Spike Timing Research Articles

Optogenetic Interrogation of Electrophysiological Dendritic Properties and Their Effect on Pacemaking Neurons from Acute Rodent Brain Slices.

Understanding dendritic excitability is essential for a complete and precise characterization of neurons' input-output relationships. Theoretical and experimental work demonstrates that the electrotonic and nonlinear properties of dendrites can alter the amplitude (e.g., through amplification) and latency of synaptic inputs as viewed in the axosomatic region where spike timing is determined. The gold-standard technique to study dendritic excitability is using dual-patch recordings with a high-resistance electrode used to patch a piece of distal dendrite in addition to a somatic patch electrode. However, this approach is often impractical when distal dendrites are too fine to patch. Therefore, we developed a technique that utilizes the expression of Channelrhodopsin-2 (ChR2) to study dendritic excitability in acute brain slices through the combination of a somatic patch electrode and optogenetic activation. The protocol describes how to prepare acute slices from mice that express ChR2 in specific cell types, and how to use two modes of light stimulation: proximal (which activates the soma and proximal dendrites in a ~100 µm diameter surrounding the soma) with the use of a high-magnification objective and full-field stimulation through a low-magnification objective (which activates the entire somato-dendritic field of the neuron). We use this technique in conjunction with various stimulation protocols to estimate model-based spectral components of dendritic filtering and the impact of dendrites on phase response curves, peri-stimulus time histograms, and entrainment of pacemaking neurons. This technique provides a novel use of optogenetics to study intrinsic dendritic excitability through the use of standard patch-clamp slice physiology. Key features • A method for studying the effects of electrotonic and nonlinear dendritic properties on the sub- and suprathreshold responses of pacemaking neurons. • Combines somatic patch clamp or perforated patch recordings with optogenetic activation in acute brain slices to investigate dendritic linear transformation without patching the dendrite. • Oscillatory illumination at various frequencies estimates spectral properties of the dendrite using subthreshold voltage-clamp recordings and studies entrainment of pacemakers in current clamp recordings. • This protocol uses Poisson white noise illumination to estimate dendritic phase response curves and peri-stimulus time histograms.

GluN2A or GluN2B subunits of the NMDA receptor contribute to changes in neuronal excitability and impairments in LTP in the hippocampus of aging mice but do not mediate detrimental effects of oligomeric Aβ (1-42).

Studies in rodent models have revealed that oligomeric beta-amyloid protein [Aβ (1-42)] plays an important role in the pathogenesis of Alzheimer's disease. Early elevations in hippocampal neuronal excitability caused by Aβ (1-42) have been proposed to be mediated via enhanced activation of GluN2B-containing N-methyl-D-aspartate receptors (NMDAR). To what extent GluN2A or GluN2B-containing NMDAR contribute to Aβ (1-42)-mediated impairments of hippocampal function in advanced rodent age is unclear. Here, we assessed hippocampal long-term potentiation (LTP) and neuronal responses 4-5 weeks after bilateral intracerebral inoculation of 8-15 month old GluN2A+/- or GluN2B+/- transgenic mice with oligomeric Aβ (1-42), or control peptide. Whole-cell patch-clamp recordings in CA1 pyramidal neurons revealed a more positive resting membrane potential and increased total spike time in GluN2A+/-, but not GluN2B+/--hippocampi following treatment with Aβ (1-42) compared to controls. Action potential 20%-width was increased, and the descending slope was reduced, in Aβ-treated GluN2A+/-, but not GluN2B+/- hippocampi. Sag ratio was increased in Aβ-treated GluN2B+/--mice. Firing frequency was unchanged in wt, GluN2A+/-, and GluN2B+/-hippocampi after Aβ-treatment. Effects were not significantly different from responses detected under the same conditions in wt littermates, however. LTP that lasted for over 2 h in wt hippocampal slices was significantly reduced in GluN2A+/- and was impaired for 15 min in GluN2B+/--hippocampi compared to wt littermates. Furthermore, LTP (>2 h) was significantly impaired in Aβ-treated hippocampi of wt littermates compared to wt treated with control peptide. LTP induced in Aβ-treated GluN2A+/- and GluN2B+/--hippocampi was equivalent to LTP in control peptide-treated transgenic and Aβ-treated wt animals. Taken together, our data indicate that knockdown of GluN2A subunits subtly alters membrane properties of hippocampal neurons and reduces the magnitude of LTP. GluN2B knockdown reduces the early phase of LTP but leaves later phases intact. Aβ (1-42)-treatment slightly exacerbates changes in action potential properties in GluN2A+/--mice. However, the vulnerability of the aging hippocampus to Aβ-mediated impairments of LTP is not mediated by GluN2A or GluN2B-containing NMDAR.

Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN.

In this study, we explore spintronic synapses composed of several Magnetic Tunnel Junctions (MTJs), leveraging their attractive characteristics such as endurance, nonvolatility, stochasticity, and energy efficiency for hardware implementation of unsupervised neuromorphic systems. Spiking Neural Networks (SNNs) running on dedicated hardware are suitable for edge computing and IoT devices where continuous online learning and energy efficiency are important characteristics. We focus in this work on synaptic plasticity by conducting comprehensive electrical simulations to optimize the MTJ-based synapse design and find the accurate neuronal pulses that are responsible for the Spike Timing Dependent Plasticity (STDP) behavior. Most proposals in the literature are based on hardware-independent algorithms that require the network to store the spiking history to be able to update the weights accordingly. In this work, we developed a new learning rule, the Bi-Sigmoid STDP (B2STDP), which originates from the physical properties of MTJs. This rule enables immediate synaptic plasticity based on neuronal activity, leveraging in-memory computing. Finally, the integration of this learning approach within an SNN framework leads to a 91.71% accuracy in unsupervised image classification, demonstrating the potential of MTJ-based synapses for effective online learning in hardware-implemented SNNs.

Biophysics of Frequency-Dependent Variation in Paresthesia and Pain Relief during Spinal Cord Stimulation.

The neurophysiological effects of spinal cord stimulation (SCS) for chronic pain are poorly understood, resulting in inefficient failure-prone programming protocols and inadequate pain relief. Nonetheless, novel stimulation patterns are regularly introduced and adopted clinically. Traditionally, paresthetic sensation is considered necessary for pain relief, although novel paradigms provide analgesia without paresthesia. However, like pain relief, the neurophysiological underpinnings of SCS-induced paresthesia are unknown. Here, we paired biophysical modeling with clinical paresthesia thresholds (of both sexes) to investigate how stimulation frequency affects the neural response to SCS relevant to paresthesia and analgesia. Specifically, we modeled the dorsal column (DC) axonal response, dorsal column nucleus (DCN) synaptic transmission, conduction failure within DC fiber collaterals, and dorsal horn network output. Importantly, we found that high-frequency stimulation reduces DC fiber activation thresholds, which in turn accurately predicts clinical paresthesia perception thresholds. Furthermore, we show that high-frequency SCS produces asynchronous DC fiber spiking and ultimately asynchronous DCN output, offering a plausible biophysical basis for why high-frequency SCS is less comfortable and produces qualitatively different sensation than low-frequency stimulation. Finally, we demonstrate that the model dorsal horn network output is sensitive to SCS-inherent variations in spike timing, which could contribute to heterogeneous pain relief across patients. Importantly, we show that model DC fiber collaterals cannot reliably follow high-frequency stimulation, strongly affecting the network output and typically producing antinociceptive effects at high frequencies. Altogether, these findings clarify how SCS affects the nervous system and provide insight into the biophysics of paresthesia generation and pain relief.

Spike timing-based coding in neuromimetic tactile system enables dynamic object classification.

Rapid processing of tactile information is essential to human haptic exploration and dexterous object manipulation. Conventional electronic skins generate frames of tactile signals upon interaction with objects. Unfortunately, they are generally ill-suited for efficient coding of temporal information and rapid feature extraction. In this work, we report a neuromorphic tactile system that uses spike timing, especially the first-spike timing, to code dynamic tactile information about touch and grasp. This strategy enables the system to seamlessly code highly dynamic information with millisecond temporal resolution on par with the biological nervous system, yielding dynamic extraction of tactile features. Upon interaction with objects, the system rapidly classifies them in the initial phase of touch and grasp, thus paving the way to fast tactile feedback desired for neuro-robotics and neuro-prosthetics.

