Wiring Nanoscale Biosensors with Piezoelectric Nanomechanical Resonators

Nanoscale integrated circuits and sensors will require methods for unobtrusive interconnection with the macroscopic world to fully realize their potential. We report on a nanoelectromechanical system that may present a solution to the wiring problem by enabling information from multisite sensors to be multiplexed onto a single output line. The basis for this method is a mechanical Fourier transform mediated by piezoelectrically coupled nanoscale resonators. Our technique allows sensitive, linear, and real-time measurement of electrical potentials from conceivably any voltage-sensitive device. With this method, we demonstrate the direct transduction of neuronal action potentials from an extracellular microelectrode. This approach to wiring nanoscale devices could lead to minimally invasive implantable sensors with thousands of channels for in vivo neuronal recording, medical diagnostics, and electrochemical sensing

Transient Information Flow in a Network of Excitatory and Inhibitory Model Neurons: Role of Noise and Signal Autocorrelation

We investigate the performance of sparsely-connected networks of integrate-and-fire neurons for ultra-short term information processing. We exploit the fact that the population activity of networks with balanced excitation and inhibition can switch from an oscillatory firing regime to a state of asynchronous irregular firing or quiescence depending on the rate of external background spikes. We find that in terms of information buffering the network performs best for a moderate, non-zero, amount of noise. Analogous to the phenomenon of stochastic resonance the performance decreases for higher and lower noise levels. The optimal amount of noise corresponds to the transition zone between a quiescent state and a regime of stochastic dynamics. This provides a potential explanation on the role of non-oscillatory population activity in a simplified model of cortical micro-circuits.Comment: 27 pages, 7 figures, to appear in J. Physiology (Paris) Vol. 9

Fluctuation-enhanced sensing

We present a short survey on fluctuation-enhanced gas sensing. We compare some of its main characteristics with those of classical sensing. We address the problem of linear response, information channel capacity, missed alarms and false alarms.Comment: Keynote Talk at SPIE's 4th international symposium on Fluctuations and Noise, Conference Noise and Fluctuations in Circuits, Devices and Materials, Florence, Italy, May 20-24, 200

Offset Electrodes for Enhanced Neural Recording in Microchannels

Microchannel electrodes have emerged in recent years as promising interfaces for recording signals in peripheral nerves. Unlike many technologies, microchannels maintain stable long-term connections and can record activity in individual or small groups of axons. Unfortunately, a traditional symmetrical mid-channel electrode configuration, designed to reduce noise artifacts, prevents microchannels from being used to distinguish between signals traveling in opposite directions. This is a profound limitation given that most nerves contain a mix of efferent and afferent axons and microchannels were initially conceived and later used as the basic building block in arrays designed to record bi-directional neural traffic in regenerated nerve fibers. Off-center, or “offset”, recording sites have been predicted to record larger signals than mid-channel locations. Unlike the mid-channel configuration, offset electrode asymmetry suggests it has the capacity to differentiate between efferent and afferent neural activity. Despite these apparent advantages, a theoretical basis for signal enhancement at offset locations has not been identified and, to our knowledge, no efforts to leverage offset electrodes for signal enhancement or discrimination in microchannels have been undertaken. This work provides a theoretical basis to explain signal enhancement at offset electrodes. The theory is used to explore offset electrode configurations that maximize signal amplitudes and enhance differences between signals traveling in opposite directions. Neural recordings are used to validate theoretical predictions and to explore novel reference configurations that seek to minimize noise artifacts. Key shape differences between signals recorded for action potentials traveling in opposite directions are characterized and exploited to further enhance signal discrimination at offset electrodes, as well as to reduce the rate of overlapping spikes in more complex neural recording scenarios, including compound action potentials. Overall, this work introduces the offset electrode configuration as a new paradigm for recording signals in peripheral nerves and provides a foundation for the development of future devices with enhanced performance and signal discrimination capabilities.Off-center, or “offset”, recording sites have been predicted to record larger signals than mid-channel locations. Unlike mid-channel electrodes, offset electrode asymmetry suggests they have the capacity to differentiate between efferent and afferent neural activity. Despite these apparent advantages, the theoretical underpinnings for signal enhancement at offset locations has not been identified and, to our knowledge, no efforts have been made to leverage offset electrodes for signal enhancement or discrimination in microchannels. This work provides a theoretical basis to explain signal enhancement at offset electrodes. The theory is used to explore and identify offset electrode configurations that maximize signal amplitudes and seek to enhance differences between signals traveling in opposite direction. Neural recordings in microchannels containing optimally-positioned offset electrodes are used to validate theoretical predictions and to explore novel reference configurations for minimizing noise artifacts. Shape differences between signals recorded at mid-channel and offset locations are characterized and exploited to further enhance signal discrimination at offset electrodes for single units and reduce the rate of overlapping spikes in more complex multi-unit spike trains as well as the compound action potential. Overall, this work demonstrates a new paradigm for neural recording in microchannels that provides a foundation for the development of future devices with enhanced performance and signal discrimination capabilities

