Input Spike Rate Research Articles

Exploring the potential of self-pulsing optical microresonators for spiking neural networks and sensing

Photonic platforms are promising for implementing neuromorphic hardware due to their high processing speed, low power consumption, and ability to perform parallel processing. A ubiquitous device in integrated photonics, which has been extensively employed for the realization of optical neuromorphic hardware, is the microresonator. The ability of CMOS-compatible silicon microring resonators to store energy enhances the nonlinear interaction between light and matter, enabling energy efficient nonlinearity, fading memory and the generation of spikes via self-pulsing. In the self-pulsing regime, a constant input signal can be transformed into a time-dependent signal based on pulse sequences. Previous research has shown that self-pulsing enables the microresonator to function as an energy-efficient artificial spiking neuron. Here, we extend the experimental study of single and coupled microresonators in the self-pulsing regime to confirm their potential as building blocks for scalable photonic spiking neural networks. Furthermore, we demonstrate their potential for introducing all-optical long-term memory and event detection capabilities into integrated photonic neural networks. In particular, we show all-optical long-term memory up to at least 10 μs and detection of input spike rates, which is encoded into different stable self-pulsing dynamics.

Communication Theoretical Modeling and Analysis of Tripartite Synapses With Astrocytes in Synaptic Molecular Communication

Astrocytes, the most abundant glial cells in brain, being in physical proximity of pre- and postsynaptic terminals of the chemical synapse, form tripartite synapses. The feedback from astrocytes introduces synaptic plasticity, which modulates information transmission through neurons. Various other synaptic events also cause plasticity, hence combining them in a single model is quite challenging. In this paper, we study the combined effect of short-term depression (STD) and long-term potentiation (LTP) on vesicle release process in a tripartite synapse. STD decreases the release probability due to slower replenishment rates of releasable vesicles, whereas LTP is due to the positive feedback from astrocytes that increases release probability. Thus, we evaluate vesicle release probability and mutual information between input spikes and vesicle release to quantify the effects of STD and LTP on information transmission. Moreover, the effect of different synaptic parameters such as number of releasable vesicles, input spike rate and replenishment rate of the vesicles, is analyzed on information transmission. It is observed that release probability is predominantly affected by LTP, however, presence of STD decreases the achievable average mutual information over time. Furthermore, the synapses with higher number of releasable vesicles are observed to become stronger with time.

A Model for the Diffusive Filling-In Algorithm Operating in Spike Mode

To run cortical circuit simulations in spike mode, i.e. taking into account the neural representation of information in terms of sequences of electrical pulses (also known as spikes), the use of customized hardware, which is specific for this purpose, is recommended. Simulations using more traditional hardware can be prohibitive. In this context, theoretical predictions are important for customized hardware design. For example, theoretical predictions lead to an adequate neuron model choice. To make such theoretical predictions, the cortical circuit simulations are carried out in amplitude mode. Differently from the spike mode, in amplitude mode information is represented by sequences of scalar values that describe neural input and output spike rates. In this paper, it was proposed amplitude and spike mode simulations of a cortical algorithm, namely the diffusive filling-in algorithm, to investigate whether predictions based on the amplitude-mode results approximate well the behavior of the customized hardware (spike mode results). The diffusive filling-in algorithm was chosen because it is simple enough for spike-mode simulation in a conventional computer, but the proposed amplitude-mode prediction method is the same for more complex algorithms or circuits. We provide a highly realistic comparison between amplitude-mode and spike-mode in the diffusive filling-in case, which suggests that the amplitude mode is reliable for theoretical predictions useful for customized hardware design for cortical circuit simulation. The goal of this paper is not to bring closure to these discussions but to suggest a way of avoiding possible issues that could compromise the success of the customized device design.

Short-term synaptic depression can increase the rate of information transfer at a release site.

