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Background: To investigate the behavior of spiking neural networks (SNNs), the sensitivity of input perturbation serves as an effective metric for assessing the influence on the network output. However, existing methods fall short in evaluating the sensitivity of SNNs featuring biologically plausible leaky integrate-and-fire (LIF) neurons due to the intricate neuronal dynamics during the feedforward process. Methods: This paper first defines the sensitivity of a temporal-coded spiking neuron (SN) as the deviation between the perturbed and unperturbed output under a given input perturbation with respect to overall inputs. Then, the sensitivity algorithm of an entire SNN is derived iteratively from the sensitivity of each individual neuron. Instead of using the actual firing time, the desired firing time is employed to derive a more precise analytical expression of the sensitivity. Moreover, the expectation of the membrane potential difference is utilized to quantify the magnitude of the input deviation. Results/Conclusions: The theoretical results achieved with the proposed algorithm are in reasonable agreement with the simulation results obtained with extensive input data. The sensitivity also varies monotonically with changes in other parameters, except for the number of time steps, providing valuable insights for choosing appropriate values to construct the network. Nevertheless, the sensitivity exhibits a piecewise decreasing tendency with respect to the number of time steps, with the length and starting point of each piece contingent upon the specific parameter values of the neuron. [ABSTRACT FROM AUTHOR]

The retrosplenial cortex (RSC) is a key component of the brain's memory systems, with anatomical connections to the hippocampus, anterior thalamus, and entorhinal cortex. This circuit has been implicated in episodic memory and many of these structures have been shown to encode temporal information, which is critical for episodic memory. For example, hippocampal time cells reliably fire during specific segments of time during a delay period. Although RSC lesions are known to disrupt temporal memory, time cells have not been observed there. In this study, we reanalyzed archival RSC neuronal firing data during the intertrial delay period from two previous experiments involving different behavioral tasks, a blocked alternation task and a cued T‐maze task. For the blocked alternation task, rats were required to approach the east or west arm of a plus maze for reward during different blocks of trials. Because the reward locations were not cued, the rat had to remember the goal location for each trial. In the cued T‐maze task, the reward location was explicitly cued with a light and the rats simply had to approach the light for reward, so there was no requirement to hold a memory during the intertrial delay. Time cells were prevalent in the blocked alternation task, and most time cells clearly differentiated the east and west trials. We also found that RSC neurons could exhibit off‐response time fields, periods of reliably inhibited firing. Time cells were also observed in the cued T‐maze, but they were less prevalent and they did not differentiate left and right trials as well as in the blocked alternation task, suggesting that RSC time cells are sensitive to the memory demands of the task. These results suggest that temporal coding is a prominent feature of RSC firing patterns, consistent with an RSC role in episodic memory. [ABSTRACT FROM AUTHOR]

Background: To investigate the behavior of spiking neural networks (SNNs), the sensitivity of input perturbation serves as an effective metric for assessing the influence on the network output. However, existing methods fall short in evaluating the sensitivity of SNNs featuring biologically plausible leaky integrate-and-fire (LIF) neurons due to the intricate neuronal dynamics during the feedforward process. Methods: This paper first defines the sensitivity of a temporal-coded spiking neuron (SN) as the deviation between the perturbed and unperturbed output under a given input perturbation with respect to overall inputs. Then, the sensitivity algorithm of an entire SNN is derived iteratively from the sensitivity of each individual neuron. Instead of using the actual firing time, the desired firing time is employed to derive a more precise analytical expression of the sensitivity. Moreover, the expectation of the membrane potential difference is utilized to quantify the magnitude of the input deviation. Results/Conclusions: The theoretical results achieved with the proposed algorithm are in reasonable agreement with the simulation results obtained with extensive input data. The sensitivity also varies monotonically with changes in other parameters, except for the number of time steps, providing valuable insights for choosing appropriate values to construct the network. Nevertheless, the sensitivity exhibits a piecewise decreasing tendency with respect to the number of time steps, with the length and starting point of each piece contingent upon the specific parameter values of the neuron.

Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neurons. In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding. Our focus is on two distinct types of skip connection architectures: (1) addition-based skip connections, and (2) concatenation-based skip connections. We find that addition-based skip connections introduce an additional delay in terms of spike timing. On the other hand, concatenation-based skip connections circumvent this delay but produce time gaps between after-convolution and skip connection paths, thereby restricting the effective mixing of information from these two paths. To mitigate these issues, we propose a novel approach involving a learnable delay for skip connections in the concatenation-based skip connection architecture. This approach successfully bridges the time gap between the convolutional and skip branches, facilitating improved information mixing. We conduct experiments on public datasets including MNIST and Fashion-MNIST, illustrating the advantage of the skip connection in TTFS coding architectures. Additionally, we demonstrate the applicability of TTFS coding on beyond image recognition tasks and extend it to scientific machine-learning tasks, broadening the potential uses of SNNs [ABSTRACT FROM AUTHOR]

Simple Summary: The locus coeruleus–norepinephrine (LC-NE) system plays a critical role in regulating various brain functions through its diffuse projections in the brain. However, how the LC-NE system modulates temporal coding of sensory information along the sensory pathway remains poorly understood. To address this question, we recorded single-unit activity from a rat's somatosensory thalamus in response to whisker stimulation while stimulating the LC. By comparing the temporal structure of the thalamic responses to the same tactile stimulus with and without LC activation, we found that LC activation optimized the temporal coding of tactile signals in the thalamus by moving spikes to time points where they more accurately represent the tactile stimulus. The results shed light on the future development of neuromodulation technologies to enhance and/or restore brain functions. The brainstem noradrenergic nucleus, the locus coeruleus (LC), exerts heavy influences on sensory processing, perception, and cognition through its diffuse projections throughout the brain. Previous studies have demonstrated that LC activation modulates the response and feature selectivity of thalamic relay neurons. However, the extent to which LC modulates the temporal coding of sensory information in the thalamus remains mostly unknown. Here, we found that LC stimulation significantly altered the temporal structure of the responses of the thalamic relay neurons to repeated whisker stimulation. A substantial portion of events (i.e., time points where the stimulus reliably evoked spikes as evidenced by dramatic elevations in the firing rate of the spike density function) were removed during LC stimulation, but many new events emerged. Interestingly, spikes within the emerged events have a higher feature selectivity, and therefore transmit more information about a tactile stimulus, than spikes within the removed events. This suggests that LC stimulation optimized the temporal coding of tactile information to improve information transmission. We further reconstructed the original whisker stimulus from a population of thalamic relay neurons' responses and corresponding feature selectivity. As expected, we found that reconstruction from thalamic responses was more accurate using spike trains of thalamic neurons recorded during LC stimulation than without LC stimulation, functionally confirming LC optimization of the thalamic temporal code. Together, our results demonstrated that activation of the LC-NE system optimizes temporal coding of sensory stimulus in the thalamus, presumably allowing for more accurate decoding of the stimulus in the downstream brain structures. [ABSTRACT FROM AUTHOR]

