speed emulations of iono-neuronal dynamics orders-of-magnitude faster than digital simulation. However, their inherently limited input voltage dynamic range vs power consumption and silicon die area tradeoffs makes them highly sensitive to transistor mismatch due to fabrication inaccuracy, device noise, and other nonidealities. This limitation precludes robust analog very-large-scale-integration (aVLSI) circuits implementation of emergent iono-neuronal dynamics computations beyond simple spiking with limited ion channel dynamics. Here we present versatile neuromorphic analog building-block circuits that afford near-maximum voltage dynamic range operating within the low-power MOS transistor weak-inversion regime which is ideal for aVLSI implementation or implantable biomimetic device applications. The fabricated microchip allowed robust realization of dynamic iono-neuronal computations such as coincidence detection of presynaptic spikes or pre- and postsynaptic activities. As a critical performance benchmark, the high-speed and highly interactive iono-neuronal simulation capability on-chip enabled our prompt discovery of a minimal model of chaotic pacemaker bursting, an emergent iono-neuronal behavior of fundamental biological significance which has hitherto defied experimental testing or computational exploration via conventional digital or analog simulations. These compact and power-efficient transistor analogs of emergent iono-neuronal dynamics open new avenues for next-generation neuromorphic, neuroprosthetic, and brain-machine interface applications.

A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

“…The transistor count reflects the relative complexity of our biophysically grounded iono-neuromorphic model compared to phenomenological models. Additional transistors were also needed in our wide-dynamicrange subthreshold CMOS circuit designs, which effectively mitigated the effects of transistor mismatch and significantly improved the robustness of aVLSI implementation (44,53). The capacitors occupied the bulk of the chip area as some of them were relatively large (30 pF) in order to achieve a 1∶1 electronicto-biological time scale at nA level currents.…”

“…1A). A set of CMOS building block circuits biased in the subthreshold regime for robust iono-neuromorphic modeling [with wide input dynamic range to overcome device mismatch in subthreshold circuits (44)] are configured to emulate fast AMPA and slower NMDA channels, as described previously (53). The output currents are sent to a membrane node circuit that keeps the membrane potential V MEM near the resting potential V REST in the absence of stimulation (Fig.…”

“…Such hardware vulnerability is mitigated by allowing sufficiently large device area and the use of wide-dynamic-range circuit designs as proposed here (44,53). Interestingly, the STDP algorithm itself can be used to correct for such circuit imperfections, making aVLSI implementations of the STDP learning rule relatively robust to device mismatch (95).…”

“…Our iono-neuromorphic model is based on the hypothesis that retrograde endocannabinoid signaling and presynaptic NMDAR may provide a second coincidence detector of pre-and postsynaptic activity in addition to postsynaptic NMDAR (50)(51)(52). To emulate the underlying biophysical mechanisms, we employ a recently proposed wide-dynamic-range iono-neuromorphic CMOS circuit design approach that allows robust modeling of all types of voltage-dependent or ligand-gated ion channel and intracellular ionic dynamics on analog very-large-scale-integrated (aVLSI) circuits (53). We show that our iono-neuromorphic model readily reproduces LTP and LTD based on either the SRDP or STDP learning rules implemented on the same CMOS chip.…”

Effects of maturation and acidosis on the chaos-like complexity of the neural respiratory output in the isolated brainstem of the tadpole,Rana esculenta

“…In contrast, the lung rhythm does not and is therefore assumed to depend on pace maker neurons (3,20). Electronic models predict that the bursting pattern of such neurons can be chaotic and that as few as three ion channels (one for burst initiation and two for burst termination) would be sufficient, "without the need for any intrinsic or extrinsic stochastic influences or other complex intracellular processes" (41). Furthermore, the pre-Bötzinger complex that drives ventilation in neonatal rodents and contains pace maker neurons, produces also a chaos-like dynamic, when it is sufficiently exited by high concentrations of K ϩ (8).…”