The release of neurotransmitters from synapses obeys complex and stochastic dynamics. Depending on the recent history of synaptic activation, many synapses depress the probability of releasing more neurotransmitter, which is known as synaptic depression. Our understanding of how synaptic depression affects the information efficacy, however, is limited. Here we propose a mathematically tractable model of both synchronous spike-evoked release and asynchronous release that permits us to quantify the information conveyed by a synapse. The model transits between discrete states of a communication channel, with the present state depending on many past time steps, emulating the gradual depression and exponential recovery of the synapse. Asynchronous and spontaneous releases play a critical role in shaping the information efficacy of the synapse. We prove that depression can enhance both the information rate and the information rate per unit energy expended, provided that synchronous spike-evoked release depresses less (or recovers faster) than asynchronous release. Furthermore, we explore the theoretical implications of short-term synaptic depression adapting on longer time scales, as part of the phenomenon of metaplasticity. In particular, we show that a synapse can adjust its energy expenditure by changing the dynamics of short-term synaptic depression without affecting the net information conveyed by each successful release. Moreover, the optimal input spike rate is independent of the amplitude or time constant of synaptic depression. We analyze the information efficacy of three types of synapses for which the short-term dynamics of both synchronous and asynchronous release have been experimentally measured. In hippocampal autaptic synapses, the persistence of asynchronous release during depression cannot compensate for the reduction of synchronous release, so that the rate of information transmission declines with synaptic depression. In the calyx of Held, the information rate per release remains constant despite large variations in the measured asynchronous release rate. Lastly, we show that dopamine, by controlling asynchronous release in corticostriatal synapses, increases the synaptic information efficacy in nucleus accumbens.

Joint Optimization of Input Spike Rate and Receiver Decision Threshold to Maximize Achievable Bit Rate of Neuro-Spike Communication Channel

Nano-networks employ novel nano-scale communication techniques. These techniques are inspired by biological systems. Neuro-spike communication is an example of this communication paradigm. A new example of nano-networks is the artificial neural system where nano-machines are linked to neurons to treat the neurodegenerative diseases. In these networks, nano-machines are used to replace the damaged segments of the nervous system and they behave exactly like biological entities. In this paper, by considering a point-to-point model for the neuro-spike communication which contains several sources of randomness such as the axonal noise, random amplitude, and the synaptic noise, we analyze the achievable bit rate of the neuro-spike communication channel. This model can be divided into two main parts. The first part is the axonal pathway and the second one encompasses the synaptic transmission and the spike generation. First, we focus on the axonal pathway and we model this part as a binary channel through defining the axonal shot noise probability. Next, we investigate the synaptic transmission part to model this part as a binary channel. Then, we consider the neuro-spike communication channel as two cascaded binary channels to obtain its error probability and achievable bit rate. To enhance the achievable bit rate of the neuro-spike communication, we design an optimum spike detection receiver and also we propose an optimum input spike rate. We derive closed-form descriptions for the decision threshold of the optimum receiver and the optimum input spike rate. Since they are coupled, we propose a recursive scheme to derive their optimum values. Finally, simulation results are used to investigate the impact of the axonal noise, the synaptic noise, and modifications of the spikes' shape due to propagation along the axon on the achievable bit rate of the neuro-spike communication channel.

An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.

Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic, prosthetic, and industrial applications.

A Cross-layer based mapping for spiking neural network onto network on chip

Network-on-Chip provides a packet-based and scalable inter-connected structure for spiking neural networks. However, existing neural mapping methods just distribute all neurons of a population into an on-chip network core or nearby cores sequentially. As there is no connection among population, the population based mapping degrades inter-neuron communicating performance between different cores. This paper presents a Cross-LAyer based neural MaPping method that maps synaptic connected neurons belonging to adjacent layers into the same on-chip network node. In order to adapt to various input patterns, the strategy also takes input spike rate into consideration and remap neurons for improving mapping efficiency. The method helps to reduce inter-core communication cost. The experimental results demonstrate the efficient results of the proposed mapping strategy in the aspect of spike transfer latency as well as dynamic energy cost improvement. In the applications of handwritten digits and edge extraction, in which the type of interconnection among neurons is different, the neural mapping algorithm reduces spike average transfer latency by maximum 42.83%, and reduces dynamic energy by maximum 36.29%.