Introduction: Understanding speech in a noisy environment, as opposed to speech in quiet, becomes increasingly more difficult with increasing age. Using the quiet-aged gerbil, we studied the effects of aging on speech-in-noise processing. Specifically, behavioral vowel discrimination and the encoding of these vowels by single auditory-nerve fibers were compared, to elucidate some of the underlying mechanisms of age-related speech-in-noise perception deficits. Methods: Young-adult and quiet-aged Mongolian gerbils, of either sex, were trained to discriminate a deviant naturally-spoken vowel in a sequence of vowel standards against a speech-like background noise. In addition, we recorded responses from single auditory-nerve fibers of young-adult and quiet-aged gerbils while presenting the same speech stimuli. Results: Behavioral vowel discrimination was not significantly affected by aging. For both young-adult and quiet-aged gerbils, the behavioral discrimination between /eː/ and /iː/ was more difficult to make than /eː/ vs. /aː/ or /iː/ vs. /aː/, as evidenced by longer response times and lower d’ values. In young-adults, spike timing-based vowel discrimination agreed with the behavioral vowel discrimination, while in quiet-aged gerbils it did not. Paradoxically, discrimination between vowels based on temporal responses was enhanced in aged gerbils for all vowel comparisons. Representation schemes, based on the spectrum of the inter-spike interval histogram, revealed stronger encoding of both the fundamental and the lower formant frequencies in fibers of quiet-aged gerbils, but no qualitative changes in vowel encoding. Elevated thresholds in combination with a fixed stimulus level, i.e., lower sensation levels of the stimuli for old individuals, can explain the enhanced temporal coding of the vowels in noise. Discussion: These results suggest that the altered auditory-nerve discrimination metrics in old gerbils may mask age-related deterioration in the central (auditory) system to the extent that behavioral vowel discrimination matches that of the young adults. [ABSTRACT FROM AUTHOR]

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an exact mapping from a network with Rectified Linear Units (ReLUs) to an SNN that fires exactly one spike per neuron. For our constructive proof, we assume that an arbitrary multi-layer ReLU network with or without convolutional layers, batch normalization and max pooling layers was trained to high performance on some training set. Furthermore, we assume that we have access to a representative example of input data used during training and to the exact parameters (weights and biases) of the trained ReLU network. The mapping from deep ReLU networks to SNNs causes zero percent drop in accuracy on CIFAR10, CIFAR100 and the ImageNet-like data sets Places365 and PASS. More generally our work shows that an arbitrary deep ReLU network can be replaced by an energy-efficient single-spike neural network without any loss of performance. [ABSTRACT FROM AUTHOR]

In neuromorphic computing, the coding method of spiking neurons serves as the foundation and is crucial for various aspects of network operation. Existing mainstream coding methods, such as rate coding and temporal coding, have different focuses, and each has its own advantages and limitations. This paper proposes a novel coding scheme called activeness coding that integrates the strengths of both rate and temporal coding methods. It encompasses precise timing information of the most recent neuronal spike as well as the historical firing rate information. The results of basic characteristic tests demonstrate that this encoding method accurately expresses input information and exhibits robustness. Furthermore, an unsupervised learning method based on activeness-coding triplet spike-timing dependent plasticity (STDP) is introduced, with the MNIST classification task used as an example to assess the performance of this encoding method in solving cognitive tasks. Test results show an improvement in accuracy of approximately 4.5%. Additionally, activeness coding also exhibits potential advantages in terms of resource conservation. Overall, activeness offers a promising approach for spiking neural network encoding with implications for various applications in the field of neural computation. [ABSTRACT FROM AUTHOR]

Sensory restoration by optogenetic neurostimulation provides a promising future alternative to current electrical stimulation approaches. So far, channelrhodopsins (ChRs) typically contain a C-terminal fluorescent protein (FP) tag for visualization that potentially poses an additional risk for clinical translation. Previous work indicated a reduction of optogenetic stimulation efficacy upon FP removal. Here, we further optimized the fast-gating, red-light-activated ChR f-Chrimson to achieve efficient optogenetic stimulation in the absence of the C-terminal FP. Upon FP removal, we observed a massive amplitude reduction of photocurrents in transfected cells in vitro and of optogenetically evoked activity of the adeno-associated virus (AAV) vector-transduced auditory nerve in mice in vivo. Increasing the AAV vector dose restored optogenetically evoked auditory nerve activity but was confounded by neural loss. Of various C-terminal modifications, we found the replacement of the FP by the Kir2.1 trafficking sequence (TSKir2.1) to best restore both photocurrents and optogenetically evoked auditory nerve activity with only mild neural loss few months after dosing. In conclusion, we consider f-Chrimson-TSKir2.1 to be a promising candidate for clinical translation of optogenetic neurostimulation such as by future optical cochlear implants.

IntroductionUnderstanding speech in a noisy environment, as opposed to speech in quiet, becomes increasingly more difficult with increasing age. Using the quiet-aged gerbil, we studied the effects of aging on speech-in-noise processing. Specifically, behavioral vowel discrimination and the encoding of these vowels by single auditory-nerve fibers were compared, to elucidate some of the underlying mechanisms of age-related speech-in-noise perception deficits.MethodsYoung-adult and quiet-aged Mongolian gerbils, of either sex, were trained to discriminate a deviant naturally-spoken vowel in a sequence of vowel standards against a speech-like background noise. In addition, we recorded responses from single auditory-nerve fibers of young-adult and quiet-aged gerbils while presenting the same speech stimuli.ResultsBehavioral vowel discrimination was not significantly affected by aging. For both young-adult and quiet-aged gerbils, the behavioral discrimination between /eː/ and /iː/ was more difficult to make than /eː/ vs. /aː/ or /iː/ vs. /aː/, as evidenced by longer response times and lower d’ values. In young-adults, spike timing-based vowel discrimination agreed with the behavioral vowel discrimination, while in quiet-aged gerbils it did not. Paradoxically, discrimination between vowels based on temporal responses was enhanced in aged gerbils for all vowel comparisons. Representation schemes, based on the spectrum of the inter-spike interval histogram, revealed stronger encoding of both the fundamental and the lower formant frequencies in fibers of quiet-aged gerbils, but no qualitative changes in vowel encoding. Elevated thresholds in combination with a fixed stimulus level, i.e., lower sensation levels of the stimuli for old individuals, can explain the enhanced temporal coding of the vowels in noise.DiscussionThese results suggest that the altered auditory-nerve discrimination metrics in old gerbils may mask age-related deterioration in the central (auditory) system to the extent that behavioral vowel discrimination matches that of the young adults.

