ElecFeX is a user-friendly toolbox for efficient feature extraction from single-cell electrophysiological recordings.

Characterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Technological developments have enabled the collection of hundreds of neural recordings; this calls for new tools capable of performing feature extraction efficiently. To address the urgent need for a powerful and accessible tool, we developed ElecFeX, an open-source MATLAB-based toolbox that (1) has an intuitive graphical user interface, (2) provides customizable measurements for a wide range of electrophysiological features, (3) processes large-size datasets effortlessly via batch analysis, and (4) yields formatted output for further analysis. We implemented ElecFeX on a diverse set of neural recordings; demonstrated its functionality, versatility, and efficiency in capturing electrical features; and established its significance in distinguishing neuronal subgroups across brain regions and species. ElecFeX is thus presented as a user-friendly toolbox to benefit the neuroscience community by minimizing the time required for extracting features from their electrophysiological datasets.

Visual attention to features and space in mice using reverse correlation.

Visual attention allows the brain to evoke behaviors based on the most important visual features. Mouse models offer immense potential to gain a circuit-level understanding of this phenomenon, yet how mice distribute attention across features and locations is not well understood. Here, we describe a new approach to address this limitation by training mice to detect weak vertical bars in a background of dynamic noise while spatial cues manipulate their attention. By adapting a reverse-correlation method from human studies, we linked behavioral decisions to stimulus features and locations. We show that mice deployed attention to a small rostral region of the visual field. Within this region, mice attended to multiple features (orientation, spatial frequency, contrast) that indicated the presence of weak vertical bars. This attentional tuning grew with training, multiplicatively scaled behavioral sensitivity, approached that of an ideal observer, and resembled the effects of attention in humans. Taken together, we demonstrate that mice can simultaneously attend to multiple features and locations of a visual stimulus.

Impaired dendritic spike generation in the Fragile X prefrontal cortex is due to loss of dendritic sodium channels.

Patients with Fragile X syndrome, the leading monogenetic cause of autism, suffer from impairments related to the prefrontal cortex, including working memory and attention. Synaptic inputs to the distal dendrites of layer 5 pyramidal neurons in the prefrontal cortex have a weak influence on the somatic membrane potential. To overcome this filtering, distal inputs are transformed into local dendritic Na+ spikes, which propagate to the soma and trigger action potential output. Layer 5 extratelencephalic (ET) prefrontal cortex (PFC) neurons project to the brainstem and various thalamic nuclei and are therefore well positioned to integrate task-relevant sensory signals and guide motor actions. We used current clamp and outside-out patch clamp recording to investigate dendritic spike generation in ET neurons from male wild-type and Fmr1 knockout (FX) mice. The threshold for dendritic spikes was more depolarized in FX neurons compared to wild-type. Analysis of voltage responses to simulated in vivo 'noisy' current injections showed that a larger dendritic input stimulus was required to elicit dendritic spikes in FX ET dendrites compared to wild-type. Patch clamp recordings revealed that the dendritic Na+ conductance was significantly smaller in FX ET dendrites. Taken together, our results suggest that the generation of Na+ -dependent dendritic spikes is impaired in ET neurons of the PFC in FX mice. Considering our prior findings that somatic D-type K+ and dendritic hyperpolarization-activated cyclic nucleotide-gated-channel function is reduced in ET neurons, we suggest that dendritic integration by PFC circuits is fundamentally altered in Fragile X syndrome. KEY POINTS: Dendritic spike threshold is depolarized in layer 5 prefrontal cortex neurons in Fmr1 knockout (FX) mice. Simultaneous somatic and dendritic recording with white noise current injections revealed that larger dendritic stimuli were required to elicit dendritic spikes in FX extratelencephalic (ET) neurons. Outside-out patch clamp recording revealed that dendritic sodium conductance density was lower in FX ET neurons.

The Magnitude, But Not the Sign, of MT Single-Trial Spike-Time Correlations Predicts Motion Detection Performance.