Axonal Channel Capacity in Neuro-Spike Communication.

Novel nano-scale communication techniques are inspired by biological systems. Neuro-spike communication is an example of this communication paradigm which transfers vital information about external and internal conditions of the body through the nervous system. The analysis of this communication paradigm is beneficial to exploit in the artificial neural systems where nano-machines are linked to neurons to treat the neurodegenerative diseases. In these networks, nano-machines are used to replace the damaged segments of the nervous system and they exactly behave like biological entities. In neuro-spike communication, neurons / nano-machines exploit the electro-chemical spikes and molecular communication to transfer information. This communication paradigm can be divided into three main parts, namely the axonal pathway, the synaptic transmission, and the spike generation. In this paper, we focus on the axonal transmission part as a separate channel since the capacity of the axonal pathway has a significant effect on the capacity of neuro-spike communication channel. In thinner axons, the capacity of this part is the bottleneck of the neuro-spike communication channel capacity. Hence, we investigate the restricting factors of the axonal transmission which limit its capacity. We derive the capacity of single-input single-output and multiple-input single-output (MISO) axonal channels. In the MISO case, we investigate the effect of the correlation among inputs on the channel capacity. Moreover, we derive a closed form description for the optimum value of the input spike rate to maximize the capacity of the axonal channel when the information is encoded by firing rate of neurons / nano-machines.

Information-theoretic analysis of a dynamic release site using a two-channel model of depression

Synapses are dynamic communication channels between neurons as their rates of information transfer depend on past history. While information theory has been used to study the information efficacy of synapses [1-3], the effect of synaptic dynamics, including short-term depression and facilitation, on the information rate is not yet fully understood. To reduce the complexity of the problem, we confine ourselves here to a single release site of the synapse. This allows us to analytically calculate the information transfer at the release site for a simple model of synaptic depression which is based on binary channels. The input of the model is a spike train, modeled by an independent identically distributed process X={Xi}i=1∞, where each Xi has a Bernoulli distribution with P(Xi = 0) = α. The model's output is a process Y={Yi}i=1∞, such that if there is a release at time, then Yi = 1 and otherwise Yi = 0. We model the short term depression by two binary asymmetric channels that represent the possible states of the release site: the 'recovered' state, when no release occurred in the previous time step (Figure ​(Figure1a),1a), and the 'used' state, following vesicle release (Figure ​(Figure1b).1b). In particular, we assume that the release probability is reduced following a release, that is p2 ≤ p1 and 1 - q2 ≤ 1 - q1. Figure 1 Each individual channel in Figure ​Figure11 will have a mutual information rate, either r1 or r2. As Xi is Bernoulli-distributed, ri=h(αpi+α¯qi¯)-αh(pi)-α¯h(qi¯) for i = 1,2, where h(·) is the entropy of a Bernoulli random variable and x¯=1-x. We prove that the mutual information rate of the release site with depression is a linear summation of the information rates of these two channels. The mutual information rate I(X;Y) between the input process X nd the output process Y, is I(X;Y) = θr1 + (1 - θ)r2 where θ=αp2¯+α¯q2α(p1+p2¯)+α(q1¯+q2) The closed form expression of the mutual information rate allows us to study the effect of depression analytically. Through simulations we show that for a range of parameters, depression improves the rate of information transfer at the release site. We also show that when the level of depression is increased (i.e., with smaller p2 and larger q2), the release site's information capacity is reached at lower input spike rates. Therefore, the optimal spike rate of the presynaptic neuron has a reverse relationship with the depression level of its release site. This means that synaptic depression can save energy while maintaining information rate. The two-channel model of release site is a building block for the construction of more precise models of synaptic transmission. These advanced models will enable us to evaluate and study the synaptic information rates analytically.

A 0.7 V, 40 nW Compact, Current-Mode Neural Spike Detector in 65 nm CMOS.