A spiking neural network (SNN) is a bottom-up tool used to describe information processing in brain microcircuits. It is becoming a crucial neuromorphic computational model. Spike-timing-dependent plasticity (STDP) is an unsupervised brain-like learning rule implemented in many SNNs and neuromorphic chips. However, a significant performance gap exists between ideal model simulation and neuromorphic implementation. The performance of STDP learning in neuromorphic chips deteriorates because the resolution of synaptic efficacy in such chips is generally restricted to 6 bits or less, whereas simulations employ the entire 64-bit floating-point precision available on digital computers. Previously, we introduced a bio-inspired learning rule named adaptive STDP and demonstrated via numerical simulation that adaptive STDP (using only 4-bit fixedpoint synaptic efficacy) performs similarly to STDP learning (using 64-bit floatingpoint precision) in a noisy spike pattern detection model. Herein, we present the experimental results demonstrating the performance of adaptive STDP learning. To the best of our knowledge, this is the first study that demonstrates unsupervised noisy spatiotemporal spike pattern detection to perform well and maintain the simulation performance on a mixed-signal CMOS neuromorphic chip with lowresolution synaptic efficacy. The chip was designed in Taiwan Semiconductor Manufacturing Company (TSMC) 250 nm CMOS technology node and comprises a soma circuit and 256 synapse circuits along with their learning circuitry. [ABSTRACT FROM AUTHOR]

In macaques, the middle lateral auditory cortex (ML) is a belt region adjacent to the primary auditory cortex (A1) and believed to be at a hierarchically higher level. Although ML single-unit responses have been studied for several auditory stimuli, the ability of ML cells to encode amplitude modulation (AM)-an ability that has been widely studied in A1-has not yet been characterized. Here, we compared the responses of A1 and ML neurons to amplitude-modulated (AM) noise in awake macaques. Although several of the basic properties of A1 and ML responses to AM noise were similar, we found several key differences. ML neurons were less likely to phase lock, did not phase lock as strongly, and were more likely to respond in a nonsynchronized fashion than A1 cells, consistent with a temporal-to-rate transformation as information ascends the auditory hierarchy. ML neurons tended to have lower temporally (phase-locking) based best modulation frequencies than A1 neurons. Neurons that decreased their firing rate in response to AM noise relative to their firing rate in response to unmodulated noise became more common at the level of ML than they were in A1. In both A1 and ML, we found a prevalent class of neurons that usually have enhanced rate responses relative to responses to the unmodulated noise at lower modulation frequencies and suppressed rate responses relative to responses to the unmodulated noise at middle modulation frequencies.NEW & NOTEWORTHY ML neurons synchronized less than A1 neurons, consistent with a hierarchical temporal-to-rate transformation. Both A1 and ML had a class of modulation transfer functions previously unreported in the cortex with a low-modulation-frequency (MF) peak, a middle-MF trough, and responses similar to unmodulated noise responses at high MFs. The results support a hierarchical shift toward a two-pool opponent code, where subtraction of neural activity between two populations of oppositely tuned neurons encodes AM.

Tinnitus is a phantom auditory sensation in the absence of external sounds, while hyperacusis is an atypical sensitivity to external sounds that leads them to be perceived as abnormally loud or even painful. Both conditions may reflect the brain's over-compensation for reduced input from the ear. The present work differentiates between two compensation models: The additive central noise compensates for hearing loss and is likely to generate tinnitus, whereas the multiplicative central gain compensates for hidden hearing loss and is likely to generate hyperacusis. Importantly, both models predict increased variance in central representations of sounds, especially a nonlinear increase in variance by the central gain. The increased central variance limits the amount of central compensation and reduces temporal synchrony, which can explain the insufficient central gain reported in the literature. Future studies need to collect trial-by-trial firing variance data so that the present variance-based model can be falsified.

Classification and recognition tasks performed on photonic hardware-based neural networks often require at least one offline computational step, such as in the increasingly popular reservoir computing paradigm. Removing this offline step can significantly improve the response time and energy efficiency of such systems. We present numerical simulations of different algorithms that utilize ultrafast photonic spiking neurons as receptive fields to allow for image recognition without an offline computing step. In particular, we discuss the merits of event, spike-time and rank-order based algorithms adapted to this system. These techniques have the potential to significantly improve the efficiency and effectiveness of optical classification systems, minimizing the number of spiking nodes required for a given task and leveraging the parallelism offered by photonic hardware.

The brainstem noradrenergic nucleus, the locus coeruleus (LC), exerts heavy influences on sensory processing, perception, and cognition through its diffuse projections throughout the brain. Previous studies have demonstrated that LC activation modulates the response and feature selectivity of thalamic relay neurons. However, the extent to which LC modulates the temporal coding of sensory information in the thalamus remains mostly unknown. Here, we found that LC stimulation significantly altered the temporal structure of the responses of the thalamic relay neurons to repeated whisker stimulation. A substantial portion of events (i.e., time points where the stimulus reliably evoked spikes as evidenced by dramatic elevations in the firing rate of the spike density function) were removed during LC stimulation, but many new events emerged. Interestingly, spikes within the emerged events have a higher feature selectivity, and therefore transmit more information about a tactile stimulus, than spikes within the removed events. This suggests that LC stimulation optimized the temporal coding of tactile information to improve information transmission. We further reconstructed the original whisker stimulus from a population of thalamic relay neurons’ responses and corresponding feature selectivity. As expected, we found that reconstruction from thalamic responses was more accurate using spike trains of thalamic neurons recorded during LC stimulation than without LC stimulation, functionally confirming LC optimization of the thalamic temporal code. Together, our results demonstrated that activation of the LC-NE system optimizes temporal coding of sensory stimulus in the thalamus, presumably allowing for more accurate decoding of the stimulus in the downstream brain structures.