Spike-time correlations capture the short timescale covariance between the activity of neurons on a single trial. These correlations can significantly vary in magnitude and sign from trial to trial, and have been proposed to contribute to information encoding in visual cortex. While monkeys performed a motion-pulse detection task, we examined the behavioral impact of both the magnitude and sign of single-trial spike-time correlations between two nonoverlapping pools of middle temporal (MT) neurons. We applied three single-trial measures of spike-time correlation between our multiunit MT spike trains (Pearson's, absolute value of Pearson's, and mutual information), and examined the degree to which they predicted a subject's performance on a trial-by-trial basis. We found that on each trial, positive and negative spike-time correlations were almost equally likely, and, once the correlational sign was accounted for, all three measures were similarly predictive of behavior. Importantly, just before the behaviorally relevant motion pulse occurred, single-trial spike-time correlations were as predictive of the performance of the animal as single-trial firing rates. While firing rates were positively associated with behavioral outcomes, the presence of either strong positive or negative correlations had a detrimental effect on behavior. These correlations occurred on short timescales, and the strongest positive and negative correlations modulated behavioral performance by ∼9%, compared with trials with no correlations. We suggest a model where spike-time correlations are associated with a common noise source for the two MT pools, which in turn decreases the signal-to-noise ratio of the integrated signals that drive motion detection.SIGNIFICANCE STATEMENT Previous work has shown that spike-time correlations occurring on short timescales can affect the encoding of visual inputs. Although spike-time correlations significantly vary in both magnitude and sign across trials, their impact on trial-by-trial behavior is not fully understood. Using neural recordings from area MT (middle temporal) in monkeys performing a motion-detection task using a brief stimulus, we found that both positive and negative spike-time correlations predicted behavioral responses as well as firing rate on a trial-by-trial basis. We propose that strong positive and negative spike-time correlations decreased behavioral performance by reducing the signal-to-noise ratio of integrated MT neural signals.

Systems-based analysis of dendritic nonlinearities reveals temporal feature extraction in mouse L5 cortical neurons.

What do dendritic nonlinearities tell a neuron about signals injected into the dendrite? Linear and nonlinear dendritic components affect how time-varying inputs are transformed into action potentials (APs), but the relative contribution of each component is unclear. We developed a novel systems-identification approach to isolate the nonlinear response of layer 5 pyramidal neuron dendrites in mouse prefrontal cortex in response to dendritic current injections. We then quantified the nonlinear component and its effect on the soma, using functional models composed of linear filters and static nonlinearities. Both noise and waveform current injections revealed linear and nonlinear components in the dendritic response. The nonlinear component consisted of fast Na+ spikes that varied in amplitude 10-fold in a single neuron. A functional model reproduced the timing and amplitude of the dendritic spikes and revealed that they were selective to a preferred input dynamic (~4.5 ms rise time). The selectivity of the dendritic spikes became wider in the presence of additive noise, which was also predicted by the functional model. A second functional model revealed that the dendritic spikes were weakly boosted before being linearly integrated at the soma. For both our noise and waveform dendritic input, somatic APs were dependent on the somatic integration of the stimulus, followed a subset of large dendritic spikes, and were selective to the same input dynamics preferred by the dendrites. Our results suggest that the amplitude of fast dendritic spikes conveys information about high-frequency features in the dendritic input, which is then combined with low-frequency somatic integration.NEW & NOTEWORTHY The nonlinear response of layer 5 mouse pyramidal dendrites was isolated with a novel systems-based approach. In response to dendritic current injections, the nonlinear component contained mostly fast, variable-amplitude, Na+ spikes. A functional model accounted for the timing and amplitude of the dendritic spikes and revealed that dendritic spikes are selective to a preferred input dynamic, which was verified experimentally. Thus, fast dendritic nonlinearities behave as high-frequency feature detectors that influence somatic action potentials.

Dynamics of the functional link between area MT LFPs and motion detection.

