Spike Encoding Research Articles

Encoding of action by the Purkinje cells of the cerebellum.

SummaryExecution of accurate eye movements depends critically on the cerebellum1,2,3, suggesting that Purkinje cells (P-cells) may predict motion of the eye. Yet, this encoding has remained a long-standing puzzle: P-cells show little consistent modulation with respect to saccade amplitude4,5 or direction4, and critically, their discharge lasts longer than duration of a saccade6,7. Here, we analyzed P-cell discharge in the oculomotor vermis of behaving monkeys8,9 and found neurons that increased or decreased their activity during saccades. We estimated the combined effect of these two populations via their projections on the caudal fastigial nucleus (cFN) and uncovered a simple-spike population response that precisely predicted the real-time motion of the eye. When we organized the P-cells according to each cell's complex-spike directional tuning, the simple-spike population response predicted both the real-time speed and direction of saccade multiplicatively via a gain-field. This suggests that the cerebellum predicts the real-time motion of the eye during saccades via the combined inputs of P-cells onto individual nucleus neurons. A gain-field encoding of simple spikes emerges if the P-cells that project onto a nucleus neuron are not selected at random, but share a common complex-spike property.

Wavelet decomposition and phase encoding of temporal signals using spiking neurons

Spike encoding is the initial yet crucial step for any application domain of Artificial Spiking Neural Networks (ASNN). However, current encoding methods are not suitable to process complex temporal signal. Motivated by the modulation relationship found between afferent synaptic currents in biological neurons, this study proposes a spike phase encoding method for ASNN, which could perform wavelet decomposition on the input signal, and encode the wavelet spectrum into synchronized output spike trains. The spike delays in each synchronizing period represent the spectrum amplitudes. The encoding method was tested in two implementation examples: (a) encoding of human voice records for speech recognition propose; and (b) encoding of multichannel electroencephalography (EEG) records with the aim to detect interictal spikes in patients with epilepsy. Empirical evaluations confirm that encoded spike trains constitute a good representation of the continuous wavelet transform of the original signal, with the ability to capture interesting features from the input signal.

Advantages and limitations of the use of optogenetic approach in studying fast-scale spike encoding.

Understanding single-neuron computations and encoding performed by spike-generation mechanisms of cortical neurons is one of the central challenges for cell electrophysiology and computational neuroscience. An established paradigm to study spike encoding in controlled conditions in vitro uses intracellular injection of a mixture of signals with fluctuating currents that mimic in vivo-like background activity. However this technique has two serious limitations: it uses current injection, while synaptic activation leads to changes of conductance, and current injection is technically most feasible in the soma, while the vast majority of synaptic inputs are located on the dendrites. Recent progress in optogenetics provides an opportunity to circumvent these limitations. Transgenic expression of light-activated ionic channels, such as Channelrhodopsin2 (ChR2), allows induction of controlled conductance changes even in thin distant dendrites. Here we show that photostimulation provides a useful extension of the tools to study neuronal encoding, but it has its own limitations. Optically induced fluctuating currents have a low cutoff (~70Hz), thus limiting the dynamic range of frequency response of cortical neurons. This leads to severe underestimation of the ability of neurons to phase-lock their firing to high frequency components of the input. This limitation could be worked around by using short (2 ms) light stimuli which produce membrane potential responses resembling EPSPs by their fast onset and prolonged decay kinetics. We show that combining application of short light stimuli to different parts of dendritic tree for mimicking distant EPSCs with somatic injection of fluctuating current that mimics fluctuations of membrane potential in vivo, allowed us to study fast encoding of artificial EPSPs photoinduced at different distances from the soma. We conclude that dendritic photostimulation of ChR2 with short light pulses provides a powerful tool to investigate population encoding of simulated synaptic potentials generated in dendrites at different distances from the soma.