In this paper, we describe a novel low power, compact, current-mode spike detector circuit for real-time neural recording systems where neural spikes or action potentials (AP) are of interest. Such a circuit can enable massive compression of data facilitating wireless transmission. This design can generate a high signal-to-noise ratio (SNR) output by approximating the popularly used nonlinear energy operator (NEO) through standard analog blocks. We show that a low pass filter after the NEO can be used for two functions-(i) estimate and cancel low frequency interference and (ii) estimate threshold for spike detection. The circuit is implemented in a 65 nm CMOS process and occupies 200 μm × 150 μ m of chip area. Operating from a 0.7 V power supply, it consumes about 30 nW of static power and 7 nW of dynamic power for 100 Hz input spike rate making it the lowest power consuming spike detector reported so far.

Determinants of gain modulation enabled by short-term depression at an inhibitory cerebellar synapse

Neurons adapt rapidly the slope, also known as gain, of their input-output function to time-varying conditions. Gain modulation is a prominent mechanism in many brain processes, such as auditory processing and attention scaling of orientation tuning curves. It is known to amplify neuronal signals, prevent firing saturation, and play a key role in coordinate transformation [1]. Synaptic short-term depression (STD) at the excitatory synapse from mossy fibres (MFs) to granule cells in the cerebellum has previously been found to introduce a gain change, and enhance inhibition-mediated gain modulation [2]. Similar results were discovered for STD at the inhibitory synapse from Purkinje cells (PCs) to cerebellar nucleus (CN) neurons, where STD modulates gain and enhances excitation-mediated gain modulation [3]. In both cases – whether STD is applied at the excitatory or inhibitory synapse, respectively – the non-linearity introduced by STD in the relationship between input firing rate and average conductance, was found to underlie the effects of STD. We use a multi-compartmental model of a cerebellar nucleus neuron [4] to understand how STD at an inhibitory synapse can add a multiplicative component in the transformation performed by excitatory input. To do so, we use input from PCs, applied at an inhibitory synapse with STD, and excitatory input from MFs, while changing the level of STD by manipulating the presynaptic release probability (R) [5]. We find that gain modulation resulting from the introduction of STD increases with the extent of depression. To further our understanding, we investigate the effects of STD using synchronous input, regular input, and their combination. We find that the multiplicative component introduced by STD remains, but varies in value for different input conditions. Moreover, we present a detailed analysis of how a non-linear mapping between input spike rate and synaptic conductance can result in multiplicative operations.

Leptin modulates the intrinsic excitability of AgRP/NPY neurons in the arcuate nucleus of the hypothalamus.

The hypothalamic arcuate nucleus (ARH) is a brain region critical for regulation of food intake and a primary area for the action of leptin in the CNS. In lean mice, the adipokine leptin inhibits neuropeptide Y (NPY) and agouti-related peptide (AgRP) neuronal activity, resulting in decreased food intake. Here we show that diet-induced obesity in mice is associated with persistent activation of NPY neurons and a failure of leptin to reduce the firing rate or hyperpolarize the resting membrane potential. However, the molecular mechanism whereby diet uncouples leptin's effect on neuronal excitability remains to be fully elucidated. In NPY neurons from lean mice, the Kv channel blocker 4-aminopyridine inhibited leptin-induced changes in input resistance and spike rate. Consistent with this, we found that ARH NPY neurons have a large, leptin-sensitive delayed rectifier K(+) current and that leptin sensitivity of this current is blunted in neurons from diet-induced obese mice. This current is primarily carried by Kv2-containing channels, as the Kv2 channel inhibitor stromatoxin-1 significantly increased the spontaneous firing rate in NPY neurons from lean mice. In HEK cells, leptin induced a significant hyperpolarizing shift in the voltage dependence of Kv2.1 but had no effect on the function of the closely related channel Kv2.2 when these channels were coexpressed with the long isoform of the leptin receptor LepRb. Our results suggest that dynamic modulation of somatic Kv2.1 channels regulates the intrinsic excitability of NPY neurons to modulate the spontaneous activity and the integration of synaptic input onto these neurons in the ARH.

