Drawing inferences from Fano factor calculations

Conductance properties ofα-T3Corbino disks

In this work, we investigate anα-T3lattice in the form of a Corbino disk, characterized by inner and outer radiiR1andR2, threaded by a tunable magnetic flux. Through exact (analytic) solution of the stationary Dirac-Weyl equation, we compute the transmission probability of the carriers and hence obtain the conductance features for0<α⩽1(αdenotes the strength of the hopping between the central atom and one of the other two) which allows ascertaining the role of the flat band, alongwith scrutinizing the transport features from graphene to a dice lattice. Our results reveal periodic Aharonov-Bohm (AB) oscillations in the conductance, reminiscent of the utility of the Corbino disk as an electron pump. Further, these results are strongly influenced by parameters, such as, doping level, ratio of the inner and outer radii, magnetic flux, andα. Additionally, complex quantum interference effect resulting in the possible emergence of higher harmonic modes and split-peak structures in the conductance, become prominent for smallerαvalues and larger ratios of the radii. We also find that, away from the charge-neutrality point (zero doping), the conductance oscillations are more pronounced and sensitive to the various parameters, with the corresponding behavior largely governed via the evanescent wave transport. Further, the Fano factor reveals distinct transport regimes, transitioning from Poissonian to pseudo-diffusive forα < 1, and from ballistic to pseudo-diffusive at the dice limit (α = 1). Thus, this setup serves as a fertile ground for studying the generation of quantum Hall current and AB oscillations in a flat band system, alongwith demonstrating intricate appearance of higher harmonics in the electron transport. Finally, to put things in perspective, we have compared our results with those for graphene disks that highlight the difference between the two with regard to device applications.

A unifying model for microRNA-guided silencing of messenger RNAs

Silencing by the miRNA-guided RNA induced silencing complex (miRISC) is dependent on Ago2-chaperoned base pairing between the miRNA 5' seed (5'S) and a complementary sequence in the 3' untranslated region of an mRNA. Prevailing mechanistic understanding posits that initial 5'S pairing can further allow functional base pair expansion into the 3' non-seed (3'NS), while functionally distinct non-canonical pairing was reported between only the 3'NS and the mRNA coding sequence. We developed single-molecule kinetics through equilibrium Poisson sampling (SiMKEPS) to measure highly precise binding and dissociation rate constants of varying-length target sequences to 5'S and 3'NS in a paradigmatic miRISC isolated from human cells, revealing distinct stable states of miRISC with mutually exclusive 5'S and 3'NS pairing. Our data suggest conformational rearrangements of the Ago2-bound miRNA that regulate alternative 5'S- and 3'NS-driven target recognition. The resulting model reconciles previously disparate observations and deepens our acumen for successfully marshaling RNA silencing therapies.

The geometry of correlated variability leads to highly suboptimal discriminative sensory coding

The brain represents the world through the activity of neural populations; however, whether the computational goal of sensory coding is to support discrimination of sensory stimuli or to generate an internal model of the sensory world is unclear. Correlated variability across a neural population (noise correlations) is commonly observed experimentally, and many studies demonstrate that correlated variability improves discriminative sensory coding compared to a null model with no correlations. However, such results do not address whether correlated variability is optimal for discriminative sensory coding. If the computational goal of sensory coding is discriminative, than correlated variability should be optimized to support that goal. We assessed optimality of noise correlations for discriminative sensory coding in diverse datasets by developing two novel null models, each with a biological interpretation. Across datasets, we found that correlated variability in neural populations leads to highly suboptimal discriminative sensory coding according to both null models. Furthermore, biological constraints prevent many subsets of the neural populations from achieving optimality, and subselecting based on biological criteria leaves red discriminative coding performance suboptimal. Finally, we show that optimal subpopulations are exponentially small as the population size grows. Together, these results demonstrate that the geometry of correlated variability leads to highly suboptimal discriminative sensory coding.NEW & NOTEWORTHY The brain represents the world through the activity of neural populations that exhibit correlated variability. We assessed optimality of correlated variability for discriminative sensory coding in diverse datasets by developing two novel null models. Across datasets, correlated variability in neural populations leads to highly suboptimal discriminative sensory coding according to both null models. Biological constraints prevent the neural populations from achieving optimality. Together, these results demonstrate that the geometry of correlated variability leads to highly suboptimal discriminative sensory coding.

Real-world epilepsy monitoring with ultra long-term subcutaneous EEG: a 15-month prospective study

Objective

Novel subcutaneous electroencephalography (sqEEG) systems enable prolonged, near-continuous cerebral monitoring in real-world conditions. Nevertheless, the feasibility, acceptability and overall clinical utility of these systems remains unclear. We report on the longest observational study using ultra long-term sqEEG to date.

Methods

We conducted a 15-month prospective, observational study including ten adult people with treatment-resistant epilepsy. After device implantation, patients were asked to record sqEEG, to use an electronic seizure diary and to complete acceptability and usability questionnaires. sqEEG seizures were annotated visually, aided by automated detection. Seizure clustering was assessed via Fano Factor analysis and seizure periodicity at multiple timescales was investigated through circular statistics.

Results

Over a median duration of 438 days, ten patients recorded a median 18.8 hours/day, totalling 71,984 hours of real-world sqEEG data. Adherence and acceptability remained high throughout the study. While 754 sqEEG seizures were recorded across patients, over half (52%) of these were not reported in the patient diary. Of the 140 (27%) diary reports not associated with an identifiable sqEEG seizure, the majority (68%) were reported as seizures with preserved awareness. The sqEEG to diary F1 agreement score was highly variable, ranging from 0.06 to 0.97. Patient-specific patterns of seizure clustering and seizure periodicity were observed at multiple (circadian and multidien) timescales.

Interpretation

We demonstrate feasibility and high acceptability of ultra long-term (months-years) sqEEG monitoring. These systems help provide real-world, more objective seizure counting compared to patient diaries. It is possible to monitor individual temporal fluctuations of seizure occurrence, including seizure cycles.

Connectivity Network Feature Sharing in Single-Cell RNA Sequencing Data Identifies Rare Cells

Single-cell RNA sequencing is a valuable technique for identifying diverse cell subtypes. A key challenge in this process is that the detection of rare cells is often missed by conventional methods due to low abundance and subtle features of these cells. To overcome this, we developed SCLCNF (Local Connectivity Network Feature Sharing in Single-Cell RNA sequencing), a novel approach that identifies rare cells by analyzing features uniquely expressed in these cells. SCLCNF creates a cellular connectivity network, considering how each cell relates to its neighbors. This network helps to pinpoint coexpression patterns unique to rare cells, utilizing a rarity score to confirm their presence. Our method performs better in detecting rare cells than existing techniques, offering enhanced robustness. It has proven to be effective in human gastrula data sets for accurately pinpointing rare cells, and in sepsis data sets where it uncovers previously unidentified rare cell populations.