Spiking neural networks (SNNs) are mimicking computationally powerful biologically inspired models in which neurons communicate through sequences of spikes, regarded here as sparse binary sequences of zeros and ones. In neuroscience it is conjectured that time encoding, where the information is carried by the temporal position of spikes, is playing a crucial role at least in some parts of the brain where estimation of the spiking rate with a large latency cannot take place. Motivated by the efficiency of temporal coding, compared with the widely used rate coding, the goal of this paper is to develop and train an energy-efficient time-coded deep spiking neural network system. To ensure that the similarity among input stimuli is translated into a correlation of the spike sequences, we introduce correlative temporal encoding and extended correlative temporal encoding techniques to map analog input information into input spike patterns. Importantly, we propose an implementation where all multiplications in the system are replaced with at most a few additions. As a more efficient alternative to both rate-coded SNNs and artificial neural networks, such system represents a preferable solution for the implementation of neuromorphic hardware. We consider data classification tasks where input spike patterns are presented to a feed-forward architecture with leaky-integrate-and-fire neurons. The SNN is trained by backpropagation through time with the objective to match sequences of output spikes with those of specifically designed target spike patterns, each corresponding to exactly one class. During inference the target spike pattern with the smallest van Rossum distance from the output spike pattern determines the class. Extensive simulations indicate that the proposed system achieves a classification accuracy at par with that of state-of-the-art machine learning models. [ABSTRACT FROM AUTHOR]

Hippocampal theta frequency is a somewhat neglected topic relative to theta power, phase, coherence, and cross-frequency coupling. Accordingly, here we review and present new data on variation in hippocampal theta frequency, focusing on functional associations (temporal coding, anxiety reduction, learning, and memory). Taking the rodent hippocampal theta frequency to running-speed relationship as a model, we identify two doubly-dissociable frequency components: (a) the slope component of the theta frequency-to-stimulus-rate relationship ("theta slope"); and (b) its y-intercept frequency ("theta intercept"). We identify three tonic determinants of hippocampal theta frequency. (1) Hotter temperatures increase theta frequency, potentially consistent with time intervals being judged as shorter when hot. Initial evidence suggests this occurs via the "theta slope" component. (2) Anxiolytic drugs with widely-different post-synaptic and pre-synaptic primary targets share the effect of reducing the "theta intercept" component, supporting notions of a final common pathway in anxiety reduction involving the hippocampus. (3) Novelty reliably decreases, and familiarity increases, theta frequency, acting upon the "theta slope" component. The reliability of this latter finding, and the special status of novelty for learning, prompts us to propose a Novelty Elicits Slowing of Theta frequency (NEST) hypothesis, involving the following elements: (1) Theta frequency slowing in the hippocampal formation is a generalised response to novelty of different types and modalities; (2) Novelty-elicited theta slowing is a hippocampal-formation-wide adaptive response functioning to accommodate the additional need for learning entailed by novelty; (3) Lengthening the theta cycle enhances associativity; (4) Even part-cycle lengthening may boost associativity; and (5) Artificial theta stimulation aimed at enhancing learning should employ low-end theta frequencies. [ABSTRACT FROM AUTHOR]

Animal communication sounds exhibit complex temporal structure because of the amplitude fluctuations that comprise the sound envelope. In human speech, envelope modulations drive synchronized activity in auditory cortex (AC), which correlates strongly with comprehension (Giraud and Poeppel, 2012; Peelle and Davis, 2012; Haegens and Zion Golumbic, 2018). Studies of envelope coding in single neurons, performed in nonhuman animals, have focused on periodic amplitude modulation (AM) stimuli and use response metrics that are not easy to juxtapose with data from humans. In this study, we sought to bridge these fields. Specifically, we looked directly at the temporal relationship between stimulus envelope and spiking, and we assessed whether the apparent diversity across neurons’ AM responses contributes to the population representation of speech-like sound envelopes. We gathered responses from single neurons to vocoded speech stimuli and compared them to sinusoidal AM responses in auditory cortex (AC) of alert, freely moving Mongolian gerbils of both sexes. While AC neurons displayed heterogeneous tuning to AM rate, their temporal dynamics were stereotyped. Preferred response phases accumulated near the onsets of sinusoidal AM periods for slower rates (,8 Hz), and an over-representation of amplitude edges was apparent in population responses to both sinusoidal AM and vocoded speech envelopes. Crucially, this encoding bias imparted a decoding benefit: a classifier could discriminate vocoded speech stimuli using summed population activity, while higher frequency modulations required a more sophisticated decoder that tracked spiking responses from individual cells. Together, our results imply that the envelope structure relevant to parsing an acoustic stream could be read-out from a distributed, redundant population code. [ABSTRACT FROM AUTHOR]

Spiking Neural Networks (SNNs) hold great potential for mimicking the brain's efficient processing of information. Although biological evidence suggests that precise spike timing is crucial for effective information encoding, contemporary SNN research mainly concentrates on adjusting connection weights. In this work, we introduce Delay Learning based on Temporal Coding (DLTC), an innovative approach that integrates delay learning with a temporal coding strategy to optimize spike timing in SNNs. DLTC utilizes a learnable delay shift, which assigns varying levels of importance to different informational elements. This is complemented by an adjustable threshold that regulates firing times, allowing for earlier or later neuron activation as needed. We have tested DLTC's effectiveness in various contexts, including vision and auditory classification tasks, where it consistently outperformed traditional weight-only SNNs. The results indicate that DLTC achieves remarkable improvements in accuracy and computational efficiency, marking a step forward in advancing SNNs towards real-world applications. Our codes are accessible at https://github.com/sunpengfei1122/DLTC. [ABSTRACT FROM AUTHOR]

Hardware-based Spiking Neural Networks (SNNs) are regarded as promising candidates for the cognitive computing system due to its low power consumption and highly parallel operation. In this paper, we train the SNN in which the firing time carries information using temporal backpropagation. The temporally encoded SNN with 512 hidden neurons achieved an accuracy of 96.90% for the MNIST test set. Furthermore, the effect of the device variation on the accuracy in temporally encoded SNN is investigated and compared with that of the rate-encoded network. In a hardware configuration of our SNN, NOR-type analog memory having an asymmetric floating gate is used as a synaptic device. In addition, we propose a neuron circuit including a refractory period generator for temporally encoded SNN. The performance of the 2-layer neural network composed of synapses and proposed neurons is evaluated through circuit simulation using SPICE based on the BSIM3v3 model with $0.35~\mu \text{m}$ technology. The network with 128 hidden neurons achieved an accuracy of 94.9%, a 0.1% reduction compared to that of the system simulation of the MNIST dataset. Finally, each block’s latency and power consumption constituting the temporal network is analyzed and compared with those of the rate-encoded network depending on the total time step. Assuming that the network has 256 total time steps, the temporal network consumes 15.12 times less power than the rate-encoded network and makes decisions 5.68 times faster.