Simplified spiking neural network architecture and STDP learning algorithm applied to image classification

Spiking neural networks (SNN) have gained popularity in embedded applications such as robotics and computer vision. The main advantages of SNN are the temporal plasticity, ease of use in neural interface circuits and reduced computation complexity. SNN have been successfully used for image classification. They provide a model for the mammalian visual cortex, image segmentation and pattern recognition. Different spiking neuron mathematical models exist, but their computational complexity makes them ill-suited for hardware implementation. In this paper, a novel, simplified and computationally efficient model of spike response model (SRM) neuron with spike-time dependent plasticity (STDP) learning is presented. Frequency spike coding based on receptive fields is used for data representation; images are encoded by the network and processed in a similar manner as the primary layers in visual cortex. The network output can be used as a primary feature extractor for further refined recognition or as a simple object classifier. Results show that the model can successfully learn and classify black and white images with added noise or partially obscured samples with up to ×20 computing speed-up at an equivalent classification ratio when compared to classic SRM neuron membrane models. The proposed solution combines spike encoding, network topology, neuron membrane model and STDP learning.

Changes in Purkinje cell simple spike encoding of reach kinematics during adaption to a mechanical perturbation.

The cerebellum is essential in motor learning. At the cellular level, changes occur in both the simple spike and complex spike firing of Purkinje cells. Because simple spike discharge reflects the main output of the cerebellar cortex, changes in simple spike firing likely reflect the contribution of the cerebellum to the adapted behavior. Therefore, we investigated in Rhesus monkeys how the representation of arm kinematics in Purkinje cell simple spike discharge changed during adaptation to mechanical perturbations of reach movements. Monkeys rapidly adapted to a novel assistive or resistive perturbation along the direction of the reach. Adaptation consisted of matching the amplitude and timing of the perturbation to minimize its effect on the reach. In a majority of Purkinje cells, simple spike firing recorded before and during adaptation demonstrated significant changes in position, velocity, and acceleration sensitivity. The timing of the simple spike representations change within individual cells, including shifts in predictive versus feedback signals. At the population level, feedback-based encoding of position increases early in learning and velocity decreases. Both timing changes reverse later in learning. The complex spike discharge was only weakly modulated by the perturbations, demonstrating that the changes in simple spike firing can be independent of climbing fiber input. In summary, we observed extensive alterations in individual Purkinje cell encoding of reach kinematics, although the movements were nearly identical in the baseline and adapted states. Therefore, adaption to mechanical perturbation of a reaching movement is accompanied by widespread modifications in the simple spike encoding.

Optical stimulation of zebrafish hair cells expressing channelrhodopsin-2.

Vertebrate hair cells are responsible for the high fidelity encoding of mechanical stimuli into trains of action potentials (spikes) in afferent neurons. Here, we generated a transgenic zebrafish line expressing Channelrhodopsin-2 (ChR2) under the control of the hair-cell specific myo6b promoter, in order to examine the role of the mechanoelectrical transduction (MET) channel in sensory encoding in afferent neurons. We performed in vivo recordings from afferent neurons of the zebrafish lateral line while activating hair cells with either mechanical stimuli from a waterjet or optical stimuli from flashes of ∼470-nm light. Comparison of the patterns of encoded spikes during 100-ms stimuli revealed no difference in mean first spike latency between the two modes of activation. However, there was a significant increase in the variability of first spike latency during optical stimulation as well as an increase in the mean number of spikes per stimulus. Next, we compared encoding of spikes during hair-cell stimulation at 10, 20, and 40-Hz. Consistent with the increased variability of first spike latency, we saw a significant decrease in the vector strength of phase-locked spiking during optical stimulation. These in vivo results support a physiological role for the MET channel in the high fidelity of first spike latency seen during encoding of mechanical sensory stimuli. Finally, we examined whether remote activation of hair cells via ChR2 activation was sufficient to elicit escape responses in free-swimming larvae. In transgenic larvae, 100-ms flashes of ∼470-nm light resulted in escape responses that occurred concomitantly with field recordings indicating Mauthner cell activity. Altogether, the myo6b:ChR2 transgenic line provides a platform to investigate hair-cell function and sensory encoding, hair-cell sensory input to the Mauthner cell, and the ability to remotely evoke behavior in free-swimming zebrafish.