Thalamic epileptic spikes disrupt sleep spindles in patients with epileptic encephalopathy

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Epileptic encephalopathies share the trait of spike-wave activation during non-REM sleep (EE-SWAS), a sleep stage dominated by sleep spindles, which are brain oscillations known to coordinate offline memory consolidation. Epileptic activity has been proposed to hijack the circuits driving these thalamocortical oscillations, thereby contributing to cognitive impairment. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we provide evidence for epileptic spike interference of thalamic sleep spindle production in patients with EE-SWAS. First, we show that epileptic spikes and sleep spindles are both predicted by slow oscillations during stage two sleep (N2), but at different phases of the slow oscillation. Next, we demonstrate that sleep-activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate increases after a cortical spike, P ≈ 0). We then show that epileptic spikes in the thalamus increase the thalamic spindle refractory period (P ≈ 0). Finally, we show that in three patients with EE-SWAS, there is a downregulation of sleep spindles for 30 s after each thalamic spike (P < 0.01). These direct human thalamocortical observations support a proposed mechanism for epileptiform activity to impact cognitive function, wherein epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy with spike and wave activation during sleep.

Stretch-Induced Injury Affects Cortical Neuronal Networks in a Time- and Severity-Dependent Manner

Traumatic brain injury (TBI) is the leading cause of accident-related death and disability in the world and can lead to long-term neuropsychiatric symptoms, such as a decline in cognitive function and neurodegeneration. TBI includes primary and secondary injury, with head trauma and deformation of the brain caused by the physical force of the impact as primary injury, and cellular and molecular cascades that lead to cell death as secondary injury. Currently, there is no treatment for TBI-induced cell damage and neural circuit dysfunction in the brain, and thus, it is important to understand the underlying cellular mechanisms that lead to cell damage. In the current study, we use stretchable microelectrode arrays (sMEAs) to model the primary injury of TBI to study the electrophysiological effects of physically injuring cortical cells. We recorded electrophysiological activity before injury and then stretched the flexible membrane of the sMEAs to injure the cells to varying degrees. At 1, 24, and 72 h post-stretch, we recorded activity to analyze differences in spike rate, Fano factor, burstlet rate, burstlet width, synchrony of firing, local network efficiency, and Q statistic. Our results demonstrate that mechanical injury changes the firing properties of cortical neuron networks in culture in a time- and severity-dependent manner. Our results suggest that changes to electrophysiological properties after stretch are dependent on the strength of synchronization between neurons prior to injury.

Time-dependent homeostatic mechanisms underlie brain-derived neurotrophic factor action on neural circuitry

Plasticity and homeostatic mechanisms allow neural networks to maintain proper function while responding to physiological challenges. Despite previous work investigating morphological and synaptic effects of brain-derived neurotrophic factor (BDNF), the most prevalent growth factor in the central nervous system, how exposure to BDNF manifests at the network level remains unknown. Here we report that BDNF treatment affects rodent hippocampal network dynamics during development and recovery from glutamate-induced excitotoxicity in culture. Importantly, these effects are not obvious when traditional activity metrics are used, so we delve more deeply into network organization, functional analyses, and in silico simulations. We demonstrate that BDNF partially restores homeostasis by promoting recovery of weak and medium connections after injury. Imaging and computational analyses suggest these effects are caused by changes to inhibitory neurons and connections. From our in silico simulations, we find that BDNF remodels the network by indirectly strengthening weak excitatory synapses after injury. Ultimately, our findings may explain the difficulties encountered in preclinical and clinical trials with BDNF and also offer information for future trials to consider.

The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian

Gene expression inherently gives rise to stochastic variation ("noise") in the production of gene products. Minimizing noise is crucial for ensuring reliable cellular functions. However, noise cannot be suppressed below a certain intrinsic limit. For constitutively expressed genes, this limit is typically assumed to be Poissonian noise, wherein the variance in mRNA numbers is equal to their mean. Here, we demonstrate that several cell division genes in fission yeast exhibit mRNA variances significantly below this limit. The reduced variance can be explained by a gene expression model incorporating multiple transcription and mRNA degradation steps. Notably, in this sub-Poissonian regime, distinct from Poissonian or super-Poissonian regimes, cytoplasmic noise is effectively suppressed through a higher mRNA export rate. Our findings redefine the lower limit of eukaryotic gene expression noise and uncover molecular requirements for achieving ultralow noise, which is expected to be important for vital cellular functions.

Inference on autoregulation in gene expression with variance-to-mean ratio

Some genes can promote or repress their own expressions, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation from gene expression data. This method only needs to compare the mean and variance of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.

Sleep spindles in the healthy brain from birth through 18 years

STUDY OBJECTIVE

Sleep spindles are present from birth and reflect cognitive functions across the lifespan, but normative values for this cognitive biomarker across development are lacking. This study aims to establish normative spindle features over development.

METHODS

All available normal 19-channel electroencephalograms from developmentally normal children between February 2002 and June 2021 in the MGH EEG lab were analyzed. Approximately, 20 000 spindles were hand-marked to train and validate an automated spindle detector across ages. Normative values for spindle rate, duration, frequency, refractory period, and interhemispheric lag are provided for each channel and each age.

RESULTS

Sleep EEGs from 567 developmentally normal children (range 0 days to 18 years) were included. The detector had excellent performance (F1 = 0.47). Maximal spindle activity is seen over central regions during infancy and adolescence and frontopolar regions during childhood. Spindle rate and duration increase nonlinearly, with the most rapid changes during the first 4 months of life and between ages 3 and 14 years. Peak spindle frequency follows a U-shaped curve and discrete frontal slow and central fast spindles are evident by 18 months. Spindle refractory periods decrease between ages 1 and 14 years while interhemispheric asynchrony decreases over the first 3 months of life and between ages 1 and 14 years.

CONCLUSIONS

These data provide age- and region-specific normative values for sleep spindles across development, where measures that deviate from these values can be considered pathological. As spindles provide a noninvasive biomarker for cognitive function across the lifespan, these normative measures can accelerate the discovery and diagnosis in neurodevelopmental disorders.

Probing Transient Riboswitch Structures via Single Molecule Accessibility Analysis

Riboswitches are a class of RNA motifs in the untranslated regions of bacterial messenger RNAs (mRNAs) that can adopt different conformations to regulate gene expression. The binding of specific small molecule or ion ligands, or other RNAs, influences the conformation the riboswitch adopts. Single Molecule Kinetic Analysis of RNA Transient Structure (SiM-KARTS) offers an approach for probing this structural isomerization, or conformational switching, at the level of single mRNA molecules. SiM-KARTS utilizes fluorescently labeled, short, sequence-complementary DNA or RNA oligonucleotide probes that transiently access a specific RNA conformation over another. Binding and dissociation to a surface-immobilized target RNA of arbitrary length are monitored by Total Internal Reflection Fluorescence Microscopy (TIRFM) and quantitatively analyzed, via spike train and burst detection, to elucidate the rate constants of isomerization, revealing mechanistic insights into riboswitching.