The impact of spike timing precision and spike emission reliability on decoding accuracy

Precisely timed and reliably emitted spikes are hypothesized to serve multiple functions, including improving the accuracy and reproducibility of encoding stimuli, memories, or behaviours across trials. When these spikes occur as a repeating sequence, they can be used to encode and decode a potential time series. Here, we show both analytically and in simulations that the error incurred in approximating a time series with precisely timed and reliably emitted spikes decreases linearly with the number of neurons or spikes used in the decoding. This was verified numerically with synthetically generated patterns of spikes. Further, we found that if spikes were imprecise in their timing, or unreliable in their emission, the error incurred in decoding with these spikes would be sub-linear. However, if the spike precision or spike reliability increased with network size, the error incurred in decoding a time-series with sequences of spikes would maintain a linear decrease with network size. The spike precision had to increase linearly with network size, while the probability of spike failure had to decrease with the square-root of the network size. Finally, we identified a candidate circuit to test this scaling relationship: the repeating sequences of spikes with sub-millisecond precision in area HVC (proper name) of the zebra finch. This scaling relationship can be tested using both neural data and song-spectrogram-based recordings while taking advantage of the natural fluctuation in HVC network size due to neurogenesis.

Synapse Neurotransmitter Channel-Inspired AlOx Memristor with "V" Type Oxygen Vacancy Distribution.

Memristor possesses great potential and advantages in neuromorphic computing, while consistency and power consumption issues have been hindering its commercialization. Low cost and accuracy are the advantages of human brain, so memristors can be used to construct brain-like synaptic devices to solve these problems. In this work, a five-layer AlOx device with a V-shaped oxygen distribution is used to simulate biological synapses. The device simulates synapse structurally. Further, under electrical stimulation, O2- moves to the Ti electrode and oxygen vacancy (Vo) moves to the Pt electrode, thus forming a conductive filament (CF), which simulates the Ca2+ flow and releases neurotransmitters to the postsynaptic membrane, thus realizing the transmission of information. By controlling applied voltage, the regulation of Ca2+ gated pathway is realized to control the Ca2+ internal flow and achieve different degrees of information transmission. Long-term Potentiation (LTP)/Long-term Depression (LTD), Spike Timing Dependent Plasticity (STDP), these basic synaptic performances can be simulated. The AlOx device realizes low power consumption (56.7 pJ/392 fJ), high switching speed (25ns/60ns), and by adjusting the window value, the nonlinearity is improved (0.133/0.084), a high recognition accuracy (98.18%) is obtained in neuromorphic simulation. It shows a great prospect in multi-value storage and neuromorphic computing.

Theta Phase Precession and the Dynamics of Spatiotemporal Asymmetry in Hippocampal Place Fields

A consistently observed characteristic of hippocampal place fields is the shifting of the field's center of mass and the subsequent development of asymmetry. This is evidenced by the expansion of the place field in an asymmetric manner over repeated traversals, wherein a rat's initial entry into the field prompts a low firing rate that substantially increases as it exits. Our study delves deeper into these spatiotemporal asymmetries of place field spiking, specifically examining their relationship with theta oscillations and disentangling the effects from environmental factors. Analysis of neuronal data from a T-maze alternation task revealed a distinct non-uniform firing pattern of place cells. This pattern was markedly skewed in relation to the phases of theta cycles, indicating a phase precession effect. We found that as the rat navigates through the place field, there is a systematic advancement of spike timing, with action potentials tending to cluster in the early phases of theta during the exit phase of the field. Such temporal coordination of spikes suggests that the timing of neural activity within theta cycles is as integral as spatial location, influencing the dynamics of place field expansion and potentially affecting the hippocampal local field potential. This discovery underscores the role of theta phase precession in spatial encoding, proposing a novel temporal component that could underpin the cognitive map within the hippocampus. R01 MH126236 R01 AG055544. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

High-performance deep spiking neural networks via at-most-two-spike exponential coding

Spiking neural networks (SNNs) provide necessary models and algorithms for neuromorphic computing. A popular way of building high-performance deep SNNs is to convert ANNs to SNNs, taking advantage of advanced and well-trained ANNs. Here we propose an ANN to SNN conversion methodology that uses a time-based coding scheme, named At-most-two-spike Exponential Coding (AEC), and a corresponding AEC spiking neuron model for ANN-SNN conversion. AEC neurons employ quantization-compensating spikes to improve coding accuracy and capacity, with each neuron generating up to two spikes within the time window. Two exponential decay functions with tunable parameters are proposed to represent the dynamic encoding thresholds, based on which pixel intensities are encoded into spike times and spike times are decoded into pixel intensities. The hyper-parameters of AEC neurons are fine-tuned based on the loss function of SNN-decoded values and ANN-activation values. In addition, we design two regularization terms for the number of spikes, providing the possibility to achieve the best trade-off between accuracy, latency and power consumption. The experimental results show that, compared to other similar methods, the proposed scheme not only obtains deep SNNs with higher accuracy, but also has more significant advantages in terms of energy efficiency and inference latency. More details can be found at https://github.com/RPDS2020/AEC.git.