Kainate receptors: on the dark side

Synaptic glutamate receptors can be divided into metabotropic receptors, an eight-member GPCR family, and ionotropic glutamate receptors, a three-member family of NMDA, kainate, and AMPA tetrameric receptors. The most neglected are the five subunit (GluK1–5) kainate receptors, often either lumped with the AMPA receptors because of pharmacological ambiguity or relegated to the role of synaptic modulators (Lerma, 2003). In the paper by Lindstrom et al. (2014) published in this issue of The Journal of Physiology, not only is the kainate receptor shown to be central to synaptic communication, but subtypes of the receptor are localized to specific cell types in order to convey unique signals across the synapse. Nature demonstrates that receptor subtypes really matter and that neurons segregate proteins to multiplex information across labelled lines using binding and kinetics, not just hard wiring. The first synapse in retina is perhaps the ideal model for exploring the implications of receptor diversity. Photoreceptors release glutamate that is detected by three groups of cells. One, the On bipolar cell, uses the mGluR6 coupled with TRPM1 to invert the sign of the synapse – transforming photoreceptor excitation in the dark into a depolarizing On light response (Morgans et al. 2010). The other two, the horizontal cells and Off bipolar cells are sign conserving but use pharmacologically discrete ionotropic glutamate receptors. Thus three cell types perform different functions that are enabled by their specific and distinct glutamate receptors (Miller & Slaughter, 1986). The photoreceptor synapse is playing the game of telephone, sending out one message that is heard differently by each cell type. In the mouse retina, where at least 10 types of cone bipolar cells have been identified (Wassle et al. 2009; Helmstaedter et al. 2013), each of the majority middle wavelength sensitive cones contacts all of the bipolar cell types with only one exception (Haverkamp et al. 2005). But the diversity of postsynaptic receptors clearly means this divergence is not redundant. Bipolar cells, the interlocutors between the cone light sensors and the ganglion cell spike encoders, first establish parallel visual pathways. The DeVries lab has redefined this synapse. In a ground-breaking study, he found that the Off bipolar cells can be further subdivided and that each subtype expresses a different ionotropic receptor (DeVries, 2000). There are AMPA-expressing Off bipolar cells (termed cb2) that press their receptors very close to the cone release sites while other, kainate-expressing Off bipolar cells (termed cb1/cb3), are positioned at the base of the synaptic terminal far from release sites and consequently sense lower and more prolonged levels of glutamate (DeVries et al. 2006). But there are differences in the glutamate responses of the kainate receptor-expressing neurons that the current paper disentangles. The cb1 synapses are relatively simple, dominated by GluK1 receptors. The cb3 synapses appear more complex, probably GluK1/GluK5-containing heteromers and perhaps also containing GluK3. The receptors may also be modulated by Neto1. Furthermore, about 20% of the cb3 current derives from AMPA receptors. There may be further layers of complexity at the Off bipolar synapse. The cb1 kainate receptors have two distinct rates of recovery from desensitization, indicating either two desensitized states of the protein or modulation of the receptor to alter it recovery rate. In mouse retina the AMPA (type 1), kainate (type 2/3a), and mixed AMPA–kainate receptor (type 3b/4) Off bipolar cell types can also be distinguished, but do not match the homologous bipolar cells in guinea-pig (Puller et al. 2013). Either the homology classification needs revision or there is an intriguing flexibility in receptor expression. Furthermore, AMPA and kainate receptors in the mixed mouse bipolar cell do not co-localize, suggesting they can be differentially activated. Each new level of receptor complexity has revealed additional coding mechanisms and this is only the first retinal synapse.

Mimicking the End Organ Architecture of Slowly Adapting Type I Afferents May Increase the Durability of Artificial Touch Sensors.