Quantitative properties of the creation and activation of a cell-intrinsic duration-encoding engram

The engram encoding the interval between the conditional stimulus (CS) and the unconditional stimulus (US) in eyeblink conditioning resides within a small population of cerebellar Purkinje cells. CSs activate this engram to produce a pause in the spontaneous firing rate of the cell, which times the CS-conditional blink. We developed a Bayesian algorithm that finds pause onsets and offsets in the records from individual CS-alone trials. We find that the pause consists of a single unusually long interspike interval. Its onset and offset latencies and their trial-to-trial variability are proportional to the CS-US interval. The coefficient of variation (CoV = σ/μ) are comparable to the CoVs for the conditional eye blink. The average trial-to-trial correlation between the onset latencies and the offset latencies is close to 0, implying that the onsets and offsets are mediated by two stochastically independent readings of the engram. The onset of the pause is step-like; there is no decline in firing rate between the onset of the CS and the onset of the pause. A single presynaptic spike volley suffices to trigger the reading of the engram; and the pause parameters are unaffected by subsequent volleys. The Fano factors for trial-to-trial variations in the distribution of interspike intervals within the intertrial intervals indicate pronounced non-stationarity in the endogenous spontaneous spiking rate, on which the CS-triggered firing pause supervenes. These properties of the spontaneous firing and of the engram read out may prove useful in finding the cell-intrinsic, molecular-level structure that encodes the CS-US interval.

Modelling the simultaneous encoding/serial experience theory of the perceptual moment: a blink of meta-experience

One way to understand a system is to explore how its behaviour degrades when it is overloaded. This approach can be applied to understanding conscious perception by presenting stimuli in rapid succession in the 'same' perceptual event/moment. In previous work, we have identified a striking dissociation during the perceptual moment, between what is encoded into working memory [Lag-1 sparing in the attentional blink (AB)] and what is consciously perceived (Lag-1 impairing in the experiential blink). This paper links this dissociation to what, taking inspiration from the metacognition literature, could be called meta-experience; i.e. how the ability to track and comment on one's visual experience with subjectivity ratings reflects objective performance. Specifically, we provide evidence that the information (in bits) associated with an encoding into working memory decouples from the experiential reflection upon that perceptual/encoding event and that this decoupling is largest when there is the greatest perceptual overload. This is the meta-experiential blink. Meta-experiential self-observation is common to many computational models, including connectionist interpretations of consciousness, Bayesian observers and the readout-enhanced simultaneous type/serial token (reSTST) model. We assess how our meta-experiential blink data could be modelled using the concept of self-observation, providing model fits to behavioural and electroencephalogram responses in the reSTST model. We discuss the implications of our computational modelling of parallel encoding but serial experience for theories of conscious perception. Specifically, we (i) inform theories of Lag-1 sparing during the AB and (ii) consider the implications for the global workspace theory of conscious perception and higher-order theories of consciousness.

Temporal Patterns of Spontaneous Fixational Eye Movements: The Influence of Basal Ganglia

BACKGROUND

Spontaneity is a unique feature of the nervous system. One of the fundamentally critical and recognized forms of spontaneous motor activity is witnessed in the visuomotor system. Microsaccades, the miniature spontaneous eye movements, are critical for the visual perception. We hypothesized that microsaccades follow specific temporal patterns that are modulated by the basal ganglia output.

METHODS

We used high-resolution video-oculography to capture microsaccades in 48 subjects (31 healthy and 17 with Parkinson's disease) when subjects were asked to hold their gaze on a straight-ahead target projected on white background. We analyzed spontaneous discharge patterns of microsaccades.

RESULTS

The first analysis considering coefficient of variation in intersaccadic interval distribution demonstrated that microsaccades in Parkinson's disease are more dispersed than the control group. The second analysis scrutinized microsaccades' temporal variability and revealed 3 distinct occurrence patterns: regular rhythmic, clustered, and randomly occurring following a Poisson-like process. The regular pattern was relatively more common in Parkinson's disease. Subthalamic DBS modulated this temporal pattern. The amount of change in the temporal variability depended on the DBS-induced volume of tissue activation and its overlap with the subthalamic nucleus. The third analysis determined the autocorrelations of microsaccades within 2-second time windows. We found that Parkinson's disease altered local temporal organization in microsaccade generation, and DBS had a modulatory effect.

CONCLUSION

The microsaccades occur in 3 temporal patterns. The basal ganglia are one of the modulators of the microsaccade spontaneity.

Single-cell and ensemble activity of lumbar intermediate and ventral horn interneurons in the spinal air-stepping cat

We explored the relationship between population interneuronal network activation and motor output in the adult, in vivo, air-stepping, spinal cat. By simultaneously measuring the activity of large numbers of spinal interneurons, we explored ensembles of coherently firing interneurons and their relation to motor output. In addition, the networks were analyzed in relation to their spatial distribution along the lumbar enlargement for evidence of localized groups driving particular phases of the locomotor step cycle. We simultaneously recorded hindlimb EMG activity during stepping and extracellular signals from 128 channels across two polytrodes inserted within lamina V-VII of two separate lumbar segments. Results indicated that spinal interneurons participate in one of two ensembles that are highly correlated with the flexor or the extensor muscle bursts during stepping. Interestingly, less than half of the isolated single units were significantly unimodally tuned during the step cycle whereas >97% of the single units of the ensembles were significantly correlated with muscle activity. These results show the importance of population scale analysis in neural studies of behavior as there is a much greater correlation between muscle activity and ensemble firing than between muscle activity and individual neurons. Finally, we show that there is no correlation between interneurons' rostrocaudal locations within the lumbar enlargement and their preferred phase of firing or ensemble participation. These findings indicate that spinal interneurons of lamina V-VII encoding for different phases of the locomotor cycle are spread throughout the lumbar enlargement in the adult spinal cord.NEW & NOTEWORTHY We report on the ensemble organization of interneuronal activity in the spinal cord during locomotor movements and show that lumbar intermediate zone interneurons organize in two groups related to the two major phases of walking: stance and swing. Ensemble organization is also shown to better correlate with muscular output than single-cell activity, although ensemble membership does not appear to be somatotopically organized within the spinal cord.

A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging

Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly released a resource toolbox and provide a user-friendly cloud-based implementation.

