Dynamical systems that evolve unidirectionally with respect to time provide a natural mechanism for implementing a time-domain, near-zero-threshold energy rectifier. In this paper we implement such a dynamical system using a pair of differential, leaky floating-gates and demonstrate that the circuit can sense and record signals of interest while compensating for environmental variations. A Fowler-Nordheim (FN) tunneling current has been used to implement the leakage process, which we experimentally show can be modulated by signals at energy levels below femtojoules. At this level of energy, the proposed FN-system could be self-powered using different types of biopotential energy sources like intra-cellular potentials, a feature that was not possible with previously reported recorders. Furthermore, the degree of modulation is shown to be a function of the input intensity as well as time-of-occurrence, which opens up the possibility of using reconstruction techniques to reconstruct the input signal from measurement of multiple sensing devices. Using devices fabricated in a 0.5 μm standard CMOS process, we demonstrate recording of 6 mV events with retention capability lasting over 30 minutes.

Cyborg insects provide a unique platform to implement autonomous robotics on a large scale. Compared to vertebrates, insect cyborgs can be deployed in swarms and at a significantly lower cost. In this paper, we propose a cyborg sensing platform that leverages the acute olfactory sensing capability of locusts (Schistocerca americana) for standoff detection of target chemicals. Contrary to cyborg sensing technologies that are based on implantable neural devices, the proposed platform relies on extraneous palp tracking which can be measured non-invasively for extended periods of time. In this work, locusts are conditioned (trained) to respond (move their palp) to a specific target odor and the palp movements are measured in real-time using a silver-enhanced infrared reflectance technique. The measured results correlate well with a gold-standard palp tracking method. The efficacy of our platform based on behavioral readout is demonstrated for non-invasive chemical sensing.

Prior to the demonstration, several locusts will be trained on a target odor (eg. Hexanol), according to the protocol described in the appended paper. The demonstration setup (Fig 1) consists of a trained locust (free or restrained) enclosed in a transparent enclosure, an odor delivery system and a palp tracking system. The odor delivery system contains an air pump with two outputs. One of the outputs (main line) goes directly to the locust through a filter. This tube helps in maintaining a constant flow rate over the duration of the experiment. The other output passes through an odor bottle and joins the main tube. The flow through this tube is controlled by a solenoid valve that is operated by the demonstrator. The odor bottles will contain either hexanol (target odor) or benzaldehyde (control). Entire setup occupies less than 3 feet by 2 feet and requires one power outlet for driving the pump.

A 220 by 128 current mode imaging sensor with polarization sensitivity is used to image intrinsic neural activity in vivo from the antenna lobe of a locust. The polarization sensitivity is achieved via post-CMOS fabrication deposition of pixelated aluminum nanowire polarization filters at four different orientations offset by 45 degrees. The measured change in polarization response from the neural cells baseline for hexanol is 0.38%, for octanol is 0.15%, and for the combined odor is 0.45%.

In this paper, we present recent work on bioinspired polarization imaging sensors and their applications in biomedicine. In particular, we focus on three different aspects of these sensors. First, we describe the electro-optical challenges in realizing a bioinspired polarization imager, and in particular, we provide a detailed description of a recent low-power complementary metal-oxide-semiconductor (CMOS) polarization imager. Second, we focus on signal processing algorithms tailored for this new class of bioinspired polarization imaging sensors, such as calibration and interpolation. Third, the emergence of these sensors has enabled rapid progress in characterizing polarization signals and environmental parameters in nature, as well as several biomedical areas, such as label-free optical neural recording, dynamic tissue strength analysis, and early diagnosis of flat cancerous lesions in a murine colorectal tumor model. We highlight results obtained from these three areas and discuss future applications for these sensors.

This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology.

We present a model of olfactory bulb-cortex interaction for the purpose of mixture processing with gas sensor arrays. The olfactory bulb is modeled with a neurodynamic model whose lateral inhibitory connections are learned through a modified Hebbian-anti-Hebbian rule. Bulbar outputs are then projected in a non-topographic fashion onto the olfactory cortex. Associational connections within cortex using Hebbian learning form a content addressable memory. Finally, inhibitory feedback from cortex is used to modulate bulbar activity. Depending on the form of feedback, Hebbian or anti-Hebbian, the model is able to perform background suppression or mixture segmentation. The model is validated on experimental data from a gas sensor array.

We propose a biologically inspired model of olfactory processing for chemosensor arrays. The model captures three functions in the early olfactory pathway: chemotopic convergence of receptor neurons onto the olfactory bulb, center on-off surround lateral interactions, and adaptation to sustained stimuli. The projection of ORNs onto glomerular units is simulated with a self-organizing model of chemotopic convergence, which leads to odor specific spatial patterning. This information serves as an input to a network of mitral cells with center on-off surround lateral inhibition, which enhances the initial contrast among odors and decouples odor identity from intensity. Finally, slow adaptation of mitral cells adds a temporal dimension to the spatial patterns that further enhances odor discrimination. The model is validated using experimental data from an array of temperature-modulated metal-oxide sensors.

This paper presents a neuromorphic model of two olfactory signal- processing primitives: chemotopic convergence of olfactory receptor neurons, and center on-off surround lateral inhibition in the olfactory bulb. A self-organizing model of receptor convergence onto glomeruli is used to generate a spatially organized map, an olfactory image. This map serves as input to a lattice of spiking neurons with lateral connections. The dynamics of this recurrent network transforms the initial olfactory image into a spatio-temporal pattern that evolves and stabilizes into odor- and intensity-coding attractors. The model is validated using experimental data from an array of temperature-modulated gas sensors. Our results are consistent with recent neurobiological findings on the antennal lobe of the honeybee and the locust.