Hippocampal theta frequency is a somewhat neglected topic relative to theta power, phase, coherence, and cross-frequency coupling. Accordingly, here we review and present new data on variation in hippocampal theta frequency, focusing on functional associations (temporal coding, anxiety reduction, learning, and memory). Taking the rodent hippocampal theta frequency to running-speed relationship as a model, we identify two doubly-dissociable frequency components: (a) the slope component of the theta frequency-to-stimulus-rate relationship (“theta slope”); and (b) its y-intercept frequency (“theta intercept”). We identify three tonic determinants of hippocampal theta frequency. (1) Hotter temperatures increase theta frequency, potentially consistent with time intervals being judged as shorter when hot. Initial evidence suggests this occurs via the “theta slope” component. (2) Anxiolytic drugs with widely-different post-synaptic and pre-synaptic primary targets share the effect of reducing the “theta intercept” component, supporting notions of a final common pathway in anxiety reduction involving the hippocampus. (3) Novelty reliably decreases, and familiarity increases, theta frequency, acting upon the “theta slope” component. The reliability of this latter finding, and the special status of novelty for learning, prompts us to propose a Novelty Elicits Slowing of Theta frequency (NEST) hypothesis, involving the following elements: (1) Theta frequency slowing in the hippocampal formation is a generalised response to novelty of different types and modalities; (2) Novelty-elicited theta slowing is a hippocampal-formation-wide adaptive response functioning to accommodate the additional need for learning entailed by novelty; (3) Lengthening the theta cycle enhances associativity; (4) Even part-cycle lengthening may boost associativity; and (5) Artificial theta stimulation aimed at enhancing learning should employ low-end theta frequencies.

Neurons use two main schemes to encode information: rate coding (frequency of firing) and temporal coding (timing or pattern of firing). While the importance of rate coding is well established, it remains controversial whether temporal codes alone are sufficient for controlling behavior. Moreover, the molecular mechanisms underlying the generation of specific temporal codes are enigmatic. Here, we show in Drosophila clock neurons that distinct temporal spike patterns, dissociated from changes in firing rate, encode time-dependent arousal and regulate sleep. From a large-scale genetic screen, we identify the molecular pathways mediating the circadian-dependent changes in ionic flux and spike morphology that rhythmically modulate spike timing. Remarkably, the daytime spiking pattern alone is sufficient to drive plasticity in downstream arousal neurons, leading to increased firing of these cells. These findings demonstrate a causal role for temporal coding in behavior and define a form of synaptic plasticity triggered solely by temporal spike patterns.

Featured Application: Rapid decision-making on micro drones. Taking advantage of faster speed, less resource consumption and better biological interpretability of spiking neural networks, this paper developed a novel spiking neural network reinforcement learning method using actor-critic architecture and temporal coding. The simple improved leaky integrate-and-fire (LIF) model was used to describe the behavior of a spike neuron. Then the actor-critic network structure and the update formulas using temporally encoded information were provided. The current model was finally examined in the decision-making task, the gridworld task, the UAV flying through a window task and the avoiding a flying basketball task. In the 5 × 5 grid map, the value function learned was close to the ideal situation and the quickest way from one state to another was found. A UAV trained by this method was able to fly through the window quickly in simulation. An actual flight test of a UAV avoiding a flying basketball was conducted. With this model, the success rate of the test was 96% and the average decision time was 41.3 ms. The results show the effectiveness and accuracy of the temporal coded spiking neural network RL method. In conclusion, an attempt was made to provide insights into developing spiking neural network reinforcement learning methods for decision-making and autonomous control of unmanned systems. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) using time-to-first-spike (TTFS) codes, in which neurons fire at most once, are appealing for rapid and low power processing. In this theoretical paper, we focus on information coding and decoding in those networks, and introduce a new unifying mathematical framework that allows the comparison of various coding schemes. In an early proposal, called rank-order coding (ROC), neurons are maximally activated when inputs arrive in the order of their synaptic weights, thanks to a shunting inhibition mechanism that progressively desensitizes the neurons as spikes arrive. In another proposal, called NoM coding, only the first N spikes of M input neurons are propagated, and these "first spike patterns" can be readout by downstream neurons with homogeneous weights and no desensitization: as a result, the exact order between the first spikes does notmatter. This paper also introduces a third option--"Ranked-NoM" (R-NoM), which combines features from both ROC and NoM coding schemes: only the first N input spikes are propagated, but their order is readout by downstream neurons thanks to inhomogeneous weights and linear desensitization. The unifyingmathematical framework allows the three codes to be compared in terms of discriminability, which measures to what extent a neuron responds more strongly to its preferred input spike pattern than to random patterns. This discriminability turns out to be much higher for R-NoM than for the other codes, especially in the early phase of the responses. We also argue that R-NoM is much more hardware-friendly than the original ROC proposal, although NoM remains the easiest to implement in hardware because it only requires binary synapses. [ABSTRACT FROM AUTHOR]

Recent years witness an increasing demand for using spiking neural networks (SNNs) to implement artificial intelligent systems. There is a demand of combining SNNs with reinforcement learning architectures to find an effective training method. Recently, temporal coding method has been proposed to train spiking neural networks while preserving the asynchronous nature of spiking neurons to preserve the asynchronous nature of SNNs. We propose a training method that enables temporal coding method in RL tasks. To tackle the problemof high sparsity of spikes, we introduce a self-incremental variable to push each spiking neuron to fire, which makes SNNs fully differentiable. In addition, an encoding method is proposed to solve the problem of information loss of temporal-coded inputs. The experimental results show that the SNNs trained by our proposed method can achieve comparable performance of the state-of-the-art artificial neural networks in benchmark tasks of reinforcement learning. [ABSTRACT FROM AUTHOR]