In vivo flow cytometry reveals a circadian rhythm of circulating tumor cells

Circulating tumor cells (CTCs) is an established biomarker of cancer metastasis. The circulation dynamics of CTCs are important for understanding the mechanisms underlying tumor cell dissemination. Although studies have revealed that the circadian rhythm may disrupt the growth of tumors, it is generally unclear whether the circadian rhythm controls the release of CTCs. In clinical examinations, the current in vitro methods for detecting CTCs in blood samples are based on a fundamental assumption that CTC counts in the peripheral blood do not change significantly over time, which is being challenged by recent studies. Since it is not practical to draw blood from patients repeatedly, a feasible strategy to investigate the circadian rhythm of CTCs is to monitor them by in vivo detection methods. Fluorescence in vivo flow cytometry (IVFC) is a powerful optical technique that is able to detect fluorescent circulating cells directly in living animals in a noninvasive manner over a long period of time. In this study, we applied fluorescence IVFC to monitor CTCs noninvasively in an orthotopic mouse model of human prostate cancer. We observed that CTCs exhibited stochastic bursts over cancer progression. The probability of the bursting activity was higher at early stages than at late stages. We longitudinally monitored CTCs over a 24-h period, and our results revealed striking daily oscillations in CTC counts that peaked at the onset of the night (active phase for rodents), suggesting that the release of CTCs might be regulated by the circadian rhythm.

Task-dependent mixed selectivity in the subiculum

CA1 and subiculum (SUB) connect the hippocampus to numerous output regions. Cells in both areas have place-specific firing fields, although they are more dispersed in SUB. Weak responses to head direction and running speed have been reported in both regions. However, how such information is encoded in CA1 and SUB and the resulting impact on downstream targets are poorly understood. Here, we estimate the tuning of simultaneously recorded CA1 and SUB cells to position, head direction, and speed. Individual neurons respond conjunctively to these covariates in both regions, but the degree of mixed representation is stronger in SUB, and more so during goal-directed spatial navigation than free foraging. Each navigational variable could be decoded with higher precision, from a similar number of neurons, in SUB than CA1. The findings point to a possible contribution of mixed-selective coding in SUB to efficient transmission of hippocampal representations to widespread brain regions.

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CA1 and subiculum neurons respond conjunctively to position, head direction, and speed

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The degree of conjunctive coding (“mixed selectivity”) is stronger in the subiculum

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Mixed selectivity is stronger during goal-directed navigation than in free foraging

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Decoding of each navigational covariate is more accurate with mixed selectivity

Single bacterial resolvases first exploit, then constrain intrinsic dynamics of the Holliday junction to direct recombination

Homologous recombination forms and resolves an entangled DNA Holliday Junction (HJ) crucial for achieving genetic reshuffling and genome repair. To maintain genomic integrity, specialized resolvase enzymes cleave the entangled DNA into two discrete DNA molecules. However, it is unclear how two similar stacking isomers are distinguished, and how a cognate sequence is found and recognized to achieve accurate recombination. We here use single-molecule fluorescence observation and cluster analysis to examine how prototypic bacterial resolvase RuvC singles out two of the four HJ strands and achieves sequence-specific cleavage. We find that RuvC first exploits, then constrains the dynamics of intrinsic HJ isomer exchange at a sampled branch position to direct cleavage toward the catalytically competent HJ conformation and sequence, thus controlling recombination output at minimal energetic cost. Our model of rapid DNA scanning followed by ‘snap-locking’ of a cognate sequence is strikingly consistent with the conformational proofreading of other DNA-modifying enzymes.

Activity labeling in vivo using CaMPARI2 reveals intrinsic and synaptic differences between neurons with high and low firing rate set points

Neocortical pyramidal neurons regulate firing around a stable mean firing rate (FR) that can differ by orders of magnitude between neurons, but the factors that determine where individual neurons sit within this broad FR distribution are not understood. To access low- and high-FR neurons for ex vivo analysis, we used Ca2+- and UV-dependent photoconversion of CaMPARI2 in vivo to permanently label neurons according to mean FR. CaMPARI2 photoconversion was correlated with immediate early gene expression and higher FRs ex vivo and tracked the drop and rebound in ensemble mean FR induced by prolonged monocular deprivation. High-activity L4 pyramidal neurons had greater intrinsic excitability and recurrent excitatory synaptic strength, while E/I ratio, local output strength, and local connection probability were not different. Thus, in L4 pyramidal neurons (considered a single transcriptional cell type), a broad mean FR distribution is achieved through graded differences in both intrinsic and synaptic properties.

Focal Sleep Spindle Deficits Reveal Focal Thalamocortical Dysfunction and Predict Cognitive Deficits in Sleep Activated Developmental Epilepsy

Childhood epilepsy with centrotemporal spikes (CECTS) is the most common focal epilepsy syndrome, yet the cause of this disease remains unknown. Now recognized as a mild epileptic encephalopathy, children exhibit sleep-activated focal epileptiform discharges and cognitive difficulties during the active phase of the disease. The association between the abnormal electrophysiology and sleep suggests disruption to thalamocortical circuits. Thalamocortical circuit dysfunction resulting in pathologic epileptiform activity could hinder the production of sleep spindles, a brain rhythm essential for memory processes. Despite this pathophysiologic connection, the relationship between spindles and cognitive symptoms in epileptic encephalopathies has not been previously evaluated. A significant challenge limiting such work has been the poor performance of available automated spindle detection methods in the setting of sharp activities, such as epileptic spikes. Here, we validate a robust new method to accurately measure sleep spindles in patients with epilepsy. We then apply this detector to a prospective cohort of male and female children with CECTS with combined high-density EEGs during sleep and cognitive testing at varying time points of disease. We show that: (1) children have a transient, focal deficit in spindles during the symptomatic phase of disease; (2) spindle rate anticorrelates with spike rate; and (3) spindle rate, but not spike rate, predicts performance on cognitive tasks. These findings demonstrate focal thalamocortical circuit dysfunction and provide a pathophysiological explanation for the shared seizures and cognitive symptoms in CECTS. Further, this work identifies sleep spindles as a potential treatment target of cognitive dysfunction in this common epileptic encephalopathy.SIGNIFICANCE STATEMENT Childhood epilepsy with centrotemporal spikes is the most common idiopathic focal epilepsy syndrome, characterized by self-limited focal seizures and cognitive symptoms. Here, we provide the first evidence that focal thalamocortical circuit dysfunction underlies the shared seizures and cognitive dysfunction observed. In doing so, we identify sleep spindles as a mechanistic biomarker, and potential treatment target, of cognitive dysfunction in this common developmental epilepsy and provide a novel method to reliably quantify spindles in brain recordings from patients with epilepsy.

