Fire Neuron Research Articles

An Integrated Approach of CNT Front-end Amplifier towards Spikes Monitoring for Neuro-prosthetic Diagnosis

The future neuro-prosthetic devices would be required spikes data monitoring through sub-nanoscale transistors that enables to neuroscientists and clinicals for scalable, wireless and implantable applications. This research investigates the spikes monitoring through integrated CNT front-end amplifier for neuro-prosthetic diagnosis. The proposed carbon nanotube-based architecture consists of front-end amplifier (FEA), integrate fire neuron and pseudo resistor technique that observed high electrical performance through neural activity. A pseudo resistor technique ensures large input impedance for integrated FEA by compensating the input leakage current. While carbon nanotube based FEA provides low-voltage operation with directly impacts on the power consumption and also give detector size that demonstrates fidelity of the neural signals. The observed neural activity shows amplitude of spiking in terms of action potential up to 80 μV while local field potentials up to 40 mV by using proposed architecture. This fully integrated architecture is implemented in Analog cadence virtuoso using design kit of CNT process. The fabricated chip consumes less power consumption of 2 μW under the supply voltage of 0.7 V. The experimental and simulated results of the integrated FEA achieves 60 GΩ of input impedance and input referred noise of 8.5 nv/ $$\rm\sqrt{Hz}$$ over the wide bandwidth. Moreover, measured gain of the amplifier achieves 75 dB midband from range of 1 KHz to 35 KHz. The proposed research provides refreshing neural recording data through nanotube integrated circuit and which could be beneficial for the next generation neuroscientists.

Resonate and fire neuron with fixed magnetic skyrmions

In the brain, the membrane potential of many neurons oscillates in a subthreshold damped fashion and fire when excited by an input frequency that nearly equals their eigen frequency. In this work, we investigate theoretically the artificial implementation of such “resonate-and-fire” neurons by utilizing the magnetization dynamics of a fixed magnetic skyrmion in the free layer of a magnetic tunnel junction (MTJ). To realize firing of this nanomagnetic implementation of an artificial neuron, we propose to employ voltage control of magnetic anisotropy or voltage generated strain as an input (spike or sinusoidal) signal, which modulates the perpendicular magnetic anisotropy. This results in continual expansion and shrinking (i.e., breathing) of a skyrmion core that mimics the subthreshold oscillation. Any subsequent input pulse having an interval close to the breathing period or a sinusoidal input close to the eigen frequency drives the magnetization dynamics of the fixed skyrmion in a resonant manner. The time varying electrical resistance of the MTJ layer due to this resonant oscillation of the skyrmion core is used to drive a Complementary Metal Oxide Semiconductor buffer circuit, which produces spike outputs. By rigorous micromagnetic simulation, we investigate the interspike timing dependence and response to different excitatory and inhibitory incoming input pulses. Finally, we show that such resonate and fire neurons have potential application in coupled nanomagnetic oscillator based associative memory arrays.

Spiking Neural Networks with Unsupervised Learning Based on STDP Using Resistive Synaptic Devices and Analog CMOS Neuron Circuit.

We designed the CMOS analog integrate and fire (I&F) neuron circuit can drive resistive synaptic device. The neuron circuit consists of a current mirror for spatial integration, a capacitor for temporal integration, asymmetric negative and positive pulse generation part, a refractory part, and finally a back-propagation pulse generation part for learning of the synaptic devices. The resistive synaptic devices were fabricated using HfOx switching layer by atomic layer deposition (ALD). The resistive synaptic device had gradual set and reset characteristics and the conductance was adjusted by spike-timing-dependent-plasticity (STDP) learning rule. We carried out circuit simulation of synaptic device and CMOS neuron circuit. And we have developed an unsupervised spiking neural networks (SNNs) for 5 × 5 pattern recognition and classification using the neuron circuit and synaptic devices. The hardware-based SNNs can autonomously and efficiently control the weight updates of the synapses between neurons, without the aid of software calculations.

CMOS event-driven tactile sensor circuit

In this paper, we present a device for low-latency compressive pressure sensing based on the use of a piezoelectric material coupled to mixed-mode neuromorphic CMOS circuits. The sensing element is based on the POSFET (Piezoelectric Oxide-Semiconductor-Field-Effect-Transistor) device, whereby the polarization induced by pressure in the piezoelectric material biases a CMOS transistor. The resulting output current feeds a subthreshold Leaky Integrate and Fire neuron that encodes the sensed force into trains of digital pulses. The proposed device produces output pulses whose instantaneous frequency is proportional to the applied force, only when the input force changes, drastically compressing the signal. The output features also low latency with respect to the change, as it is not limited by the sampling frequency of clocked digital to analog converters. We present the simulation as well as the full electrical and electromechanical characterization of the latest device prototype produced in AMS 180-nm process – coupled with the piezoelectric material – highlighting improvements over older versions, comprising smaller silicon estate, higher sensitivity, lower power consumption and bandwidth.

Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology

The connectivity of a neuronal network has a major effect on its functionality and role. It is generally believed that the complex network structure of the brain provides a physiological basis for information processing. Therefore, identifying the network’s topology has received a lot of attentions in neuroscience and has been the center of many research initiatives such as Human Connectome Project. Nevertheless, direct and invasive approaches that slice and observe the neural tissue have proven to be time consuming, complex and costly. As a result, the inverse methods that utilize firing activity of neurons in order to identify the (functional) connections have gained momentum recently, especially in light of rapid advances in recording technologies; It will soon be possible to simultaneously monitor the activities of tens of thousands of neurons in real time. While there are a number of excellent approaches that aim to identify the functional connections from firing activities, the scalability of the proposed techniques plays a major challenge in applying them on large-scale datasets of recorded firing activities. In exceptional cases where scalability has not been an issue, the theoretical performance guarantees are usually limited to a specific family of neurons or the type of firing activities. In this paper, we formulate the neural network reconstruction as an instance of a graph learning problem, where we observe the behavior of nodes/neurons (i.e., firing activities) and aim to find the links/connections. We develop a scalable learning mechanism and derive the conditions under which the estimated graph for a network of Leaky Integrate and Fire (LIf) neurons matches the true underlying synaptic connections. We then validate the performance of the algorithm using artificially generated data (for benchmarking) and real data recorded from multiple hippocampal areas in rats.

Integrated neuron circuit for implementing neuromorphic system with synaptic device

In this paper, we propose and fabricate Integrate & Fire neuron circuit for implementing neuromorphic system. Overall operation of the circuit is verified by measuring discrete devices and the output characteristics of the circuit. Since the neuron circuit shows asymmetric output characteristic that can drive synaptic device with Spike-Timing-Dependent-Plasticity (STDP) characteristic, the autonomous weight update process is also verified by connecting the synaptic device and the neuron circuit. The timing difference of the pre-neuron and the post-neuron induce autonomous weight change of the synaptic device. Unlike 2-terminal devices, which is frequently used to implement neuromorphic system, proposed scheme of the system enables autonomous weight update and simple configuration by using 4-terminal synapse device and appropriate neuron circuit. Weight update process in the multi-layer neuron-synapse connection ensures implementation of the hardware-based artificial intelligence, based on Spiking-Neural- Network (SNN).

Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET

Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI- MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (~1011 neuron based) large neural networks.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Population density equations for stochastic processes with memory kernels.

We present a method for solving population density equations (PDEs)--a mean-field technique describing homogeneous populations of uncoupled neurons-where the populations can be subject to non-Markov noise for arbitrary distributions of jump sizes. The method combines recent developments in two different disciplines that traditionally have had limited interaction: computational neuroscience and the theory of random networks. The method uses a geometric binning scheme, based on the method of characteristics, to capture the deterministic neurodynamics of the population, separating the deterministic and stochastic process cleanly. We can independently vary the choice of the deterministic model and the model for the stochastic process, leading to a highly modular numerical solution strategy. We demonstrate this by replacing the master equation implicit in many formulations of the PDE formalism by a generalization called the generalized Montroll-Weiss equation-a recent result from random network theory-describing a random walker subject to transitions realized by a non-Markovian process. We demonstrate the method for leaky- and quadratic-integrate and fire neurons subject to spike trains with Poisson and gamma-distributed interspike intervals. We are able to model jump responses for both models accurately to both excitatory and inhibitory input under the assumption that all inputs are generated by one renewal process.

Spike-Based Readout of POSFET Tactile Sensors

We present a low-power compact circuit for the event-driven readout of tactile sensors. The taxel is based on the POSFET device, a sensotronic unit where a piezo-electric material is deposited on the gate of a transistor, integrated with a Leaky-Integrate and Fire neuron circuit. This device encodes the applied force with trains of digital pulses, using the asynchronous Address Event Representation protocol. The instantaneous output spiking frequency is directly proportional to the force applied to the taxel. We show a full characterization of the device as well as quantitative measurements from a prototype fabricated on AMS CMOS 350-nm process. The taxel can reliably distinguish applied forces from 0.1 N up to 1.5 N.