The evolution of a visually guided perceptual decision results from multiple neural processes, and recent work suggests that signals with different neural origins are reflected in separate frequency bands of the cortical local field potential (LFP). Spike activity and LFPs in the middle temporal area (MT) have a functional link with the perception of motion stimuli (referred to as neural-behavioral correlation). To cast light on the different neural origins that underlie this functional link, we compared the temporal dynamics of the neural-behavioral correlations of MT spikes and LFPs. Wide-band activity was simultaneously recorded from two locations of MT from monkeys performing a threshold, two-stimuli, motion pulse detection task. Shortly after the motion pulse occurred, we found that high-gamma (100-200 Hz) LFPs had a fast, positive correlation with detection performance that was similar to that of the spike response. Beta (10-30 Hz) LFPs were negatively correlated with detection performance, but their dynamics were much slower, peaked late, and did not depend on stimulus configuration or reaction time. A late change in the correlation of all LFPs across the two recording electrodes suggests that a common input arrived at both MT locations prior to the behavioral response. Our results support a framework in which early high-gamma LFPs likely reflected fast, bottom-up, sensory processing that was causally linked to perception of the motion pulse. In comparison, late-arriving beta and high-gamma LFPs likely reflected slower, top-down, sources of neural-behavioral correlation that originated after the perception of the motion pulse.

ROC-based estimates of neural-behavioral covariations using matched filters.

Correlations between responses in visual cortex and perceptual performance help draw a functional link between neural activity and visually guided behavior. These correlations are commonly derived with ROC-based neural-behavioral covariances (referred to as choice or detect probability) using boxcar analysis windows. Although boxcar windows capture the covariation between neural activity and behavior during steady-state stimulus presentations, they are not optimized to capture these correlations during short time-varying visual inputs. In this study, we implemented a matched-filter technique, combined with cross-validation, to improve the estimation of ROC-based neural-behavioral covariance under short and dynamic stimulus conditions. We show that this approach maximizes the area under the ROC curve and converges to the true neural-behavioral covariance using a Poisson spiking model. We also demonstrate that the matched filter, combined with cross-validation, reveals the dynamics of the neural-behavioral covariations of individual MT neurons during the detection of a brief motion stimulus.

Parietal cortex signals come unstuck in time.

Humans and other animals are surprisingly adept at estimating the duration of temporal intervals, even without the use of watches and clocks. This ability is typically studied in the lab by asking observers to indicate their estimate of the time between two external sensory events. The results of such studies confirm that humans can accurately estimate durations on a variety of time scales. Although many brain areas are thought to contribute to the representation of elapsed time, recent neurophysiological studies have linked the parietal cortex in particular to the perception of sub-second time intervals. In this Primer, we describe previous work on parietal cortex and time perception, and we highlight the findings of a study published in this issue of PLOS Biology, in which Schneider and Ghose characterize single-neuron responses during performance of a novel "Temporal Production" task. During temporal production, the observer must track the passage of time without anticipating any external sensory event, and it appears that the parietal cortex may use a unique strategy to support this type of measurement.

A compartmental model of linear resonance and signal transfer in dendrites.

Dendrites carry signals between synapses and the soma and play a central role in neural computation. Although they contain many nonlinear ion channels, their signal-transfer properties are linear under some experimental conditions. In experiments with continuous-time inputs, a resonant linear two-port model has been shown to provide a near-perfect fit to the dendrite-to-soma input-output relationship. In this study, we focused on this linear aspect of signal transfer using impedance functions that replace biophysical channel models in order to describe the electrical properties of the dendritic membrane. The membrane impedance model of dendrites preserves the accuracy of the two-port model with minimal computational complexity. Using this approach, we demonstrate two membrane impedance profiles of dendrites that reproduced the experimentally observed two-port results. These impedance profiles demonstrate that the two-port results are compatible with different computational schemes. In addition, our model highlights how dendritic resonance can minimize the location-dependent attenuation of signals at the resonant frequency. Thus, in this model, dendrites function as linear-resonant filters that carry signals between nonlinear computational units.