Circadian and multiday seizure periodicities, and seizure clusters in canine epilepsy

Advances in ambulatory intracranial EEG devices have enabled objective analyses of circadian and multiday seizure periodicities, and seizure clusters in humans. This study characterizes circadian and multiday seizure periodicities, and seizure clusters in dogs with naturally occurring focal epilepsy, and considers the implications of an animal model for the study of seizure risk patterns, seizure forecasting and personalized treatment protocols. In this retrospective cohort study, 16 dogs were continuously monitored with ambulatory intracranial EEG devices designed for humans. Detailed medication records were kept for all dogs. Seizure periodicity was evaluated with circular statistics methods. Circular non-uniformity was assessed for circadian, 7-day and approximately monthly periods. The Rayleigh test was used to assess statistical significance, with correction for multiple comparisons. Seizure clusters were evaluated with Fano factor (index of dispersion) measurements, and compared to a Poisson distribution. Relationships between interseizure interval (ISI) and seizure duration were evaluated. Six dogs met the inclusion criteria of having at least 30 seizures and were monitored for an average of 65 weeks. Three dogs had seizures with circadian seizure periodicity, one dog had a 7-day periodicity, and two dogs had approximately monthly periodicity. Four dogs had seizure clusters and significantly elevated Fano factor values. There were subject-specific differences in the dynamics of ISI and seizure durations, both within and between lead and clustered seizure categories. Our findings show that seizure timing in dogs with naturally occurring epilepsy is not random, and that circadian and multiday seizure periodicities, and seizure clusters are common. Circadian, 7-day, and monthly seizure periodicities occur independent of antiseizure medication dosing, and these patterns likely reflect endogenous rhythms of seizure risk.

Neural properties of fundamental function encoding of sound selectivity in the female avian auditory cortex

Zebra finches (Taeniopygia guttata) use their voices for communication. Song structures in the songs of individual males are important for sound recognition in females. The caudomedial mesopallium (CMM) and nidopallium (NCM) are known to be essential higher auditory regions for sound recognition. These two regions have also been discussed with respect to their fundamental functions and song selectivity. To clarify their functions and selectivity, we investigated latencies and spiking patterns and also developed a novel correlation analysis to evaluate the relationship between neural activity and the characteristics of acoustic factors. We found that the latencies and spiking patterns in response to song stimuli differed between the CMM and NCM. In addition, our correlation analysis revealed that amplitude and frequency structures were important temporal acoustic factors for both regions. Although the CMM and NCM have different fundamental functions, they share similar encoding systems for acoustic factors.

Overexpression of cypin alters dendrite morphology, single neuron activity, and network properties via distinct mechanisms

OBJECTIVE

This study investigates the effect that overexpression of cytosolic PSD-95 interactor (cypin), a regulator of synaptic PSD-95 protein localization and a core regulator of dendrite branching, exerts on the electrical activity of rat hippocampal neurons and networks.

APPROACH

We cultured rat hippocampal neurons and used lipid-mediated transfection and lentiviral gene transfer to achieve high levels of cypin or cypin mutant (cypinΔPDZ; PSD-95 non-binding) expression cellularly and network-wide, respectively.

MAIN RESULTS

Our analysis revealed that although overexpression of cypin and cypinΔPDZ increase dendrite numbers and decrease spine density, cypin and cypinΔPDZ distinctly regulate neuronal activity. At the single cell level, cypin promotes decreases in bursting activity while cypinΔPDZ reduces sEPSC frequency and further decreases bursting compared to cypin. At the network level, by using the Fano factor as a measure of spike count variability, cypin overexpression results in an increase in variability of spike count, and this effect is abolished when cypin cannot bind PSD-95. This variability is also dependent on baseline activity levels and on mean spike rate over time. Finally, our spike sorting data show that overexpression of cypin results in a more complex distribution of spike waveforms and that binding to PSD-95 is essential for this complexity.

SIGNIFICANCE

Our data suggest that dendrite morphology does not play a major role in cypin action on electrical activity.

Beyond spikes: Multiscale computational analysis of in vivo long-term recordings in the cockroach circadian clock

The circadian clock of the nocturnal Madeira cockroach is located in the accessory medulla, a small nonretinotopic neuropil in the brain's visual system. The clock comprises about 240 neurons that control rhythms in physiology and behavior such as sleep-wake cycles. The clock neurons contain an abundant number of partly colocalized neuropeptides, among them pigment-dispersing factor (PDF), the insects' most important circadian coupling signal that controls sleep-wake rhythms. We performed long-term loose-patch clamp recordings under 12:12-hr light-dark cycles in the cockroach clock in vivo. A wide range of timescales, from milliseconds to seconds, were found in spike and field potential patterns. We developed a framework of wavelet transform-based methods to detect these multiscale electrical events. We analyzed frequencies and patterns of events with interesting dynamic features, such as mixed-mode oscillations reminiscent of sharp-wave ripples. Oscillations in the beta/gamma frequency range (20-40 Hz) were observed to rise at dawn, when PDF is released, peaking just before the onset of locomotor activity of the nocturnal cockroach. We expect that in vivo electrophysiological recordings combined with neuropeptide/antagonist applications and behavioral analysis will determine whether specific patterns of electrical activity recorded in the network of the cockroach circadian clock are causally related to neuropeptide-dependent control of behavior.

Prolonged post-stimulation response induced by 980-nm infrared neural stimulation in the rat primary motor cortex

The post-stimulation response of neural activities plays an important role to evaluate the effectiveness and safety of neural modulation techniques. Previous studies have established the capability of infrared neural modulation (INM) on neural firing regulation in the central nervous system (CNS); however, the dynamic neural activity after the laser offset has not been well characterized yet. We applied 980-nm infrared diode laser light to irradiate the primary motor cortex of rats, and tungsten electrode was inserted to record the single-unit activity of neurons at the depth of 800-1000 μm (layer V of primary motor cortex). The neural activities were assessed through the change of neural firing rate and firing pattern pre- and post-stimulation with various radiant exposures. The results showed that the 980-nm laser could modulate the firing properties of neurons in the deep layer of the cortex. More neurons with post-stimulation response (78% vs. 83%) were observed at higher stimulation intensity (0.803 J/cm² vs. 1.071 J/cm², respectively). The change of firing rate also increased with radiant exposures increasing, and the response lasted up to 4.5 s at 1.071 J/cm², which was significantly longer than the theoretical thermal relaxation time. Moreover, the increasing Fano factors indicated the irregularity firing pattern of post-stimulation response. Our results confirmed that neural activity maintained a prolonged post-stimulation response after INM, which may provide necessary measurable data for optimization of INM applications in CNS.

Probing RNA structure and interaction dynamics at the single molecule level

RNA structures and their dynamic fluctuations lie at the heart of understanding key biological process such as transcription, splicing, translation and RNA decay. While conventional bulk assays have proven to identify and characterize key pathway intermediates, the generally dynamic nature of RNA structures renders the information obtained from time and ensemble averaging techniques necessarily lacking in critical details. Here we detail Single-Molecule Kinetic Analysis of RNA Transient Structure (SiM-KARTS), a method that readily monitors structural fluctuations of single RNA molecules through the repetitive interaction of fluorescent probes with an unlabeled, surface-immobilized RNA target of virtually any length and in any biological context. In addition, we demonstrate the broad applicability of SiM-KARTS by kinetically fingerprinting the binding of cognate tRNA ligand to single immobilized T-box riboswitch molecules. SiM-KARTS represents a valuable tool for probing biologically relevant structure and interaction features of potentially many diverse RNA metabolic pathways.