In effort to mimic the sensitivity and efficient information transfer of natural tactile afferents, recent work has combined force transducers and computational models of mechanosensitive afferents. Sensor durability, another feature important to sensor design, might similarly capitalize upon biological rules. In particular, gains in sensor durability might leverage insight from the compound end organ of the slowly adapting type I afferent, especially its multiple sites of spike initiation that reset each other. This work develops models of compound spiking sensors using a computational network of transduction functions and leaky integrate and fire models (together a spike encoder, the software element of a compound spiking sensor), informed by the output of an existing force transducer (hardware sensing elements of a compound spiking sensor). Individual force transducer failures are simulated with and without resetting between spike encoders to test the importance of both resetting and configuration on system durability. The results indicate that the resetting of adjacent spike encoders, upon the firing of a spike by any one, is an essential mechanism to maintain a stable overall response in the midst of transducer failure. Furthermore, results suggest that when resetting is enabled, the durability of a compound sensor is maximized when individual transducers are paired with spike encoders and multiple, paired units are employed. To explore these ideas more fully, use cases examine the design of a compound sensor to either reach a target lifetime with a set probability or determine how often to schedule maintenance to control the probability of failure.

Discharge regularity in the turtle posterior crista: comparisons between experiment and theory

Intra-axonal recordings were made from bouton fibers near their termination in the turtle posterior crista. Spike discharge, miniature excitatory postsynaptic potentials (mEPSPs), and afterhyperpolarizations (AHPs) were monitored during resting activity in both regularly and irregularly discharging units. Quantal size (qsize) and quantal rate (qrate) were estimated by shot-noise theory. Theoretically, the ratio, σV/(dμV/dt), between synaptic noise (σV) and the slope of the mean voltage trajectory (dμV/dt) near threshold crossing should determine discharge regularity. AHPs are deeper and more prolonged in regular units; as a result, dμV/dt is larger, the more regular the discharge. The qsize is larger and qrate smaller in irregular units; these oppositely directed trends lead to little variation in σV with discharge regularity. Of the two variables, dμV/dt is much more influential than the nearly constant σV in determining regularity. Sinusoidal canal-duct indentations at 0.3 Hz led to modulations in spike discharge and synaptic voltage. Gain, the ratio between the amplitudes of the two modulations, and phase leads re indentation of both modulations are larger in irregular units. Gain variations parallel the sensitivity of the postsynaptic spike encoder, the set of conductances that converts synaptic input into spike discharge. Phase variations reflect both synaptic inputs to the encoder and postsynaptic processes. Experimental data were interpreted using a stochastic integrate-and-fire model. Advantages of an irregular discharge include an enhanced encoder gain and the prevention of nonlinear phase locking. Regular and irregular units are more efficient, respectively, in the encoding of low- and high-frequency head rotations, respectively.

Upregulation of transmitter release probability improves a conversion of synaptic analogue signals into neuronal digital spikes

Action potentials at the neurons and graded signals at the synapses are primary codes in the brain. In terms of their functional interaction, the studies were focused on the influence of presynaptic spike patterns on synaptic activities. How the synapse dynamics quantitatively regulates the encoding of postsynaptic digital spikes remains unclear. We investigated this question at unitary glutamatergic synapses on cortical GABAergic neurons, especially the quantitative influences of release probability on synapse dynamics and neuronal encoding. Glutamate release probability and synaptic strength are proportionally upregulated by presynaptic sequential spikes. The upregulation of release probability and the efficiency of probability-driven synaptic facilitation are strengthened by elevating presynaptic spike frequency and Ca2+. The upregulation of release probability improves spike capacity and timing precision at postsynaptic neuron. These results suggest that the upregulation of presynaptic glutamate release facilitates a conversion of synaptic analogue signals into digital spikes in postsynaptic neurons, i.e., a functional compatibility between presynaptic and postsynaptic partners.