Single-unit data for sensory neuroscience: Responses from the auditory nerve of young-adult and aging gerbils

This dataset was collected to study the functional consequences of age-related hearing loss for the auditory nerve, which carries acoustic information from the periphery to the central auditory system. Using high-impedance glass electrodes, raw voltage traces and spike times were recorded from more than one thousand single fibres of the auditory nerve of young-adult, middle-aged, and old Mongolian gerbils raised in a quiet environment. The dataset contains not only responses to simple acoustic stimuli to characterize the fibres, but also to more complex stimuli, such as speech logatomes in background noise and Schroeder-phase stimuli. A software toolbox is provided to search through the dataset, to plot various analysed outcomes, and to give insight into the analyses. This dataset may serve as a valuable resource to test further hypotheses about age-related hearing loss. Additionally, it can aid in optimizing available computational models of the auditory system, which can contribute to, or eventually even fully replace, animal experiments.

Control of working memory by phase–amplitude coupling of human hippocampal neurons

Retaining information in working memory is a demanding process that relies on cognitive control to protect memoranda-specific persistent activity from interference1,2. However, how cognitive control regulates working memory storage is unclear. Here we show that interactions of frontal control and hippocampal persistent activity are coordinated by theta–gamma phase–amplitude coupling (TG-PAC). We recorded single neurons in the human medial temporal and frontal lobe while patients maintained multiple items in their working memory. In the hippocampus, TG-PAC was indicative of working memory load and quality. We identified cells that selectively spiked during nonlinear interactions of theta phase and gamma amplitude. The spike timing of these PAC neurons was coordinated with frontal theta activity when cognitive control demand was high. By introducing noise correlations with persistently active neurons in the hippocampus, PAC neurons shaped the geometry of the population code. This led to higher-fidelity representations of working memory content that were associated with improved behaviour. Our results support a multicomponent architecture of working memory1,2, with frontal control managing maintenance of working memory content in storage-related areas3–5. Within this framework, hippocampal TG-PAC integrates cognitive control and working memory storage across brain areas, thereby suggesting a potential mechanism for top-down control over sensory-driven processes.

Comparison of unitary synaptic currents generated by indirect and direct pathway neurons of the mouse striatum.

Two subtypes of striatal spiny projection neurons, iSPNs and dSPNs, whose axons form the "indirect" and "direct" pathways of the basal ganglia, respectively, both make synaptic connections in the external globus pallidus (GPe) but are usually found to have different effects on behavior. Activation of the terminal fields of iSPNs or dSPNs generated compound currents in almost all GPe neurons. To determine whether iSPNs and dSPNs have the same or different effects on pallidal neurons, we studied the unitary synaptic currents generated in GPe neurons by action potentials in single striatal neurons. We used optogenetic excitation to elicit repetitive firing in a small number of nearby SPNs, producing sparse barrages of inhibitory postsynaptic currents (IPSCs) in GPe neurons. From these barrages, we isolated sequences of IPSCs with similar time courses and amplitudes, which presumably arose from the same SPN. There was no difference between the amplitudes of unitary IPSCs generated by the indirect and direct pathways. Most unitary IPSCs were small, but a subset from each pathway were much larger. To determine the effects of these unitary synaptic currents on the action potential firing of GPe neurons, we drove SPNs to fire as before and recorded the membrane potential of GPe neurons. Large unitary potentials from iSPNs and dSPNs perturbed the spike timing of GPe neurons in a similar way. Most SPN-GPe neuron pairs are weakly connected, but a subset of pairs in both pathways are strongly connected.NEW & NOTEWORTHY This is the first study to record the synaptic currents generated by single identified direct or indirect pathway striatal neurons on single pallidal neurons. Each GPe neuron receives synaptic inputs from both pathways. Most striatal neurons generate small synaptic currents that become influential when occurring together, but a few are powerful enough to be individually influential.

Characterizing the spike timing of a chaotic laser by using ordinal analysis and machine learning.