Integrated Circuit with Memristor Emulator Array and Neuron Circuits for Biologically Inspired Neuromorphic Pattern Recognition

This paper details an application-specific integrated circuit (ASIC) with an array of switched-resistor-based memristors (resistor with memory) and integrate & fire (I & F) neuron circuits for the development of memristor-based pattern recognition. Since real memristors are not commercially available, a compact memristor emulator is needed for device study. The designed ASIC has five memristor emulators with one having a conductance range from 4.88[Formula: see text]ns to 4.99[Formula: see text][Formula: see text]s (200[Formula: see text]k[Formula: see text] to 204.8[Formula: see text]M[Formula: see text]) and other four having conductance ranging from 195[Formula: see text]ns to 190[Formula: see text][Formula: see text]s (5.2[Formula: see text]k[Formula: see text] to 5.12[Formula: see text]M[Formula: see text]). Signal processing has been planned to be off-chip to get the freedom of programmability of a wide range of memristive behavior. This paper introduces the memristor emulator and the realization of synapse functionalities used in neuromorphic circuits such as long term potentiation (LTP), Long Term depression (LTD) and synaptic plasticity. The ASIC has two I & F neuron circuits which are intended to be used in conjunction with memristors in a multiple chip network for pattern recognition. This paper explains the memristor emulator, I & F neuron circuit and a respective neuromorphic system for pattern recognition simulated in LTspice. The ASIC has been fabricated in AMS 350[Formula: see text]nm process.

A CORDIC Based Digital Hardware For Adaptive Exponential Integrate and Fire Neuron

This paper presents a COordinate Rotation DIgital Computer (CORDIC) based Adaptive Exponential Integrate and Fire (AdEx) neuron for efficient large scale biological neural network implementation. The accuracy of the modified model is investigated by both calculating various errors and bifurcation analysis; both show that the proposed model follows the same signaling, dynamical behavior, and bifurcation pattern as the original model. Network behavior of the original and proposed models are also observed to be very much alike in following the same activity patterns. The model is hardware synthesized and implemented on FPGA as a proof of concept. Measurement results show that the proposed model can reproduce neuronal behaviors similar to the original model. Hardware device utilization and speed also confirm the efficiency of the realized hardware compared with previous works.

Achieving Synchronization in Cardiac Oscillators with Suitable Neural Input

The normal cardiac rhythm is the result of collective, synchronized action of a large number of cardiac oscillators which play a crucial role in the determination of the sinus rhythm. The physiological function of the cardiovascular system is under the control of the autonomic nervous system (ANS). The two limbs of the ANS, sympathetic and parasympathetic, are critical in determining the oscillations within the heart. The pumping effectiveness of the heart is controlled by the sympathetic and parasympathetic nerves, which abundantly supply the heart that act in opposing ways. However, the two divisions act together to regulate the activity of the internal organs as per the needs of the body at any particular time. The cardiac centers in the central nervous system exert an influence on the heart’s activity through sympathetic and parasympathetic nerves. This influence governs the rate of beat, the systolic contractile force and the velocity of atrioventricular conduction. The parasympathetic stimulation causes a decrease in heart rate whereas sympathetic stimulation increases it. The intrinsic cardiac nervous system is seen to play an active role in regulating cardiac function which consists of sympathetic and parasympathetic neurons and interconnecting local circuits. An appropriate mathematical model that describes the electrical activity and ion exchange in the sinoatrial node (SAN) is considered and the dynamical equations describing the behaviour of the chosen model are solved with the corresponding source code developed and implemented using Matlab.An Integrate and Fire Neuron (IFN) model is developed that mimics the role of ANS which acts as an external influence to the preferred SAN cell to in order to coax synchronization between the coupled cell pair. The influence of the suitable neuron model in effecting synchrony between the coupled SAN cell pair is demonstrated with the aid of simulation studies.

Nonsmooth Bifurcations of Mean Field Systems of Two-Dimensional Integrate and Fire Neurons

Mean field systems have recently been derived that adequately predict the behaviors of large networks of coupled integrate-and-fire neurons [W. Nicola and S.A. Campbell, J. Comput. Neurosci., 35 (2013), pp. 87--108]. The mean field system for a network of neurons with spike frequency adaptation is typically a pair of differential equations for the mean adaptation and synaptic gating variable of the network. These differential equations are nonsmooth, and, in particular, are piecewise smooth continuous (PWSC). Here, we analyze the smooth and nonsmooth bifurcation structure of these equations and show that the system is organized around a pair of co-dimension-two bifurcations that involve, respectively, the collision between a Hopf equilibrium point and a switching manifold, and a saddle-node equilibrium point and a switching manifold. These two co-dimension-two bifurcations can coalesce into a co-dimension-three nonsmooth bifurcation. As the mean field system we study is a nongeneric piecewise smooth conti...