Erratum: Rutherford et al., Spike Encoding of Neurotransmitter Release Timing by Spiral Ganglion Neurons of the Cochlea

Erratum: Rutherford et al., Spike Encoding of Neurotransmitter Release Timing by Spiral Ganglion Neurons of the Cochlea

A small fraction of strongly cooperative sodium channels boosts neuronal encoding of high frequencies.

Generation of action potentials (APs) is a crucial step in neuronal information processing. Existing biophysical models for AP generation almost universally assume that individual voltage-gated sodium channels operate statistically independently, and their avalanche-like opening that underlies AP generation is coordinated only through the transmembrane potential. However, biological ion channels of various types can exhibit strongly cooperative gating when clustered. Cooperative gating of sodium channels has been suggested to explain rapid onset dynamics and large threshold variability of APs in cortical neurons. It remains however unknown whether these characteristic properties of cortical APs can be reproduced if only a fraction of channels express cooperativity, and whether the presence of cooperative channels has an impact on encoding properties of neuronal populations. To address these questions we have constructed a conductance-based neuron model in which we continuously varied the size of a fraction of sodium channels expressing cooperativity and the strength of coupling between cooperative channels . We show that starting at a critical value of the coupling strength , the activation curve of sodium channels develops a discontinuity at which opening of all coupled channels becomes an all-or-none event, leading to very rapid AP onsets. Models with a small fraction, , of strongly cooperative channels generate APs with the most rapid onset dynamics. In this regime APs are triggered by simultaneous opening of the cooperative channel fraction and exhibit a pronounced biphasic waveform often observed in cortical neurons. We further show that presence of a small fraction of cooperative Na+ channels significantly improves the ability of neuronal populations to phase-lock their firing to high frequency input fluctuation. We conclude that presence of a small fraction of strongly coupled sodium channels can explain characteristic features of cortical APs and has a functional impact of enhancing the spike encoding of rapidly varying signals.

Spike Encoding of Neurotransmitter Release Timing by Spiral Ganglion Neurons of the Cochlea

Mammalian cochlear spiral ganglion neurons (SGNs) encode sound with microsecond precision. Spike triggering relies upon input from a single ribbon-type active zone of a presynaptic inner hair cell (IHC). Using patch-clamp recordings of rat SGN postsynaptic boutons innervating the modiolar face of IHCs from the cochlear apex, at room temperature, we studied how spike generation contributes to spike timing relative to synaptic input. SGNs were phasic, firing a single short-latency spike for sustained currents of sufficient onset slope. Almost every EPSP elicited a spike, but latency (300-1500 μs) varied with EPSP size and kinetics. When current-clamp stimuli approximated the mean physiological EPSC (≈300 pA), several times larger than threshold current (rheobase, ≈50 pA), spikes were triggered rapidly (latency, ≈500 μs) and precisely (SD, <50 μs). This demonstrated the significance of strong synaptic input. However, increasing EPSC size beyond the physiological mean resulted in less-potent reduction of latency and jitter. Differences in EPSC charge and SGN baseline potential influenced spike timing less as EPSC onset slope and peak amplitude increased. Moreover, the effect of baseline potential on relative threshold was small due to compensatory shift of absolute threshold potential. Experimental first-spike latencies in response to a broad range of stimuli were predicted by a two-compartment exponential integrate-and-fire model, with latency prediction error of <100 μs. In conclusion, the close anatomical coupling between a strong synapse and spike generator along with the phasic firing property lock SGN spikes to IHC exocytosis timing to generate the auditory temporal code with high fidelity.

On the Relation Between Encoding and Decoding of Neuronal Spikes

Neural coding is a field of study that concerns how sensory information is represented in the brain by networks of neurons. The link between external stimulus and neural response can be studied from two parallel points of view. The first, neural encoding, refers to the mapping from stimulus to response. It focuses primarily on understanding how neurons respond to a wide variety of stimuli and constructing models that accurately describe the stimulus-response relationship. Neural decoding refers to the reverse mapping, from response to stimulus, where the challenge is to reconstruct a stimulus from the spikes it evokes. Since neuronal response is stochastic, a one-to-one mapping of stimuli into neural responses does not exist, causing a mismatch between the two viewpoints of neural coding. Here we use these two perspectives to investigate the question of what rate coding is, in the simple setting of a single stationary stimulus parameter and a single stationary spike train represented by a renewal process. We show that when rate codes are defined in terms of encoding, that is, the stimulus parameter is mapped onto the mean firing rate, the rate decoder given by spike counts or the sample mean does not always efficiently decode the rate codes, but it can improve efficiency in reading certain rate codes when correlations within a spike train are taken into account.