Semiconductor lasers with optical feedback are well-known nonlinear dynamical systems. Under appropriate feedback conditions, these lasers emit optical pulses that resemble neural spikes. Influenced by feedback delay and various noise sources, including quantum spontaneous emission noise, the dynamics are highly stochastic. A good understanding of the spike timing statistics is needed to develop photonic systems capable of using the fast-spiking laser output for novel applications, such as information processing or random number generation. Here we analyze experimental sequences of inter-spike intervals (ISIs) recorded when a sinusoidal signal was applied to the laser current. Different combinations of the DC value and frequency of the signal applied to the laser lead to ISI sequences with distinct statistical properties. This variability prompts an investigation into the relationship between experimental parameters and ISI sequence statistics, aiming to uncover potential encoding methods for optical spikes, since this can open a new way of encoding and decoding information in sequences of optical spikes. By using ordinal analysis and machine learning, we show that the ISI sequences have statistical ordinal properties that are similar to Flicker noise signals, characterized by a parameter α that varies with the signal that was applied to the laser current when the ISIs were recorded. We also show that for this dataset, the (α, permutation entropy) plane is more informative than the (complexity, permutation entropy) plane because it allows better differentiation of ISI sequences recorded under different experimental conditions, as well as better differentiation of original and surrogate ISI sequences.

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task.

Objective.Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different objects. While dynamics of object recognition and decision making are usually ignored in object recognition models, here we proposed a fully spiking hierarchical model, explaining the process of object recognition from information representation to making decision.Approach.Coupling a deep neural network and a recurrent attractor based decision making model beside using spike time dependent plasticity learning rules in several convolutional and pooling layers, we proposed a model which can resemble brain behaviors during an object recognition task. We also measured human choices and reaction times in a psychophysical object recognition task and used it as a reference to evaluate the model.Main results.The proposed model explains not only the probability of making a correct decision but also the time that it takes to make a decision. Importantly, neural firing rates in both feature representation and decision making levels mimic the observed patterns in animal studies (number of spikes (p-value < 10-173) and the time of the peak response (p-value < 10-31) are significantly modulated with the strength of the stimulus). Moreover, the speed-accuracy trade-off as a well-known characteristic of decision making process in the brain is also observed in the model (changing the decision bound significantly affect the reaction time (p-value < 10-59) and accuracy (p-value < 10-165)).Significance.We proposed a fully spiking deep neural network which can explain dynamics of making decision about an object in both neural and behavioral level. Results showed that there is a strong and significant correlation (r= 0.57) between the reaction time of the model and of human participants in the psychophysical object recognition task.

Memory device based on MoS2-polyvinyl alcohol for simulating synaptic behavior

A memory device based on MoS2-polyvinyl alcohol was fabricated. As a good dispersant, polyvinyl alcohol solution can disperse 2D layered materials more evenly in the solution. The 19 states of the device conductance are modulated step by step by direct current scanning, which makes it possible to perform the computing task in memory. Then, the presence of conductive filaments allows the resistance state of the memristor to be dynamically transformed, which is similar to some basic behavior of biological synapses. Further, a variety of conductivity states such as short-term potentiation, short term depression, long term potentiation and long-term depression behavior are modulated by applying voltage to simulate the basic behavior of biological synapses. Finally, the paired pulse facilitation, paired pulse depression, spiking timing dependent plasticity and spiking voltage dependent plasticity behaviors of biological synapses are simulated in memristor devices.

Fractional order memcapacitive neuromorphic elements reproduce and predict neuronal function

There is an increasing need to implement neuromorphic systems that are both energetically and computationally efficient. There is also great interest in using electric elements with memory, memelements, that can implement complex neuronal functions intrinsically. A feature not widely incorporated in neuromorphic systems is history-dependent action potential time adaptation which is widely seen in real cells. Previous theoretical work shows that power-law history dependent spike time adaptation, seen in several brain areas and species, can be modeled with fractional order differential equations. Here, we show that fractional order spiking neurons can be implemented using super-capacitors. The super-capacitors have fractional order derivative and memcapacitive properties. We implemented two circuits, a leaky integrate and fire and a Hodgkin–Huxley. Both circuits show power-law spiking time adaptation and optimal coding properties. The spiking dynamics reproduced previously published computer simulations. However, the fractional order Hodgkin–Huxley circuit showed novel dynamics consistent with criticality. We compared the responses of this circuit to recordings from neurons in the weakly-electric fish that have previously been shown to perform fractional order differentiation of their sensory input. The criticality seen in the circuit was confirmed in spontaneous recordings in the live fish. Furthermore, the circuit also predicted long-lasting stimulation that was also corroborated experimentally. Our work shows that fractional order memcapacitors provide intrinsic memory dependence that could allow implementation of computationally efficient neuromorphic devices. Memcapacitors are static elements that consume less energy than the most widely studied memristors, thus allowing the realization of energetically efficient neuromorphic devices.

