“…For inputting these spatial patterns, each row of pixels was converted to a train of rectangular voltage pulses defined by "high" and "low" stimulus levels (V high and V low , respectively) as has been used elsewhere. [42,57,62] In this scheme, the reservoir consists of n = 5 independently operated devices, each receiving information from a separate pixel row (Figure 2A). The pulse duration for a single pixel is defined as t pixel .…”
Section: Device Simulations For 5 â 5 Digit Classificationmentioning
confidence: 99%
“…These features make Mz-based biomolecular synapses (MzBS) unique candidates for memristor-based AI applications, such as reservoir computing (RC), [39][40][41][42][43] that operate in the time domain. RC relies upon the rich dynamics of physical or simulated nodes to nonlinearly process and separate temporal input signals.…”
Despite its prevalence in neurosensory systems for pattern recognition, event detection, and learning, the effects of sensory adaptation (SA) are not explored in reservoir computing (RC). Monazomycin‐based biomolecular synapse (MzBS) devices that exhibit volatile memristance and short‐term plasticity with two strength‐dependent modes of response are studied: facilitation and facilitation‐then‐depression (i.e., SA). Their ability to perform RC tasks including digit recognition, nonlinear function learning, and aerodynamic gust classification via combination of model‐based device simulations and physical experiments where SA presence is controlled is studied. Simulations exhibiting moderate SA achieve significantly higher accuracy classifying a custom 5 × 5 binary digit set, with experimental validation achieving maximum testing accuracies of 90%. Classifications of the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset achieve a maximum testing accuracy of 94.34% in devices with SA. Fitting error of the Mackey–Glass time series is also significantly reduced by SA. Experimentally obtained pressure distributions representing gusts on an airfoil in a wind tunnel are classified by MzBS reservoirs. Reservoirs exhibiting SA achieve 100% accuracy, unlike MzBS reservoirs without SA and comparable static neural networks.
“…For inputting these spatial patterns, each row of pixels was converted to a train of rectangular voltage pulses defined by "high" and "low" stimulus levels (V high and V low , respectively) as has been used elsewhere. [42,57,62] In this scheme, the reservoir consists of n = 5 independently operated devices, each receiving information from a separate pixel row (Figure 2A). The pulse duration for a single pixel is defined as t pixel .…”
Section: Device Simulations For 5 â 5 Digit Classificationmentioning
confidence: 99%
“…These features make Mz-based biomolecular synapses (MzBS) unique candidates for memristor-based AI applications, such as reservoir computing (RC), [39][40][41][42][43] that operate in the time domain. RC relies upon the rich dynamics of physical or simulated nodes to nonlinearly process and separate temporal input signals.…”
Despite its prevalence in neurosensory systems for pattern recognition, event detection, and learning, the effects of sensory adaptation (SA) are not explored in reservoir computing (RC). Monazomycin‐based biomolecular synapse (MzBS) devices that exhibit volatile memristance and short‐term plasticity with two strength‐dependent modes of response are studied: facilitation and facilitation‐then‐depression (i.e., SA). Their ability to perform RC tasks including digit recognition, nonlinear function learning, and aerodynamic gust classification via combination of model‐based device simulations and physical experiments where SA presence is controlled is studied. Simulations exhibiting moderate SA achieve significantly higher accuracy classifying a custom 5 × 5 binary digit set, with experimental validation achieving maximum testing accuracies of 90%. Classifications of the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset achieve a maximum testing accuracy of 94.34% in devices with SA. Fitting error of the Mackey–Glass time series is also significantly reduced by SA. Experimentally obtained pressure distributions representing gusts on an airfoil in a wind tunnel are classified by MzBS reservoirs. Reservoirs exhibiting SA achieve 100% accuracy, unlike MzBS reservoirs without SA and comparable static neural networks.