Differential Entropy Preserves Variational Information of Near-Infrared Spectroscopy Time Series Associated With Working Memory

Neuroscience research shows a growing interest in the application of Near-Infrared Spectroscopy (NIRS) in analysis and decoding of the brain activity of human subjects. Given the correlation that is observed between the Blood Oxygen Dependent Level (BOLD) responses that are exhibited by the time series data of functional Magnetic Resonance Imaging (fMRI) and the hemoglobin oxy/deoxy-genation that is captured by NIRS, linear models play a central role in these applications. This, in turn, results in adaptation of the feature extraction strategies that are well-suited for discretization of data that exhibit a high degree of linearity, namely, slope and the mean as well as their combination, to summarize the informational contents of the NIRS time series. In this article, we demonstrate that these features are inefficient in capturing the variational information of NIRS data, limiting the reliability and the adequacy of the conclusion on their results. Alternatively, we propose the linear estimate of differential entropy of these time series as a natural representation of such information. We provide evidence for our claim through comparative analysis of the application of these features on NIRS data pertinent to several working memory tasks as well as naturalistic conversational stimuli.

Probing Electrophysiological Indices of Perceptual Awareness across Unisensory and Multisensory Modalities

The neural underpinnings of perceptual awareness have been extensively studied using unisensory (e.g., visual alone) stimuli. However, perception is generally multisensory, and it is unclear whether the neural architecture uncovered in these studies directly translates to the multisensory domain. Here, we use EEG to examine brain responses associated with the processing of visual, auditory, and audiovisual stimuli presented near threshold levels of detectability, with the aim of deciphering similarities and differences in the neural signals indexing the transition into perceptual awareness across vision, audition, and combined visual-auditory (multisensory) processing. More specifically, we examine (1) the presence of late evoked potentials (∼>300 msec), (2) the across-trial reproducibility, and (3) the evoked complexity associated with perceived versus nonperceived stimuli. Results reveal that, although perceived stimuli are associated with the presence of late evoked potentials across each of the examined sensory modalities, between-trial variability and EEG complexity differed for unisensory versus multisensory conditions. Whereas across-trial variability and complexity differed for perceived versus nonperceived stimuli in the visual and auditory conditions, this was not the case for the multisensory condition. Taken together, these results suggest that there are fundamental differences in the neural correlates of perceptual awareness for unisensory versus multisensory stimuli. Specifically, the work argues that the presence of late evoked potentials, as opposed to neural reproducibility or complexity, most closely tracks perceptual awareness regardless of the nature of the sensory stimulus. In addition, the current findings suggest a greater similarity between the neural correlates of perceptual awareness of unisensory (visual and auditory) stimuli when compared with multisensory stimuli.

A novel approach to probabilistic characterisation of neural firing patterns

BACKGROUND

The advances in extracellular neural recording techniques result in big data volumes that necessitate fast, reliable, and automatic identification of statistically similar units. This study proposes a single framework yielding a compact set of probabilistic descriptors that characterise the firing patterns of a single unit.

NEW METHOD

Probabilistic features are estimated from an inter-spike-interval time series, without assumptions about the firing distribution or the stationarity. The first level of proposed firing patterns decomposition divides the inter-spike intervals into bursting, moderate and idle firing modes, yielding a coarse feature set. The second level identifies the successive bursting spikes, or the spiking acceleration/deceleration in the moderate firing mode, yielding a refined feature set. The features are estimated from simulated data and from experimental recordings from the lateral prefrontal cortex in awake, behaving rhesus monkeys.

RESULTS

An efficient and stable partitioning of neural units is provided by the ensemble evidence accumulation clustering. The possibility of selecting the number of clusters and choosing among coarse and refined feature sets provides an opportunity to explore and compare different data partitions.

CONCLUSIONS

The estimation of features, if applied to a single unit, can serve as a tool for the firing analysis, observing either overall spiking activity or the periods of interest in trial-to-trial recordings. If applied to massively parallel recordings, it additionally serves as an input to the clustering procedure, with the potential to compare the functional properties of various brain structures and to link the types of neural cells to the particular behavioural states.

Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability

Neurons in many brain areas exhibit high trial-to-trial variability, with spike counts that are overdispersed relative to a Poisson distribution. Recent work (Goris, Movshon, & Simoncelli, 2014 ) has proposed to explain this variability in terms of a multiplicative interaction between a stochastic gain variable and a stimulus-dependent Poisson firing rate, which produces quadratic relationships between spike count mean and variance. Here we examine this quadratic assumption and propose a more flexible family of models that can account for a more diverse set of mean-variance relationships. Our model contains additive gaussian noise that is transformed nonlinearly to produce a Poisson spike rate. Different choices of the nonlinear function can give rise to qualitatively different mean-variance relationships, ranging from sublinear to linear to quadratic. Intriguingly, a rectified squaring nonlinearity produces a linear mean-variance function, corresponding to responses with a constant Fano factor. We describe a computationally efficient method for fitting this model to data and demonstrate that a majority of neurons in a V1 population are better described by a model with a nonquadratic relationship between mean and variance. Finally, we demonstrate a practical use of our model via an application to Bayesian adaptive stimulus selection in closed-loop neurophysiology experiments, which shows that accounting for overdispersion can lead to dramatic improvements in adaptive tuning curve estimation.

Periodicity in the autocorrelation function as a mechanism for regularly occurring zero crossings or extreme values of a Gaussian process

The problem of zero crossings is of great historical prevalence and promises extensive application. The challenge is to establish precisely how the autocorrelation function or power spectrum of a one-dimensional continuous random process determines the density function of the intervals between the zero crossings of that process. This paper investigates the case where periodicities are incorporated into the autocorrelation function of a smooth process. Numerical simulations, and statistics about the number of crossings in a fixed interval, reveal that in this case the zero crossings segue between a random and deterministic point process depending on the relative time scales of the periodic and nonperiodic components of the autocorrelation function. By considering the Laplace transform of the density function, we show that incorporating correlation between successive intervals is essential to obtaining accurate results for the interval variance. The same method enables prediction of the density function tail in some regions, and we suggest approaches for extending this to cover all regions. In an ever-more complex world, the potential applications for this scale of regularity in a random process are far reaching and powerful.