Quantal Glutamate Release Is Essential for Reliable Neuronal Encodings in Cerebral Networks

BackgroundThe neurons and synapses work coordinately to program the brain codes of controlling cognition and behaviors. Spike patterns at the presynaptic neurons regulate synaptic transmission. The quantitative regulations of synapse dynamics in spike encoding at the postsynaptic neurons remain unclear.Methodology/Principal FindingsWith dual whole-cell recordings at synapse-paired cells in mouse cortical slices, we have investigated the regulation of synapse dynamics to neuronal spike encoding at cerebral circuits assembled by pyramidal neurons and GABAergic ones. Our studies at unitary synapses show that postsynaptic responses are constant over time, such as glutamate receptor-channel currents at GABAergic neurons and glutamate transport currents at astrocytes, indicating quantal glutamate release. In terms of its physiological impact, our results demonstrate that the signals integrated from quantal glutamatergic synapses drive spike encoding at GABAergic neurons reliably, which in turn precisely set spike encoding at pyramidal neurons through feedback inhibition.Conclusion/SignificanceOur studies provide the evidences for the quantal glutamate release to drive the spike encodings precisely in cortical circuits, which may be essential for programming the reliable codes in the brain to manage well-organized behaviors.

On the relation between encoding and decoding of neuronal spikes

Sensory information is represented in neuronal responses. Determining which code is used by the neurons is important for understanding how the brain processes the information [1]. Coding schemes used by neurons can be divided approximately into two categories. In rate coding, information about the stimulus depends solely on the firing rate, which is the average number of spikes per unit time. In temporal coding, on the other hand, there is significant correlation between the stimulus and any moments in the spike pattern having higher order than the mean [2]. While neural codes are characterized in terms of these encoding schemes, i.e., how the neurons encode the stimulus into the features of spike responses, experimentalists can access the neural codes only through decoding. From the decoding viewpoint, rate coding is operationally defined by counting the number of spikes over a period of time, without taking into account any correlation structure among spikes. Any scheme based on such an operation is equivalent to decoding under the Poisson assumption, because the number of spikes over a period of time, or the sample mean, is a sufficient statistic of the rate parameter of a Poisson process. Similarly, temporal coding can be defined by decoding the stimulus using a statistical model with a correlation structure between spikes (such as the m-IMI model, introduced below). If such a decoder improves on the performance of the rate decoder, it indicates that significant information about the stimulus is carried on the temporal aspect of spike trains [3]. We introduce a simple statistical model that has a correlation structure, taking the intensity function of a point process to be a product of two factors: (1) where s*(t) represents the last spike time preceding t. The statistical model with the intensity function (1) has been called the multiplicative inhomogeneous Markov interval (m-IMI) model [4]. ϕ(t) is the free firing rate, which depends only on the stimulus, and g(t- s*(t)) is the recovery function, which describes the dependency of the last spike time preceding t and hence allows the m-IMI model to have a correlation structure between spikes. Note that (1) becomes the intensity function of a Poisson process if the recovery function is constant in time. It has been reported that the m-IMI model enhances decoding performance in real data analysis [3], which encourages use of the m-IMI model to test temporal codes. Although neural codes can be defined in terms of either encoding or decoding, the resulting codes are generally different from one another. Here, we investigate the relation between the two viewpoints of neural coding in terms of rate and temporal coding schemes. Specifically, we investigate the extent to which decoders of each scheme decode rate and temporal codes that are defined in terms of encoding. Our main claim is that temporal decoding does not necessarily mean decoding a temporal code that the rate decoder fails to read, but also decoding certain rate codes with greater efficiency than the rate decoder.