“…As a result, changes to the composition of the membrane or the types of incorporated ion channels can be used to alter the current-voltage relationships. [84][85][86]110 The reversible redox properties of ferritin metalloprotein molecules is another example of intrinsic memristance. [60][61][62] The intact living tissues shown to exhibit memristance are complex in comparison to manmade systems and may have multiple sources contributing to their observed i-v relationships, including intrinsic mechanisms enabled by potassium channel activity 171 as well as extrinsic electrochemical interactions with the electrodes.…”
Section: Perspectives and Concluding Remarksmentioning
confidence: 99%
“…Additionally, biomembrane memristors doped with alamethicin have been shown through simulation to be a candidate platform for reservoir computing (RC). 110 RC uses dynamic inputs in conjunction with the internal states of volatile memristors to nonlinearly separate outputs that can classify temporal signals. 18,110–112 These results suggest that new types of SRNNs, constructed with biomembrane-based synaptic mimics, could be developed for adaptive and efficient near-sensor computing in a variety of applications.Figure 5.Representative species and their memristive properties.…”
Section: Review Of Bioderived Neuromorphic Architecturesmentioning
confidence: 99%
“…110 RC uses dynamic inputs in conjunction with the internal states of volatile memristors to nonlinearly separate outputs that can classify temporal signals. 18,110–112 These results suggest that new types of SRNNs, constructed with biomembrane-based synaptic mimics, could be developed for adaptive and efficient near-sensor computing in a variety of applications.Figure 5.Representative species and their memristive properties. Example pictures of the organisms and their electrode placements (a–c).…”
Section: Review Of Bioderived Neuromorphic Architecturesmentioning
In this concept article, we review recent works on the use of biologically-derived materials, including biomolecules, hybrid biomolecular composites, and natural tissues for achieving stimuli-responsiveness, adaptability, and memory storage for potential neuromorphic computing applications. Unlike solid-state materials, bioderived platforms are often intrinsically modular, including possessing the ability to leverage the diversity of naturally multifunctional biomolecules, cornerstones in biological transduction, signaling, and learning. The primary neuromorphic functionality of bioderived systems considered here is memory resistance, as obtained through combinations of resistive switching, activity-dependent plasticity, and memory storage. In some cases, these capabilities are achieved in bio-derived systems by closely emulating the structure, function, or signaling mechanisms found in living neurons. Our review considers bioderived systems that span multiple length scales and levels of hierarchical complexity: from single-molecule enabled devices to intact tissues, interrogated either in vitro or in vivo on living organisms. Through this exercise, we offer our perspective on research opportunities in the development and use of synapse- and neuron-inspired bioderived systems, including the prospect of biocompatible, and even tissue-like, neuromorphic materials to smartly monitor and treat injury and disease, deliver therapeutics, and interpret neural activity. These capabilities could unlock a new generation of smart, adaptable bioderived composites that autonomously report, learn (i.e., classify, forecast), compute, and actuate in the direct vicinity of living cells and tissues, i.e. at the edge of biology.
Reservoir computing is a highly efficient machine learning framework for processing temporal data by extracting input features and mapping them into higher dimensional spaces. Physical reservoirs have been realized using spintronic oscillators, atomic switch networks, volatile memristors, etc. However, these devices are intrinsically energy‐dissipative due to their resistive nature, increasing their power consumption. Therefore, memcapacitive devices can provide a more energy‐efficient approach. Herein, volatile biomembrane‐based memcapacitors are leveraged as reservoirs to solve classification tasks and process time series in simulation and experimentally. This system achieves a 99.6% accuracy for spoken‐digit classification and a normalized mean square error of in a second‐order nonlinear regression task. Furthermore, to showcase the device's real‐time temporal data processing capability, a 100% accuracy for an epilepsy detection problem is achieved. Most importantly, it is demonstrated that each memcapacitor consumes an average of 41.5 fJ of energy per spike, regardless of the selected input voltage pulse width, while maintaining an average power of 415 fW for a pulse width of 100 ms, orders of magnitude lower than those achieved by state‐of‐the‐art devices. Lastly, it is believed that the biocompatible, soft nature of our memcapacitor renders it highly suitable for computing applications in biological environments.
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