Objective Hypertension is a condition in which the blood vessels have persistently raised pressure. The damage of the cochlea is due to the loss of sensitive hair cells in the inner ear or the damage to the eighth cranial nerve. When the cochlea is damaged, the functioning abilities such as coding, differentiation, and temporal processing abilities will be affected. Hence, there might be deficits in differential sensitivity in individuals with hypertension. The aim of this article was to study the effect of hypertension on differential sensitivity as there is limited literature in this area. Method Thirty participants were included in the study and classified into two groups: group I as individuals with hypertension and group II as individuals with normal blood pressure in the age range of 25 to 45 years. Psychophysical tests like frequency, intensity, and time discrimination tests were performed using the maximum likelihood procedure (MLP) toolbox, which implements a maximum likelihood procedure for threshold estimation in MATLAB. Results In all the three test conditions, the scores were significantly poorer in individuals with hypertension compared with individuals of the normal at all the frequencies such as 500 Hz, 1,000 Hz, 2,000 Hz, and 4,000 Hz. Conclusion This could be because of reduced frequency selectivity and poor temporal coding as well as due to difficulty responding to rapid change in the envelope of sound over time because of cochlear and neural damage in individuals with hypertension. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) using time-to-first-spike (TTFS) codes, in which neurons fire at most once, are appealing for rapid and low power processing. In this theoretical paper, we focus on information coding and decoding in those networks, and introduce a new unifying mathematical framework that allows the comparison of various coding schemes. In an early proposal, called rank-order coding (ROC), neurons are maximally activated when inputs arrive in the order of their synaptic weights, thanks to a shunting inhibition mechanism that progressively desensitizes the neurons as spikes arrive. In another proposal, called NoM coding, only the first N spikes of M input neurons are propagated, and these “first spike patterns” can be readout by downstream neurons with homogeneous weights and no desensitization: as a result, the exact order between the first spikes does not matter. This paper also introduces a third option—“Ranked-NoM” (R-NoM), which combines features from both ROC and NoM coding schemes: only the first N input spikes are propagated, but their order is readout by downstream neurons thanks to inhomogeneous weights and linear desensitization. The unifying mathematical framework allows the three codes to be compared in terms of discriminability, which measures to what extent a neuron responds more strongly to its preferred input spike pattern than to random patterns. This discriminability turns out to be much higher for R-NoM than for the other codes, especially in the early phase of the responses. We also argue that R-NoM is much more hardware-friendly than the original ROC proposal, although NoM remains the easiest to implement in hardware because it only requires binary synapses.

Abstract Optogenetic stimulation of spiral ganglion neurons (SGNs) in the ear provides a future alternative to electrical stimulation used in current cochlear implants. Here, we employed fast and very fast variants of the red‐light‐activated channelrhodopsin (ChR) Chrimson (f‐Chrimson and vf‐Chrimson) to study their utility for optogenetic stimulation of SGNs in mice. The light requirements were higher for vf‐Chrimson than for f‐Chrimson, even when optimizing membrane expression of vf‐Chrimson by adding potassium channel trafficking sequences. Optogenetic time and intensity coding by single putative SGNs were compared with coding of acoustic clicks. vf‐Chrimson enabled putative SGNs to fire at near‐physiological rates with good temporal precision up to 250 Hz of stimulation. The dynamic range of SGN spike rate coding upon optogenetic stimulation was narrower than for acoustic clicks but larger than reported for electrical stimulation. The dynamic range of spike timing, on the other hand, was more comparable for optogenetic and acoustic stimulation. In conclusion, f‐Chrimson and vf‐Chrimson are promising candidates for optogenetic stimulation of SGNs in auditory research and future cochlear implants.

cGMP is generated by the cGMP‐forming guanylyl cyclases (GCs), the intracellular nitric oxide (NO)‐sensitive (soluble) guanylyl cyclase (sGC) and transmembrane GC (e.g. GC‐A and GC‐B). In summarizing the particular role of cGMP signalling for hearing, we show that GC generally do not interfere significantly with basic hearing function but rather sustain a healthy state for proper temporal coding, fast discrimination and adjustments during injury. sGC is critical for the integrity of the first synapse in the ascending auditory pathway, the inner hair cell synapse. GC‐A promotes hair cell stability under stressful conditions such as acoustic trauma or ageing. GC‐B plays a role in the development of efferent feed‐back and gain control. Regarding the crucial role hearing has for language development, speech discrimination and cognitive brain functions, differential pharmaceutical targeting of GCs offers therapeutic promise for the restoration of hearing. LINKED ARTICLES: This article is part of a themed issue on cGMP Signalling in Cell Growth and Survival. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v179.11/issuetoc [ABSTRACT FROM AUTHOR]

In this paper, we present a novel early termination based training acceleration technique for temporal coding based spiking neural network (SNN) processor design. The proposed early termination scheme can efficiently identify the non-contributing training images during the training's feedforward process, and it skips the rest of the processes to save training energy and time. A metric to evaluate each input image's contribution to training has been developed, and it is compared with pre-determined threshold to decide whether to skip the rest of the training process. For the threshold selection, an adaptive threshold calculation method is presented to increase the computation skip ratio without sacrificing accuracy. Timestep splitting approach is also employed to allow more frequent early termination in split timesteps, thus leading to more computation savings. The proposed early termination and timestep splitting techniques achieve 51.21/42.31/93.53/30.36% reduction of synaptic operations and 86.06/64.63/90.82/49.14% reduction of feedforward timestep for the training process on MNIST/Fashion-MNIST/ETH-80/EMNIST-Letters dataset, respectively. The hardware implementation of the proposed SNN processor using 28 nm CMOS process shows that the SNN processor achieves the training energy saving of 61.76/31.88% and computation cycle reduction of 69.10/36.26% on MNIST/Fashion-MNIST dataset, respectively. [ABSTRACT FROM AUTHOR]

For efficient processing, spatiotemporal spike patterns representing similar input must be able to transform into a less similar output. A new computational model with physiologically plausible parameters shows how the neuronal process referred to as "pattern separation" can be very well achieved by single neurons if the temporal qualities of the output patterns are considered. Spike patterns generated by a varying number of neurons firing with fixed different frequencies within a gamma range are used as input. The temporal and spatial summation of dendritic input combined with theta-oscillating excitability in the output neuron by subthreshold membrane potential oscillations (SMOs) lead to high temporal separation by different delays of output spikes of similar input patterns. A Winner Takes All (WTA) mechanism with backward inhibition suffices to transform the spatial overlap of input patterns to much less temporal overlap of the output patterns. The conversion of spatial patterns input into an output with differently delayed spikes enables high separation effects. Incomplete random connectivity spreads the times up to the first spike across a spatially expanded ensemble of output neurons. With the expansion, random connectivity becomes the spatial distribution mechanism of temporal features. Additionally, a "synfire chain" circuit is proposed to reconvert temporal differences into spatial ones. [ABSTRACT FROM AUTHOR]