Acupuncture to point Baihui prevents ischemia-induced functional impairment of cortical GABAergic neurons

Ischemia impairs brain function and networks, in which the vulnerability of GABAergic neurons causes neural excitotoxicity and nerve cell death. Acupuncture presumably improves the outcome of stroke patients; however, cellular mechanisms underlying this improvement remain to be elusive. We have investigated whether electrical stimuli to acupoint Baihui prevent ischemia- induced impairment of cortical GABAergic neurons. After acupuncture to a Baihui-point of mice for a week, we examined the responses of cortical GABAergic neurons to ischemia by whole-cell recording. Compared with the data from a group of ischemia only, the acupuncture prevents the impairments of spike encoding and synaptic transmission at GABAergic neurons from ischemia. This prevention is associated with the resistance of these cells to ischemia-induced changes in spike threshold potentials and refractory periods Therefore, acupuncture to Baihui-point improves ischemic stroke via preventing the impairment of cortical GABAergic neurons.

Stimulus ensemble and cortical layer determine V1 spatial receptive fields

The concept of receptive field is a linear, feed-forward view of visual signal processing. Frequently used models of V1 neurons, like the dynamic Linear filter--static nonlinearity--Poisson [corrected] spike encoder model, predict that receptive fields measured with different stimulus ensembles should be similar. Here, we tested this concept by comparing spatiotemporal maps of V1 neurons derived from two very different, but commonly used, stimulus ensembles: sparse noise and Hartley subspace stimuli. We found maps from the two methods agreed for neurons in input layer 4C but were very different for neurons in superficial layers of V1. Many layer 2/3 cells have receptive fields with multiple elongated subregions when mapped with Hartley stimuli, but their spatial maps collapse to only a single, less-elongated subregion when mapped with sparse noise. Moreover, for upper layer V1 neurons, the preferred orientation for Hartley maps is much closer to the preferred orientation measured with drifting gratings than is the orientation preference of sparse-noise maps. These results challenge the concept of a stimulus-invariant receptive field and imply that intracortical interactions shape fundamental properties of layer 2/3 neurons.

Real-time neuronal homeostasis by coordinating VGSC intrinsic properties

Homeostasis of internal environment and cellular metabolism ensures cells’ functions to be stable in living organisms. Cellular homeostasis is believed to be maintained via feedback or feedforward manners. We report a novel mechanism that maintains neuronal homeostasis through coordinating the intrinsic properties of single molecules concurrently. Spike encoding and sodium channel dynamics at cortical neurons were studied by patch-clamp recording. Voltage-gated sodium channels set refractory period and threshold potential toward different directions to stabilize the energetic barrier for firing sequential action potentials. This neuronal homeostasis is not affected by intracellular Ca 2+ signals and membrane potentials. Real-time homeostasis maintains precise and reliable neuronal encoding without any destabilization.

An artificial chaotic spiking neuron inspired by spiral ganglion cell: Paralleled spike encoding, theoretical analysis, and electronic circuit implementation

A novel chaotic spiking neuron is presented and its nonlinear dynamics and encoding functions are analyzed. A set of paralleled N neurons accepts a common analog input and outputs a set of N chaotic spike-trains. Three theorems which guarantee that the neurons can encode the analog input into a summation of the N chaotic spike-trains are derived: (1) a spike histogram of the summed spike-train can mimic waveforms of various inputs, (2) the spike-trains do not synchronize to each other and thus the summed spike-train can have N times higher encoding resolution than each single spike-train, and (3) firing rates of the neurons can be adjusted by internal parameters. The theorems are proven by using nonlinear iterative maps and are confirmed by numerical simulations as well. Electronic circuit implementation methods of the paralleled neurons are also presented and typical paralleled encoding functions are confirmed by both experimental measurements and SPICE simulations.