Integrate-and-Fire Neuron Circuit and Synaptic Device using Floating Body MOSFET with Spike Timing-Dependent Plasticity

In the previous work, we have proposed an integrate-and-fire neuron circuit and synaptic device based on the floating body MOSFET [1-3]. Integrateand- Fire(I&F) neuron circuit emulates the biological neuron characteristics such as integration, threshold triggering, output generation, refractory period using floating body MOSFET. The synaptic device has short-term and long-term memory in a single silicon device. In this paper, we connect the neuron circuit and the synaptic device using current mirror circuit for summation of post synaptic pulses. We emulate spike-timing-dependent-plasticity (STDP) characteristics of the synapse using feedback voltage without controller or clock. Using memory device in the logic circuit, we can emulate biological synapse and neuron with a small number of devices.

Extending integrate-and fire model neurons to account for the effects of weak electric fields in the presence of dendrites

Transcranial brain stimulation techniques have recently sparked a strong interest in understanding the effects of weak electric fields on neuronal network dynamics (e.g. [1,2]). The collective dynamics of large populations of coupled neurons can be efficiently studied using single-compartment (point) model neurons of the integrate-and-fire (IF) type [3], which allow for a systematic model reduction at the population level [4,5], as opposed to multi-compartment Hodgkin-Huxley type models and complex morphologies. However, existing point neuron models cannot adequately reproduce the effects of an electric field on the somatic membrane potential, which are influenced by the presence of dendritic processes [2]. Here, we present an extension for IF type point neuron models to take into account the subthreshold effects of an oscillating weak uniform extracellular field, similar to those generated in the brain by transcranial electrical stimulation [6]. Based on a ball-and-stick neuron model (i.e., a passive finite dendritic cable with a lumped soma at its end) we analytically calculate the somatic membrane polarization induced by a weak extracellular electric field using the cable equation. From this polarization we derive an equivalent input current for leaky IF as well as adaptive nonlinear IF point neurons, which explicitly depends on the (soma+dendrite) neuron model and electric field parameters. The extended point neuron model can well reproduce the relationships between electric field properties (intensity, frequency) and neuronal responses (membrane polarization, sensitivity and phase), as observed by simulations of neuron models with complex morphologies and reported in the experimental literature [7]. Our point neuron model extension is simple to implement and well suited for application in IF based neural networks.

Cell assembly dynamics of sparse inhibitory networks: a simple model for the activity of the Medium Spiny Neurons

Here we show that a simple inhibitory network model, made of sparsely connected Leaky Integrate and Fire (LIF) neurons, is able to retrieve some of the relevant dynamical features of a striatal network, in particular the appearance of cell assembly dynamics as it has been reported in in-vitro experiments of rats striatum [1]. In a first approach, we discuss how our simple model is consistent with the findings in [2,3]. For an optimal choice of the model parameters, the response of the neurons to uniformly distributed constant inputs, occurs in a bursting fashion. As shown in Figure. 1B, the neurons organize their dynamics in groups with correlated bursting-like activity, displaying typical recurrent patterns, similarly to the dynamics of Medium Spiny Neurons (MSNs). Furthermore, the firing of the neurons taking part in this structured cell assembly dynamics is characterized by a high variability, as shown in Figure 1A for a few representative neurons. This high variability is reflected in a coefficient of variation of the interspike-interval (ISI) larger than one. An important aspect of the dynamics of the MSNs is the emergence of coexisting correlated and anti-correlated assemblies, as reported in the experimental work by Carrillo-Reid et al. [1]. Indeed also in our system this aspect is present, as revealed by examining the cross-correlation matrix of the firing rates shown in Figure 1C. Here the neurons are grouped in assemblies accordingly to their level of correlation (as in Figure 1B) and it is evident that the correlated activities within the neuronal assemblies can be highly anti-correlated with other cells in the network. Figure 1 A. Firing rates of 5 selected neurons; B. raster plot activity, the firing times are colored accordingly to the assembly the neurons belong to; C. cross-correlation matrix of the firing rates. The neurons in panel B and C are clusterized accordingly to ...

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States.

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation.Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states.Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods.Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.

Tracing evolution of spatio-temporal dynamics of the cerebral cortex: cortico-cortical communication dynamics.

Tracing evolution of spatio-temporal dynamics of the cerebral cortex: cortico-cortical communication dynamics.

Event-driven contrastive divergence for spiking neuromorphic systems.

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.