Input-rate modulation of gamma oscillations is sensitive to network topology, delays and short-term plasticity

Simulated networks of excitatory and inhibitory neurons have previously been shown to reproduce critical features of experimental data regarding neural coding in V1, such as a positive relationship between thalamic input spike rate and the power of gamma frequency oscillations. This effect, referred to as modulated gamma power, could represent a neural code in V1 for stimulus characteristics that affect thalamic spike rate such as contrast or intensity. The simulated network's assumptions included homogeneous random connectivity, equal synaptic delays after spike arrival, and constant synaptic efficacies. Plausible alternative assumptions include small world connectivity, a wide distribution of axonal propagation delays, and short term synaptic plasticity, and here we assess the individual impact of each of these on the model's success in reproducing modulated gamma power. First, we developed several alternative algorithms for simulating directed networks with clustered connectivity and balanced excitation and inhibition. We found that modulated gamma power was absent in all small-world networks that had a relatively low abundance of reciprocal connectivity, which suggests that such motifs are present in V1 cortical networks at levels at least equal to those found in random networks. We also found in a different network type that the balance of excitation and inhibition could be destroyed when the network was in the small-world regime. Given all neurons had identical in-degrees, this result suggests that balance relies on motif distributions as well as mean connectivity. Second, altering the distribution of axonal delays had little effect, but increasing the mean delay led to a secondary gamma modulation at harmonics of the main peak, and since this is not observed experimentally, it suggests a mean delay in V1 networks less than 2ms. Finally, we compared two types of excitatory synaptic plasticity, and found that modulated beta power emerged in addition to gamma power for one type, in the presence of short term depression in interneurons.This article is part of a Special Issue entitled “Neural Coding”.

Microcircuits of stochastic neurons

Event Abstract Back to Event Microcircuits of stochastic neurons Stefano Cardanobile1* and Stefan Rotter1 1 BCCN Freiburg, Germany Multiplicatively interacting point processes and applications to neural modelling Stefano Cardanobile & Stefan Rotter BCCN Freiburg Mathematical analysis of complex neural network dynamics is both challenging and important for research in neuroscience. Most current approaches, though, rely on mean-field approximations, which have difficulties to evalutate the influence of network structure on its spiking dynamics. We exploit the stochastic nature of neuronal firing and set up a point process framework, based on the observation that the escape noise of real neurons is exponential with respect to their membrane voltage [1]. Assuming linear integration of inputs, this translates into a multiplicative interaction rule on the level of instantaneous firing rates: each incoming spike effectively multiplies the instantaneous firing rate by a fixed "synaptic weight". This approach is in contrast to Hawkes’ linear model [2], where the instantaneous firing rate is given by a convolution of the input spike rate with a linear temporal filter. This effectively prevents the implementation of inhibition in this model. We proved that the equations governing the dynamics of the expected firing rates in our multiplicative system are of Lotka-Volterra type, if one ignores covariances [3]. Based on numerical simulations, we show that this approximation works quite well under very general conditions. Asymptotically, the observed firing rates coincide with the solutions of the associated rate equations even in cases where the rates do not converge to a fixed point, but exhibit transient dynamics. Multiplicatively interacting point processes offer an interesting novel framework for the study of complex neural network dynamics. To illustrate this claim, we finally describe some structured networks that are able to process information, and discuss specifically competing neural populations to describe experiments where rivaling features are perceived. Our model qualitatively replicates the unimodal distribution of dwell times as observed in experiments, and it leads to an intuitive explanation of the switching dynamics. The project has been supported by BMBF grant 01GQ0420 to the BCCN Freiburg.

Preserving Information in Neural Transmission

Along most neural pathways, the spike trains transmitted from one neuron to the next are altered. In the process, neurons can either achieve a more efficient stimulus representation, or extract some biologically important stimulus parameter, or succeed at both. We recorded the inputs from single retinal ganglion cells and the outputs from connected lateral geniculate neurons in the macaque to examine how visual signals are relayed from retina to cortex. We found that geniculate neurons re-encoded multiple temporal stimulus features to yield output spikes that carried more information about stimuli than was available in each input spike. The coding transformation of some relay neurons occurred with no decrement in information rate, despite output spike rates that averaged half the input spike rates. This preservation of transmitted information was achieved by the short-term summation of inputs that geniculate neurons require to spike. A reduced model of the retinal and geniculate visual responses, based on two stimulus features and their associated nonlinearities, could account for >85% of the total information available in the spike trains and the preserved information transmission. These results apply to neurons operating on a single time-varying input, suggesting that synaptic temporal integration can alter the temporal receptive field properties to create a more efficient representation of visual signals in the thalamus than the retina.