Adaptation of Thalamic Neurons Provides Information about the Spatiotemporal Context of Stimulus History

Adaptation of neural responses due to the history of sensory input has been observed across all sensory modalities. However, the computational role of adaptation is not fully understood, especially when one considers neural coding problems in which adaptation increases the ambiguity of the neural responses to simple stimuli. To address this, we quantified the impact of adaptation on the information conveyed by thalamic neurons about paired whisker stimuli in male rat. At the single neuron level, although paired-pulse adaptation reduces the information about the present stimulus, the information per spike increases. Moreover, the adapted response can convey significant amounts of information about whether, when and where a previous stimulus occurred. At the population level, ambiguity of the adapted responses about the present stimulus can be compensated for by large numbers of neurons. Therefore, paired-pulse adaptation does not reduce the discriminability of simple stimuli. It provides information about the spatiotemporal context of stimulus history.SIGNIFICANCE STATEMENT The present work provides a computational framework that demonstrates how adaptation allows neurons to encode spatiotemporal dynamics of stimulus history.

The index of dispersion as a metric of quanta - unravelling the Fano factor

In statistics, the index of dispersion (or variance-to-mean ratio) is unity (σ²/〈x〉 = 1) for a Poisson-distributed process with variance σ² for a variable x that manifests as unit increments. Where x is a measure of some phenomenon, the index takes on a value proportional to the quanta that constitute the phenomenon. That outcome might thus be anticipated to apply for an enormously wide variety of applied measurements of quantum phenomena. However, in a photon-energy proportional radiation detector, a set of M witnessed Poisson-distributed measurements {W₁, W₂,… W_M} scaled so that the ideal expectation value of the quantum is unity, is generally observed to give σ²/〈W〉 < 1 because of detector losses as broadly indicated by Fano [Phys. Rev. (1947), 72, 26]. In other cases where there is spectral dispersion, σ²/〈W〉 > 1. Here these situations are examined analytically, in Monte Carlo simulations, and experimentally. The efforts reveal a powerful metric of quanta broadly associated with such measurements, where the extension has been made to polychromatic and lossy situations. In doing so, the index of dispersion's variously established yet curiously overlooked role as a metric of underlying quanta is indicated. The work's X-ray aspects have very diverse utility and have begun to find applications in radiography and tomography, where the ability to extract spectral information from conventional intensity detectors enables a superior level of material and source characterization.

Atypical transistor-based chaotic oscillators: Design, realization, and diversity

In this paper, we show that novel autonomous chaotic oscillators based on one or two bipolar junction transistors and a limited number of passive components can be obtained via random search with suitable heuristics. Chaos is a pervasive occurrence in these circuits, particularly after manual adjustment of a variable resistor placed in series with the supply voltage source. Following this approach, 49 unique circuits generating chaotic signals when physically realized were designed, representing the largest collection of circuits of this kind to date. These circuits are atypical as they do not trivially map onto known topologies or variations thereof. They feature diverse spectra and predominantly anti-persistent monofractal dynamics. Notably, we recurrently found a circuit comprising one resistor, one transistor, two inductors, and one capacitor, which generates a range of attractors depending on the parameter values. We also found a circuit yielding an irregular quantized spike-train resembling some aspects of neural discharge and another one generating a double-scroll attractor, which represent the smallest known transistor-based embodiments of these behaviors. Through three representative examples, we additionally show that diffusive coupling of heterogeneous oscillators of this kind may give rise to complex entrainment, such as lag synchronization with directed information transfer and generalized synchronization. The replicability and reproducibility of the experimental findings are good.

Cell diversity and network dynamics in photosensitive human brain organoids

In vitro models of the developing brain such as three-dimensional brain organoids offer an unprecedented opportunity to study aspects of human brain development and disease. However, the cells generated within organoids and the extent to which they recapitulate the regional complexity, cellular diversity and circuit functionality of the brain remain undefined. Here we analyse gene expression in over 80,000 individual cells isolated from 31 human brain organoids. We find that organoids can generate a broad diversity of cells, which are related to endogenous classes, including cells from the cerebral cortex and the retina. Organoids could be developed over extended periods (more than 9 months), allowing for the establishment of relatively mature features, including the formation of dendritic spines and spontaneously active neuronal networks. Finally, neuronal activity within organoids could be controlled using light stimulation of photosensitive cells, which may offer a way to probe the functionality of human neuronal circuits using physiological sensory stimuli.

Flexible models for spike count data with both over- and under- dispersion

A key observation in systems neuroscience is that neural responses vary, even in controlled settings where stimuli are held constant. Many statistical models assume that trial-to-trial spike count variability is Poisson, but there is considerable evidence that neurons can be substantially more or less variable than Poisson depending on the stimuli, attentional state, and brain area. Here we examine a set of spike count models based on the Conway-Maxwell-Poisson (COM-Poisson) distribution that can flexibly account for both over- and under-dispersion in spike count data. We illustrate applications of this noise model for Bayesian estimation of tuning curves and peri-stimulus time histograms. We find that COM-Poisson models with group/observation-level dispersion, where spike count variability is a function of time or stimulus, produce more accurate descriptions of spike counts compared to Poisson models as well as negative-binomial models often used as alternatives. Since dispersion is one determinant of parameter standard errors, COM-Poisson models are also likely to yield more accurate model comparison. More generally, these methods provide a useful, model-based framework for inferring both the mean and variability of neural responses.

The Shine-Dalgarno sequence of riboswitch-regulated single mRNAs shows ligand-dependent accessibility bursts

In response to intracellular signals in Gram-negative bacteria, translational riboswitches—commonly embedded in messenger RNAs (mRNAs)—regulate gene expression through inhibition of translation initiation. It is generally thought that this regulation originates from occlusion of the Shine-Dalgarno (SD) sequence upon ligand binding; however, little direct evidence exists. Here we develop Single Molecule Kinetic Analysis of RNA Transient Structure (SiM-KARTS) to investigate the ligand-dependent accessibility of the SD sequence of an mRNA hosting the 7-aminomethyl-7-deazaguanine (preQ₁)-sensing riboswitch. Spike train analysis reveals that individual mRNA molecules alternate between two conformational states, distinguished by ‘bursts' of probe binding associated with increased SD sequence accessibility. Addition of preQ₁ decreases the lifetime of the SD's high-accessibility (bursting) state and prolongs the time between bursts. In addition, ligand-jump experiments reveal imperfect riboswitching of single mRNA molecules. Such complex ligand sensing by individual mRNA molecules rationalizes the nuanced ligand response observed during bulk mRNA translation.

In response to intracellular signals, bacterial translational riboswitches embedded in mRNAs can regulate gene expression through inhibition of translation initiation. Here, the authors describe SiM-KARTS, a novel approach for detecting changes in the structure of single RNA molecules in response to a ligand.