We recently proposed the S4NN algorithm, essentially an adaptation of backpropagation to multilayer spiking neural networks that use simple non-leaky integrate-and-fire neurons and a form of temporal coding known as time-to-first-spike coding. With this coding scheme, neurons fire at most once per stimulus, but the firing order carries information. Here, we introduce BS4NN, a modification of S4NN in which the synaptic weights are constrained to be binary (+ 1 or − 1), in order to decrease memory (ideally, one bit per synapse) and computation footprints. This was done using two sets of weights: firstly, real-valued weights, updated by gradient descent, and used in the backward pass of backpropagation, and secondly, their signs, used in the forward pass. Similar strategies have been used to train (non-spiking) binarized neural networks. The main difference is that BS4NN operates in the time domain: spikes are propagated sequentially, and different neurons may reach their threshold at different times, which increases computational power. We validated BS4NN on two popular benchmarks, MNIST and Fashion-MNIST, and obtained reasonable accuracies for this sort of network (97.0% and 87.3% respectively) with a negligible accuracy drop with respect to real-valued weights (0.4% and 0.7%, respectively). We also demonstrated that BS4NN outperforms a simple BNN with the same architectures on those two datasets (by 0.2% and 0.9% respectively), presumably because it leverages the temporal dimension. [ABSTRACT FROM AUTHOR]

In hardware-based spiking neural networks (SNNs), the conversion of analog input data into the arrival time of an input pulse is regarded as a good candidate for the encoding method due to its bio-plausibility and power-efficiency. In this work, we trained an SNN encoded by time to first spike (TTFS) and performed an inference process using the behavior of the fabricated TFT-type flash synaptic device. The exponentially decaying synaptic current model required in the inference process was implemented by reading devices in the subthreshold region using triangle pulses. In a high-level system simulation, the TTFS-SNN (two-layer MLP with 512 hidden neurons) reached a high accuracy of 97.94%. Compared to conventional rate-encoded SNNs, TTFS-SNN made 2.9 times faster judgment and consumed ~10 times less energy in the inference process. Additionally, to use the network in a more stable condition, we propose a method to operate it using a rectangle pulse in the saturation region of the synaptic device. The distortion caused by this approximation was minimized by shortening the pulse width. As a result, the modified inference system showed an accuracy of 97.36%, and the prediction time and energy consumption were reduced 3.97- and 83.04-times when compared to those of the rate-SNN. Finally, we analyzed the sensitivity of the network performance due to unexpected issues that may occur in the hardware system and thus explained the competitiveness of the proposed synaptic behavior in the saturation region.

For efficient processing, spatiotemporal spike patterns representing similar input must be able to transform into a less similar output. A new computational model with physiologically plausible parameters shows how the neuronal process referred to as “pattern separation” can be very well achieved by single neurons if the temporal qualities of the output patterns are considered. Spike patterns generated by a varying number of neurons firing with fixed different frequencies within a gamma range are used as input. The temporal and spatial summation of dendritic input combined with theta-oscillating excitability in the output neuron by subthreshold membrane potential oscillations (SMOs) lead to high temporal separation by different delays of output spikes of similar input patterns. A Winner Takes All (WTA) mechanism with backward inhibition suffices to transform the spatial overlap of input patterns to much less temporal overlap of the output patterns. The conversion of spatial patterns input into an output with differently delayed spikes enables high separation effects. Incomplete random connectivity spreads the times up to the first spike across a spatially expanded ensemble of output neurons. With the expansion, random connectivity becomes the spatial distribution mechanism of temporal features. Additionally, a “synfire chain” circuit is proposed to reconvert temporal differences into spatial ones.

Listeners with sensorineural hearing loss (SNHL) struggle to understand speech, especially in noise, despite audibility compensation. These real-world suprathreshold deficits are hypothesized to arise from degraded frequency tuning and reduced temporal-coding precision; however, peripheral neurophysiological studies testing these hypotheses have been largely limited to in-quiet artificial vowels. Here, we measured single auditory-nerve-fiber responses to a connected speech sentence in noise from anesthetized male chinchillas with normal hearing (NH) or noise-induced hearing loss (NIHL). Our results demonstrated that temporal precision was not degraded following acoustic trauma, and furthermore that sharpness of cochlear frequency tuning was not the major factor affecting impaired peripheral coding of connected speech in noise. Rather, the loss of cochlear tonotopy, a hallmark of NH, contributed the most to both consonant-coding and vowel-coding degradations. Because distorted tonotopy varies in degree across etiologies (e.g., noise exposure, age), these results have important implications for understanding and treating individual differences in speech perception for people suffering from SNHL. [ABSTRACT FROM AUTHOR]

Neurons are known to encode information not just by how frequently they fire, but also at what times they fire. However, characterizations of temporal encoding in sensory cortices under conditions of health and injury are limited. Here we characterized and compared the stimulus‐evoked activity of 1210 online‐sorted units in layers II and IV of rat barrel cortex under healthy and diffuse traumatic brain injury (TBI) (caused by a weight‐drop model) conditions across three timepoints post‐injury: four days, two weeks, and eight weeks. Temporal activity patterns in the first 50 ms post‐stimulus recording showed four categories of responses: no response or 1, 2, or 3 temporally‐distinct response components, that is, periods of high unit activity separated by silence. The relative proportions of unit response categories were similar between layers II and IV in healthy conditions but not in early post‐TBI conditions. For units with multiple response components, inter‐component timings were reliable in healthy and late post‐TBI conditions but disrupted by injury. Response component times typically shifted earlier with increasing stimulus intensity and this was more pronounced in layer IV than layer II. Surprisingly, injury caused a reversal of this trend and in the late post‐TBI condition no stimulus intensity‐dependence differences were observed between layers II and IV. We speculate this indicates a potential compensatory mechanism in response to injury. These results demonstrate how temporal encoding features maladapt or functionally recover differently in sensory cortex after TBI. Such maladaptation or functional recovery is layer‐dependent, perhaps due to differences in thalamic input or local inhibitory neuronal makeup. [ABSTRACT FROM AUTHOR]