Encoding of Naturalistic Stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons

Recordings of local field potentials (LFPs) reveal that the sensory cortex displays rhythmic activity and fluctuations over a wide range of frequencies and amplitudes. Yet, the role of this kind of activity in encoding sensory information remains largely unknown. To understand the rules of translation between the structure of sensory stimuli and the fluctuations of cortical responses, we simulated a sparsely connected network of excitatory and inhibitory neurons modeling a local cortical population, and we determined how the LFPs generated by the network encode information about input stimuli. We first considered simple static and periodic stimuli and then naturalistic input stimuli based on electrophysiological recordings from the thalamus of anesthetized monkeys watching natural movie scenes. We found that the simulated network produced stimulus-related LFP changes that were in striking agreement with the LFPs obtained from the primary visual cortex. Moreover, our results demonstrate that the network encoded static input spike rates into gamma-range oscillations generated by inhibitory–excitatory neural interactions and encoded slow dynamic features of the input into slow LFP fluctuations mediated by stimulus–neural interactions. The model cortical network processed dynamic stimuli with naturalistic temporal structure by using low and high response frequencies as independent communication channels, again in agreement with recent reports from visual cortex responses to naturalistic movies. One potential function of this frequency decomposition into independent information channels operated by the cortical network may be that of enhancing the capacity of the cortical column to encode our complex sensory environment.

Adaptive WTA With an Analog VLSI Neuromorphic Learning Chip

In this paper, we demonstrate how a particular spike-based learning rule (where exact temporal relations between input and output spikes of a spiking model neuron determine the changes of the synaptic weights) can be tuned to express rate-based classical Hebbian learning behavior (where the average input and output spike rates are sufficient to describe the synaptic changes). This shift in behavior is controlled by the input statistic and by a single time constant. The learning rule has been implemented in a neuromorphic very large scale integration (VLSI) chip as part of a neurally inspired spike signal image processing system. The latter is the result of the European Union research project Convolution AER Vision Architecture for Real-Time (CAVIAR). Since it is implemented as a spike-based learning rule (which is most convenient in the overall spike-based system), even if it is tuned to show rate behavior, no explicit long-term average signals are computed on the chip. We show the rule's rate-based Hebbian learning ability in a classification task in both simulation and chip experiment, first with artificial stimuli and then with sensor input from the CAVIAR system.

Exact Subthreshold Integration with Continuous Spike Times in Discrete-Time Neural Network Simulations

Very large networks of spiking neurons can be simulated efficiently in parallel under the constraint that spike times are bound to an equidistant time grid. Within this scheme, the subthreshold dynamics of a wide class of integrate-and-fire-type neuron models can be integrated exactly from one grid point to the next. However, the loss in accuracy caused by restricting spike times to the grid can have undesirable consequences, which has led to interest in interpolating spike times between the grid points to retrieve an adequate representation of network dynamics. We demonstrate that the exact integration scheme can be combined naturally with off-grid spike events found by interpolation. We show that by exploiting the existence of a minimal synaptic propagation delay, the need for a central event queue is removed, so that the precision of event-driven simulation on the level of single neurons is combined with the efficiency of time-driven global scheduling. Further, for neuron models with linear subthreshold dynamics, even local event queuing can be avoided, resulting in much greater efficiency on the single-neuron level. These ideas are exemplified by two implementations of a widely used neuron model. We present a measure for the efficiency of network simulations in terms of their integration error and show that for a wide range of input spike rates, the novel techniques we present are both more accurate and faster than standard techniques.