Testing the odds of inherent vs. observed overdispersion in neural spike counts

The repeated presentation of an identical visual stimulus in the receptive field of a neuron may evoke different spiking patterns at each trial. Probabilistic methods are essential to understand the functional role of this variance within the neural activity. In that case, a Poisson process is the most common model of trial-to-trial variability. For a Poisson process, the variance of the spike count is constrained to be equal to the mean, irrespective of the duration of measurements. Numerous studies have shown that this relationship does not generally hold. Specifically, a majority of electrophysiological recordings show an "overdispersion" effect: responses that exhibit more intertrial variability than expected from a Poisson process alone. A model that is particularly well suited to quantify overdispersion is the Negative-Binomial distribution model. This model is well-studied and widely used but has only recently been applied to neuroscience. In this article, we address three main issues. First, we describe how the Negative-Binomial distribution provides a model apt to account for overdispersed spike counts. Second, we quantify the significance of this model for any neurophysiological data by proposing a statistical test, which quantifies the odds that overdispersion could be due to the limited number of repetitions (trials). We apply this test to three neurophysiological data sets along the visual pathway. Finally, we compare the performance of this model to the Poisson model on a population decoding task. We show that the decoding accuracy is improved when accounting for overdispersion, especially under the hypothesis of tuned overdispersion.

Effects of subthalamic deep brain stimulation on blink abnormalities of 6-OHDA lesioned rats

Parkinson's disease (PD) patients and the 6-hydroxydopamine (6-OHDA) lesioned rat model share blink abnormalities. In view of the evolutionarily conserved organization of blinking, characterization of blink reflex circuits in rodents may elucidate the neural mechanisms of PD reflex abnormalities. We examine the extent of this shared pattern of blink abnormalities by measuring blink reflex excitability, blink reflex plasticity, and spontaneous blinking in 6-OHDA lesioned rats. We also investigate whether 130-Hz subthalamic nucleus deep brain stimulation (STN DBS) affects blink abnormalities, as it does in PD patients. Like PD patients, 6-OHDA-lesioned rats exhibit reflex blink hyperexcitability, impaired blink plasticity, and a reduced spontaneous blink rate. At 130 Hz, but not 16 Hz, STN DBS eliminates reflex blink hyperexcitability and restores both short- and long-term blink plasticity. Replicating its lack of effect in PD patients, 130-Hz STN DBS does not reinstate a normal temporal pattern or rate to spontaneous blinking in 6-OHDA lesioned rats. These data show that the 6-OHDA lesioned rat is an ideal model system for investigating the neural bases of reflex abnormalities in PD and highlight the complexity of PD's effects on motor control, by showing that dopamine depletion does not affect all blink systems via the same neural mechanisms.

Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity

A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns—both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.

Fano factor estimation

Fano factor is one of the most widely used measures of variability of spike trains. Its standard estimator is the ratio of sample variance to sample mean of spike counts observed in a time window and the quality of the estimator strongly depends on the length of the window. We investigate this dependence under the assumption that the spike train behaves as an equilibrium renewal process. It is shown what characteristics of the spike train have large effect on the estimator bias. Namely, the effect of refractory period is analytically evaluated. Next, we create an approximate asymptotic formula for the mean square error of the estimator, which can also be used to find minimum of the error in estimation from single spike trains. The accuracy of the Fano factor estimator is compared with the accuracy of the estimator based on the squared coefficient of variation. All the results are illustrated for spike trains with gamma and inverse Gaussian probability distributions of interspike intervals. Finally, we discuss possibilities of how to select a suitable observation window for the Fano factor estimation.

Decoding sound source location and separation using neural population activity patterns

The strategies by which the central nervous system decodes the properties of sensory stimuli, such as sound source location, from the responses of a population of neurons are a matter of debate. We show, using the average firing rates of neurons in the inferior colliculus (IC) of awake rabbits, that prevailing decoding models of sound localization (summed population activity and the population vector) fail to localize sources accurately due to heterogeneity in azimuth tuning across the population. In contrast, a maximum-likelihood decoder operating on the pattern of activity across the population of neurons in one IC accurately localized sound sources in the contralateral hemifield, consistent with lesion studies, and did so with a precision consistent with rabbit psychophysical performance. The pattern decoder also predicts behavior in response to incongruent localization cues consistent with the long-standing "duplex" theory of sound localization. We further show that the pattern decoder accurately distinguishes two concurrent, spatially separated sources from a single source, consistent with human behavior. Decoder detection of small amounts of source separation directly in front is due to neural sensitivity to the interaural decorrelation of sound, at both low and high frequencies. The distinct patterns of IC activity between single and separated sound sources thereby provide a neural correlate for the ability to segregate and localize sources in everyday, multisource environments.

Convergent approaches toward the study of multisensory perception

Classical analytical approaches for examining multisensory processing in individual neurons have relied heavily on changes in mean firing rate to assess the presence and magnitude of multisensory interaction. However, neurophysiological studies within individual sensory systems have illustrated that important sensory and perceptual information is encoded in forms that go beyond these traditional spike-based measures. Here we review analytical tools as they are used within individual sensory systems (auditory, somatosensory, and visual) to advance our understanding of how sensory cues are effectively integrated across modalities (e.g., audiovisual cues facilitating speech processing). Specifically, we discuss how methods used to assess response variability (Fano factor, or FF), local field potentials (LFPs), current source density (CSD), oscillatory coherence, spike synchrony, and receiver operating characteristics (ROC) represent particularly promising tools for understanding the neural encoding of multisensory stimulus features. The utility of each approach and how it might optimally be applied toward understanding multisensory processing is placed within the context of exciting new data that is just beginning to be generated. Finally, we address how underlying encoding mechanisms might shape-and be tested alongside with-the known behavioral and perceptual benefits that accompany multisensory processing.

Orbitofrontal cortical activity during repeated free choice

Neurons in the orbitofrontal cortex (OFC) have been shown to encode subjective values, suggesting a role in preference-based decision-making, although the precise relation to choice behavior is unclear. In a repeated two-choice task, subjective values of each choice can account for aggregate choice behavior, which is the overall likelihood of choosing one option over the other. Individual choices, however, are impossible to predict with knowledge of relative subjective values alone. In this study we investigated the role of internal factors in choice behavior with a simple but novel free-choice task and simultaneous recording from individual neurons in nonhuman primate OFC. We found that, first, the observed sequences of choice behavior included periods of exceptionally long runs of each of two available options and periods of frequent switching. Neither a satiety-based mechanism nor a random selection process could explain the observed choice behavior. Second, OFC neurons encode important features of the choice behavior. These features include activity selective for exceptionally long runs of a given choice (stay selectivity) as well as activity selective for switches between choices (switch selectivity). These results suggest that OFC neural activity, in addition to encoding subjective values on a long timescale that is sensitive to satiety, also encodes a signal that fluctuates on a shorter timescale and thereby reflects some of the statistically improbable aspects of free-choice behavior.