Spatial attention enhances the selective integration of activity from area MT.

Distinguishing which of the many proposed neural mechanisms of spatial attention actually underlies behavioral improvements in visually guided tasks has been difficult. One attractive hypothesis is that attention allows downstream neural circuits to selectively integrate responses from the most informative sensory neurons. This would allow behavioral performance to be based on the highest-quality signals available in visual cortex. We examined this hypothesis by asking how spatial attention affects both the stimulus sensitivity of middle temporal (MT) neurons and their corresponding correlation with behavior. Analyzing a data set pooled from two experiments involving four monkeys, we found that spatial attention did not appreciably affect either the stimulus sensitivity of the neurons or the correlation between their activity and behavior. However, for those sessions in which there was a robust behavioral effect of attention, focusing attention inside the neuron's receptive field significantly increased the correlation between these two metrics, an indication of selective integration. These results suggest that, similar to mechanisms proposed for the neural basis of perceptual learning, the behavioral benefits of focusing spatial attention are attributable to selective integration of neural activity from visual cortical areas by their downstream targets.

The functional link between area MT neural fluctuations and detection of a brief motion stimulus.

Fluctuations of neural firing rates in visual cortex are known to be correlated with variations in perceptual performance. It is important to know whether these fluctuations are functionally linked to perception in a causal manner or instead reflect non-causal processes that arise after the perceptual decision is made. We recorded from middle temporal (MT) neurons from monkey subjects while they detected the random occurrence of a brief 50 ms motion pulse that occurred in either of two (or simultaneously in both) random dot patches located in the same hemisphere. The receptive field parameters of the motion pulse were matched to that preferred by each MT neuron under study. This task contained uncertainty in both space and time because, on any given trial, the subjects did not know which patch would contain the motion pulse or when the motion pulse would occur. Covariations between MT activity and behavior began just before the motion pulse onset and peaked at the maximum neural response. These neural-behavioral covariations were strongest when only one patch contained the motion pulse and were still weakly present when a patch did not contain a motion pulse. A feedforward temporal integration model with two independent detector channels captured both the detection performance and evolution of the neural-behavior covariations over time and stimulus condition. The results suggest that, when detecting a brief visual stimulus, there is a causal relationship between fluctuations in neural activity and variations in behavior across trials.

Neural correlates of subsecond time distortion in the middle temporal area of visual cortex.

How does the brain represent the passage of time at the subsecond scale? Although different conceptual models for time perception have been proposed, its neurophysiological basis remains unknown. We took advantage of a visual duration illusion produced by stimulus novelty to link changes in cortical activity in monkeys with distortions of duration perception in humans. We found that human subjects perceived the duration of a subsecond motion pulse with a novel direction longer than a motion pulse with a repeated direction. Recording from monkeys viewing identical motion stimuli but performing a different behavioral task, we found that both the duration and amplitude of the neural response in the middle temporal area of visual cortex were positively correlated with the degree of novelty of the motion direction. In contrast to previous accounts that attribute distortions in duration perception to changes in the speed of a putative internal clock, our results suggest that the known adaptive properties of neural activity in visual cortex contributes to subsecond temporal distortions.

Behavioral time course of microstimulation in cortical area MT.

Electrical stimulation of the brain is a valuable research tool and has shown therapeutic promise in the development of new sensory neural prosthetics. Despite its widespread use, we still do not fully understand how current passed through a microelectrode interacts with functioning neural circuits. Past behavioral studies have suggested that weak electrical stimulation (referred to as microstimulation) of sensory areas of cortex produces percepts that are similar to those generated by normal sensory stimuli. In contrast, electrophysiological studies using in vitro or anesthetized preparations have shown that neural activity produced by brief microstimulation is radically different and longer lasting than normal responses. To help reconcile these two aspects of microstimulation, we examined the temporal properties that microstimulation has on visual perception. We found that brief application of subthreshold microstimulation in the middle temporal (MT) area of visual cortex produced smaller and longer-lasting effects on motion perception compared with an equivalent visual stimulus. In agreement with past electrophysiological studies, a computer simulation reproduced our behavioral effects when the time course of a single microstimulation pulse was modeled with three components: an immediate fast strong excitatory component, followed by a weaker inhibitory component, and then followed by a long duration weak excitatory component. Overall, these results suggest the behavioral effects of microstimulation in our experiments were caused by the unique and long-lasting temporal effects microstimulation has on functioning cortical circuits.