The pitch of harmonic complex tones (HCTs) common in speech, music, and animal vocalizations plays a key role in the perceptual organization of sound. Unraveling the neural mechanisms of pitch perception requires animal models, but little is known about complex pitch perception by animals, and some species appear to use different pitch mechanisms than humans. Here, we tested rabbits' ability to discriminate the fundamental frequency (F0) of HCTs with missing fundamentals, using a behavioral paradigm inspired by foraging behavior in which rabbits learned to harness a spatial gradient in F0 to find the location of a virtual target within a room for a food reward. Rabbits were initially trained to discriminate HCTs with F0s in the range 400-800 Hz and with harmonics covering a wide frequency range (800-16,000 Hz) and then tested with stimuli differing in spectral composition to test the role of harmonic resolvability (experiment 1) or in F0 range (experiment 2) or in both F0 and spectral content (experiment 3). Together, these experiments show that rabbits can discriminate HCTs over a wide F0 range (200-1,600 Hz) encompassing the range of conspecific vocalizations and can use either the spectral pattern of harmonics resolved by the cochlea for higher F0s or temporal envelope cues resulting from interaction between unresolved harmonics for lower F0s. The qualitative similarity of these results to human performance supports the use of rabbits as an animal model for studies of pitch mechanisms, providing species differences in cochlear frequency selectivity and F0 range of vocalizations are taken into account. NEW & NOTEWORTHY Understanding the neural mechanisms of pitch perception requires experiments in animal models, but little is known about pitch perception by animals. Here we show that rabbits, a popular animal in auditory neuroscience, can discriminate complex sounds differing in pitch using either spectral cues or temporal cues. The results suggest that the role of spectral cues in pitch perception by animals may have been underestimated by predominantly testing low frequencies in the range of human voice. [ABSTRACT FROM AUTHOR]

Classical Artificial Neural Networks (ANNs) though well exploited in solving classification problems, do not model perfectly the information encoding process in the human brain because ANNs encode information using rate-based coding. However, biological neurons in the brain are known to encode information using temporal coding. In order to mimic the biological method of encoding information, various Spiking Neural Network (SNN) models have been developed. However, some of these models are limited in the number of spikes and do not leverage well on some classification problems. In order to address some of the inherent challenges associated with SNN, a multi-layer learning model for a multi-spiking network is proposed in this paper. The model exploits the temporal coding of spikes and the least-squares method to derive a weight update scheme. It also employs a spike locality concept in order to determine how the synaptic weights are to be adjusted at a particular spike time so as to minimize the learning interference, and thereby, increasing the number of spikes for learning. The performance of the model is evaluated on benchmarked classification datasets. A correlation-based metric is combined with a threshold concept to measure the classification accuracy of the model. The experimental results showed that the proposed model achieved better classification accuracy than some state-of-the-art multi-layer SNN learning models.

A Spiking Neural Network (SNN) can be trained indirectly by first training an Artificial Neural Network (ANN) with the conventional backpropagation algorithm, then converting it into an equivalent SNN. To reduce the computational cost of the resulting SNN as measured by the number of spikes, we present Logarithmic Temporal Coding (LTC), where the number of spikes used to encode an activation grows logarithmically with the activation value; and the accompanying Exponentiate-and-Fire (EF) neuron model, which only involves efficient bit-shift and addition operations. Moreover, we improve the training process of ANN to compensate for approximation errors due to LTC. Experimental results indicate that the resulting SNN achieves competitive performance in terms of classification accuracy at significantly lower computational cost than related work.

Abstract Neurons are known to encode information not just by how frequently they fire, but also at what times they fire. However, characterizations of temporal encoding in sensory cortices under conditions of health and injury are limited. Here we characterized and compared the stimulus‐evoked activity of 1210 online‐sorted units in layers II and IV of rat barrel cortex under healthy and diffuse traumatic brain injury (TBI) (caused by a weight‐drop model) conditions across three timepoints post‐injury: four days, two weeks, and eight weeks. Temporal activity patterns in the first 50 ms post‐stimulus recording showed four categories of responses: no response or 1, 2, or 3 temporally‐distinct response components, that is, periods of high unit activity separated by silence. The relative proportions of unit response categories were similar between layers II and IV in healthy conditions but not in early post‐TBI conditions. For units with multiple response components, inter‐component timings were reliable in healthy and late post‐TBI conditions but disrupted by injury. Response component times typically shifted earlier with increasing stimulus intensity and this was more pronounced in layer IV than layer II. Surprisingly, injury caused a reversal of this trend and in the late post‐TBI condition no stimulus intensity‐dependence differences were observed between layers II and IV. We speculate this indicates a potential compensatory mechanism in response to injury. These results demonstrate how temporal encoding features maladapt or functionally recover differently in sensory cortex after TBI. Such maladaptation or functional recovery is layer‐dependent, perhaps due to differences in thalamic input or local inhibitory neuronal makeup.

Information processing in the nervous system critically relies on temporally precise spiking activity. In the auditory system, various degrees of phase-locking can be observed from the auditory nerve to cortical neurons. The classical metric for quantifying phase-locking is the vector strength (VS), which captures the periodicity in neuronal spiking. More recently, another metric, called the correlation index (CI), was proposed to quantify the temporally reproducible response characteristics of a neuron. The CI is defined as the peak value of a normalized shuffled autocorrelogram (SAC). Both VS and CI have been used to investigate how temporal information is processed and propagated along the auditory pathways. While previous analyses of physiological data in cats suggested covariation of these two metrics, general characterization of their connection has never been performed. In the present study, we derive a rigorous relationship between VS and CI. To model phase-locking, we assume Poissonian spike trains with a temporally changing intensity function following a von Mises distribution. We demonstrate that VS and CI are mutually related via the so-called concentration parameter that determines the degree of phase-locking. We confirm that these theoretical results are largely consistent with physiological data recorded in the auditory brainstem of various animals. In addition, we generate artificial phase-locked spike sequences, for which recording and analysis parameters can be systematically manipulated. Our analysis results suggest that mismatches between empirical data and the theoretical prediction can often be explained with deviations from the von Mises distribution, including skewed or multimodal period histograms. Furthermore, temporal relations of spike trains across trials can contribute to higher CI values than predicted mathematically based on the VS. We find that, for most applications, a SAC bin width of 50 ms seems to be a favorable choice, leading to an estimated error below 2.5% for physiologically plausible conditions. Overall, our results provide general relations between the two measures of phase-locking and will aid future analyses of different physiological datasets that are characterized with these metrics. [ABSTRACT FROM AUTHOR]

Biologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes. [ABSTRACT FROM AUTHOR]