SIMPEL: Circuit model for photonic spike processing laser neurons

We propose an equivalent circuit model for photonic spike processing laser neurons with an embedded saturable absorber---a simulation model for photonic excitable lasers (SIMPEL). We show that by mapping the laser neuron rate equations into a circuit model, SPICE analysis can be used as an efficient and accurate engine for numerical calculations, capable of generalization to a variety of different laser neuron types found in literature. The development of this model parallels the Hodgkin--Huxley model of neuron biophysics, a circuit framework which brought efficiency, modularity, and generalizability to the study of neural dynamics. We employ the model to study various signal-processing effects such as excitability with excitatory and inhibitory pulses, binary all-or-nothing response, and bistable dynamics.Comment: 16 pages, 7 figure

Phasic firing and coincidence detection by subthreshold negative feedback: divisive or subtractive or, better, both

Phasic neurons typically fire only for a fast-rising input, say at the onset of a step current, but not for steady or slow inputs, a property associated with type III excitability. Phasic neurons can show extraordinary temporal precision for phase locking and coincidence detection. Exemplars are found in the auditory brain stem where precise timing is used in sound localization. Phasicness at the cellular level arises from a dynamic, voltage-gated, negative feedback that can be recruited subthreshold, preventing the neuron from reaching spike threshold if the voltage does not rise fast enough. We consider two mechanisms for phasicness: a low threshold potassium current (subtractive mechanism) and a sodium current with subthreshold inactivation (divisive mechanism). We develop and analyze three reduced models with either divisive or subtractive mechanisms or both to gain insight into the dynamical mechanisms for the potentially high temporal precision of type III-excitable neurons. We compare their firing properties and performance for a range of stimuli. The models have characteristic non-monotonic input-output relations, firing rate vs. input intensity, for either stochastic current injection or Poisson-timed excitatory synaptic conductance trains. We assess performance according to precision of phase-locking and coincidence detection by the models' responses to repetitive packets of unitary excitatory synaptic inputs with more or less temporal coherence. We find that each mechanism contributes features but best performance is attained if both are present. The subtractive mechanism confers extraordinary precision for phase locking and coincidence detection but only within a restricted parameter range when the divisive mechanism of sodium inactivation is inoperative. The divisive mechanism guarantees robustness of phasic properties, without compromising excitability, although with somewhat less precision. Finally, we demonstrate that brief transient inhibition if properly timed can enhance the reliability of firing.Postprint (published version

Signal Propagation in Feedforward Neuronal Networks with Unreliable Synapses

In this paper, we systematically investigate both the synfire propagation and firing rate propagation in feedforward neuronal network coupled in an all-to-all fashion. In contrast to most earlier work, where only reliable synaptic connections are considered, we mainly examine the effects of unreliable synapses on both types of neural activity propagation in this work. We first study networks composed of purely excitatory neurons. Our results show that both the successful transmission probability and excitatory synaptic strength largely influence the propagation of these two types of neural activities, and better tuning of these synaptic parameters makes the considered network support stable signal propagation. It is also found that noise has significant but different impacts on these two types of propagation. The additive Gaussian white noise has the tendency to reduce the precision of the synfire activity, whereas noise with appropriate intensity can enhance the performance of firing rate propagation. Further simulations indicate that the propagation dynamics of the considered neuronal network is not simply determined by the average amount of received neurotransmitter for each neuron in a time instant, but also largely influenced by the stochastic effect of neurotransmitter release. Second, we compare our results with those obtained in corresponding feedforward neuronal networks connected with reliable synapses but in a random coupling fashion. We confirm that some differences can be observed in these two different feedforward neuronal network models. Finally, we study the signal propagation in feedforward neuronal networks consisting of both excitatory and inhibitory neurons, and demonstrate that inhibition also plays an important role in signal propagation in the considered networks.Comment: 33pages, 16 figures; Journal of Computational Neuroscience (published