The effect of microsaccades on the correlation between neural activity and behavior in middle temporal, ventral intraparietal, and lateral intraparietal areas.

It is widely reported that the activity of single neurons in visual cortex is correlated with the perceptual decision of the subject. The strength of this correlation has implications for the neuronal populations generating the percepts. Here we asked whether microsaccades, which are small, involuntary eye movements, contribute to the correlation between neural activity and behavior. We analyzed data from three different visual detection experiments, with neural recordings from the middle temporal (MT), lateral intraparietal (LIP), and ventral intraparietal (VIP) areas. All three experiments used random dot motion stimuli, with the animals required to detect a transient or sustained change in the speed or strength of motion. We found that microsaccades suppressed neural activity and inhibited detection of the motion stimulus, contributing to the correlation between neural activity and detection behavior. Microsaccades accounted for as much as 19% of the correlation for area MT, 21% for area LIP, and 17% for VIP. While microsaccades only explain part of the correlation between neural activity and behavior, their effect has implications when considering the neuronal populations underlying perceptual decisions.

The effect of middle temporal spike phase on sensory encoding and correlates with behavior during a motion-detection task.

Previous studies have shown that sensory neurons that are the most informative of the stimulus tend to be the best correlated with the subject's perceptual decision. We wanted to know whether this relationship might also apply to short time segments of a neuron's response. We asked whether spikes that conveyed more information about a motion stimulus were also more tightly linked to the perceptual behavior. We examined single-neuron activity in middle temporal (MT) area while monkeys performed a motion-detection task. Because of a slow stimulus update (every 27 ms), activity in many MT neurons was entrained and phase-locked to the stimulus. These stimulus-entrained neuronal oscillations allowed us to separate spikes based on phase. We observed a large amount of variability in how spikes at different phases of the oscillation encoded the stimulus, as revealed by the spike-triggered average of the motion. Spikes during certain phases of the cycle were much more informative about the presence of coherent motion than others. Importantly, we found that the phases that were the most informative about the motion stimulus were also more correlated with the behavioral performance and reaction time of the animal. Our results suggest that the relationship between a neuron's spikes, the stimulus, and behavior can vary on a time scale of tens of milliseconds.

The neuronal transfer function: contributions from voltage- and time-dependent mechanisms.

The discovery that an array of voltage- and time-dependent channels is present in both the dendrites and soma of neurons has led to a variety of models for single-neuron computation. Most of these models, however, are based on experimental techniques that use simplified inputs of either single synaptic events or brief current injections. In this study, we used a more complex time-varying input to mimic the continuous barrage of synaptic input that neurons are likely to receive in vivo. Using dual whole-cell recordings of CA1 pyramidal neurons, we injected long-duration white-noise current into the dendrites. The amplitude variance of this stimulus was adjusted to produce either low subthreshold or high suprathreshold fluctuations of the somatic membrane potential. Somatic action potentials were produced in the high variance input condition. Applying a rigorous system-identification approach, we discovered that the neuronal input/output function was extremely well described by a model containing a linear bandpass filter followed by a nonlinear static-gain. Using computer models, we found that a range of voltage-dependent channel properties can readily account for the experimentally observed filtering in the neuronal input/output function. In addition, the bandpass signal processing of the neuronal input/output function was determined by the time-dependence of the channels. A simple active channel, however, could not account for the experimentally observed change in gain. These results suggest that nonlinear voltage- and time-dependent channels contribute to the linear filtering of the neuronal input/output function and that channel kinetics shape temporal signal processing in dendrites.