TIPS-pentacene organic field-effect transistor for optoelectronic neuromorphic simulation

Biological learning and memory of visual information are carried out through the regulation of synaptic weight by visual neuron synapses. Replicating the cognitive processes and light-induced adaptability of the human brain holds immense significance for the advancement of artificial intelligence. Here, we propose an organic photoelectric synaptic transistor based on 6,13-bis(triisopropylsilylethynyl) pentacene. The device showcases a memory window greater than 20 V and current on/off ratio of 106. Synaptic behavior transition is exhibited from short-term plasticity to long-term plasticity under photoelectric pulse modulation, including paired-pulse facilitation, spike time dependent plasticity, spike number dependent plasticity and optical enhancement/electrical inhibition. In addition, we simulate the learning and adaptation process of the synaptic device after long-term light stimulation, and recognize visual information by perceiving light pulses in different time domains.

Defects Act in an “Introverted” Manner in FeNiCrCoCu High-Entropy Alloy under Primary Damage

High-entropy alloys (HEAs) attract much attention as possible radiation-resistant materials due to their several unique properties. In this work, the generation and evolution of the radiation damage response of an FeNiCrCoCu HEA and bulk Ni in the early stages were explored using molecular dynamics (MD). The design, concerned with investigating the irradiation tolerance of the FeNiCrCoCu HEA, encompassed the following: (1) The FeNiCrCoCu HEA structure was obtained through a hybrid method that combined Monte Carlo (MC) and MD vs. the random distribution of atoms. (2) Displacement cascades caused by different primary knock-on atom (PKA) energy levels (500 to 5000 eV) of the FeNiCrCoCu HEA vs. bulk Ni were simulated. There was almost no element segregation in bulk FeNiCrCoCu obtained with the MD/MC method by analyzing the Warren–Cowley short-range order (SRO) parameters. In this case, the atom distribution was similar to the random structure that was selected as a substrate to conduct the damage cascade process. A mass of defects (interstitials and vacancies) was generated primarily by PKA departure. The number of adatoms grew, which slightly roughened the surface, and the defects were distributed deeper as the PKA energy increased for both pure Ni and the FeNiCrCoCu HEA. At the time of thermal spike, one fascinating phenomenon occurred where the number of Frenkel pairs for HEA was more than that for pure Ni. However, we obtained the opposite result, that fewer Frenkel pairs survived in the HEA than in pure Ni in the final state of the damage cascade. The number and size of defect clusters grew with increasing PKA energy levels for both materials. Defects were suppressed in the HEA; that is to say, defects were “cowards”, behaving in an introverted manner according to the anthropomorphic rhetorical method.

A novel point process model for neuronal spike trains

Point process provides a mathematical framework for characterizing neuronal spiking activities. Classical point process methods often focus on the conditional intensity function, which describes the likelihood at any time point given its spiking history. However, these models do not describe the central tendency or importance of the spike train observations. Based on the recent development on the notion of center-outward rank for point process, we propose a new modeling framework on spike train data. The new likelihood of a spike train is a product of the marginal probability on the number of spikes and the probability of spike timings conditioned on the same number. In particular, the conditioned distribution is calculated by adopting the well-known Isometric Log-Ratio transformation. We systematically compare the new likelihood with the state-of-the-art point process likelihoods in terms of ranking, outlier detection, and classification using simulations and real spike train data. This new framework can effectively identify templates as well as outliers in spike train data. It also provides a reasonable model, and the parameters can be efficiently estimated with conventional maximum likelihood methods. It is found that the proposed likelihood provides an appropriate ranking on the spike train observations, effectively detects outliers, and accurately conducts classification tasks in the given data.

Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients

We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 participants during movie watching. Recordings consist of single neurons, local field potential, and intracranial EEG activity acquired from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex implanted for monitoring of epileptic seizures. Participants watched an 8-min long excerpt from the video “Bang! You’re Dead” and performed a recognition memory test for movie content. 3 T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes spike times, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI. This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.