Dendrite-to-soma input/output function of continuous time-varying signals in hippocampal CA1 pyramidal neurons.

We examined how hippocamal CA1 neurons process complex time-varying inputs that dendrites are likely to receive in vivo. We propose a functional model of the dendrite-to-soma input/output relationship that combines temporal integration and static-gain control mechanisms. Using simultaneous dual whole cell recordings, we injected 50 s of subthreshold and suprathreshold zero-mean white-noise current into the primary dendritic trunk along the proximal 2/3 of stratum radiatum and measured the membrane potential at the soma. Applying a nonlinear system-identification analysis, we found that a cascade of a linear filter followed by an adapting static-gain term fully accounted for the nonspiking input/output relationship between the dendrite and soma. The estimated filters contained a prominent band-pass region in the 1- to 10-Hz frequency range that remained constant as a function of stimulus variance. The gain of the dendrite-to-soma input/output relationship, in contrast, varied as a function of stimulus variance. When the contribution of the voltage-dependent current I(h) was eliminated, the estimated filters lost their band-pass properties and the gain regulation was substantially altered. Our findings suggest that the dendrite-to-soma input/output relationship for proximal apical inputs to CA1 pyramidal neurons is well described as a band-pass filter in the theta frequency range followed by a gain-control nonlinearity that dynamically adapts to the statistics of the input signal.

Attentional modulation of motion integration of individual neurons in the middle temporal visual area.

We examined how spatially directed attention affected the integration of motion in neurons of the middle temporal (MT) area of visual cortex. We recorded from single MT neurons while monkeys performed a motion detection task under two attentional states. Using 0% coherent random dot motion, we estimated the optimal linear transfer function (or kernel) between the global motion and the neuronal response. This linear kernel filtered the random dot motion across direction, speed, and time. Slightly less than one-half of the neurons produced reasonably well defined kernels that also tended to account for both the directional selectivity and responses to coherent motion of different strengths. This subpopulation of cells had faster, more transient, and more robust responses to visual stimuli than neurons with kernels that did not contain well defined regions of integration. For those neurons that had large attentional modulation and produced well defined kernels, we found attention scaled the temporal profile of the transfer function with no appreciable shift in time or change in shape. Thus, for MT neurons described by a linear transfer function, attention produced a multiplicative scaling of the temporal integration window.

The role of attention in visual processing.

Attention to a visual stimulus typically increases the responses of cortical neurons to that stimulus. Because many studies have shown a close relationship between the performance of individual neurons and behavioural performance of animal subjects, it is important to consider how attention affects this relationship. Measurements of behavioural and neuronal performance taken from rhesus monkeys while they performed a motion detection task with two attentional states show that attention alters the relationship between behaviour and neuronal response. Notably, attention affects the relationship differently in different cortical visual areas. This indicates that a close relationship between neuronal and behavioural performance on a given task persists over changes in attentional state only within limited regions of visual cortex.

Dynamics of neuronal responses in macaque MT and VIP during motion detection.

We examined how the relationship between neuronal activity and behavior evolved over time during a motion-detection task. Recording from two regions of visual cortex that process motion, the middle temporal (MT) and ventral intraparietal (VIP) areas, we used the time it took subjects to detect a motion stimulus to evaluate the dynamics of the underlying neuronal signals. Single-neuron activity was correlated with stimulus detection and reaction time (RT) in both areas. The rising edge of the population response from both areas was highly predictive of RT using a simple threshold-detection model. The time course of the population responses, however, differed between MT and VIP. For MT, the onset of the neuronal response was relatively constant, whereas for VIP the onset of the neuronal responses increased with RT. In contrast to previous studies, we found that single neurons were not reliable detectors of the motion signal when constrained by realistic detection times.