Variability Of Neuronal Responses Research Articles

Dissociation of Response Variability from Firing Rate Effects in Frontal Eye Field Neurons during Visual Stimulation, Working Memory, and Attention

Recent studies suggest that trial-to-trial variability of neuronal spiking responses may provide important information about behavioral state. Observed changes in variability during sensory stimulation, attention, motor preparation, and visual discrimination suggest that variability may reflect the engagement of neurons in a behavioral task. We examined changes in spiking variability of frontal eye field (FEF) neurons in a change detection task requiring monkeys to remember a visually cued location and direct attention to that location while ignoring distracters elsewhere. In this task, the firing rates (FRs) of FEF neurons not only continuously reflect the location of the remembered cue and select targets, but also predict detection performance on a trial-by-trial basis. Changes in FEF response variability, as measured by the Fano factor (FF), showed clear dissociations from changes in FR. The FF declined in response to visual stimulation at all tested locations, even in the opposite hemifield, indicating much broader spatial tuning of the FF compared with the FR. Furthermore, despite robust spatial modulation of the FR throughout all epochs of the task, spatial tuning of the FF did not persist throughout the delay period, nor did it show attentional modulation. These results indicate that changes in variability, at least in the FEF, are most effectively driven by visual stimulation, while behavioral engagement is not sufficient. Instead, changes in variability may reflect shifts in the balance between feedforward and recurrent sources of excitatory drive.

Sensory Adaptation Improves Neural Coding Efficiency despite Increasing Correlations in Neuronal Response Variability

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Natural Movies Evoke Spike Trains with Low Spike Time Variability in Cat Primary Visual Cortex

Neuronal responses in primary visual cortex have been found to be highly variable. This has led to the widespread notion that neuronal responses have to be averaged over large numbers of neurons to obtain suitably invariant responses that can be used to reliably encode or represent external stimuli. However, it is possible that the high variability of neuronal responses may result from the use of simple, artificial stimuli and that the visual cortex may respond differently to dynamic, naturalistic images. To investigate this question, we recorded the responses of primary visual cortical neurons in the anesthetized cat under stimulation with time-varying natural movies. We found that cortical neurons on the whole exhibited a high degree of spike count variability, but a surprisingly low degree of spike time variability. The spike count variability was further reduced when all but the first spike in a burst were removed. We also found that responses exhibiting low spike time variability exhibited low spike count variability, suggesting that rate coding and temporal coding might be more compatible than previously thought. In addition, we found the spike time variability to be significantly lower when stimulated by natural movies as compared with stimulation using drifting gratings. Our results indicate that response variability in primary visual cortex is stimulus dependent and significantly lower than previous measurements have indicated.

Relating neuronal to behavioral performance: variability of optomotor responses in the blowfly.

Behavioral responses of an animal vary even when they are elicited by the same stimulus. This variability is due to stochastic processes within the nervous system and to the changing internal states of the animal. To what extent does the variability of neuronal responses account for the overall variability at the behavioral level? To address this question we evaluate the neuronal variability at the output stage of the blowfly's (Calliphora vicina) visual system by recording from motion-sensitive interneurons mediating head optomotor responses. By means of a simple modelling approach representing the sensory-motor transformation, we predict head movements on the basis of the recorded responses of motion-sensitive neurons and compare the variability of the predicted head movements with that of the observed ones. Large gain changes of optomotor head movements have previously been shown to go along with changes in the animals' activity state. Our modelling approach substantiates that these gain changes are imposed downstream of the motion-sensitive neurons of the visual system. Moreover, since predicted head movements are clearly more reliable than those actually observed, we conclude that substantial variability is introduced downstream of the visual system.

How stochastic adaptation of neurons shapes interspike interval statistics – theory and experiment

Trial-to-trial variability of neuronal responses is a prominent feature of sensory systems and has a significant impact on subsequent sensory signal processing. Fluctuations of the underlying ionic currents due to channel noise represent a major intrinsic source of noise that causes neuronal response variability. In many sensory systems it is, however, difficult to assess the type of channel noise by direct somatic recordings without severely damaging the sensory transduction machinery. Here we develop a novel indirect approach to distinguish different types of noise based on the interspike interval (ISI) statistics. The method is then used to determine the dominating source of noise of a sensory neuron. In particular, we seek to distinguish noise originating from fast spike-generating channels from channel noise associated to slow adaptation currents. Adaptation currents are found in many neurons and profoundly shape the signal transmission properties

Using computer simulations to determine the limitations of dynamic clamp stimuli applied at the soma in mimicking distributed conductance sources

In previous studies we used the technique of dynamic clamp to study how temporal modulation of inhibitory and excitatory inputs control the frequency and precise timing of spikes in neurons of the deep cerebellar nuclei (DCN). Although this technique is now widely used, it is limited to interpreting conductance inputs as being location independent; i.e., all inputs that are biologically distributed across the dendritic tree are applied to the soma. We used computer simulations of a morphologically realistic model of DCN neurons to compare the effects of purely somatic vs. distributed dendritic inputs in this cell type. We applied the same conductance stimuli used in our published experiments to the model. To simulate variability in neuronal responses to repeated stimuli, we added a somatic white current noise to reproduce subthreshold fluctuations in the membrane potential. We were able to replicate our dynamic clamp results with respect to spike rates and spike precision for different patterns of background synaptic activity. We found only minor differences in the spike pattern generation between focal or distributed input in this cell type even when strong inhibitory or excitatory bursts were applied. However, the location dependence of dynamic clamp stimuli is likely to be different for each cell type examined, and the simulation approach developed in the present study will allow a careful assessment of location dependence in all cell types.

Modeling extracellular potentials

Modeling extracellular potentials

Methods for predicting cortical UP and DOWN states from the phase of deep layer local field potentials

During anesthesia, slow-wave sleep and quiet wakefulness, neuronal membrane potentials collectively switch between de- and hyperpolarized levels, the cortical UP and DOWN states. Previous studies have shown that these cortical UP/DOWN states affect the excitability of individual neurons in response to sensory stimuli, indicating that a significant amount of the trial-to-trial variability in neuronal responses can be attributed to ongoing fluctuations in network activity. However, as intracellular recordings are frequently not available, it is important to be able to estimate their occurrence purely from extracellular data. Here, we combine in vivo whole cell recordings from single neurons with multi-site extracellular microelectrode recordings, to quantify the performance of various approaches to predicting UP/DOWN states from the deep-layer local field potential (LFP). We find that UP/DOWN states in deep cortical layers of rat primary auditory cortex (A1) are predictable from the phase of LFP at low frequencies (< 4Hz), and that the likelihood of a given state varies sinusoidally with the phase of LFP at these frequencies. We introduce a novel method of detecting cortical state by combining information concerning the phase of the LFP and ongoing multi-unit activity.

Local field potentials indicate network state and account for neuronal response variability

Multineuronal recordings have revealed that neurons in primary visual cortex (V1) exhibit coordinated fluctuations of spiking activity in the absence and in the presence of visual stimulation. From the perspective of understanding a single cell's spiking activity relative to a behavior or stimulus, these network fluctuations are typically considered to be noise. We show that these events are highly correlated with another commonly recorded signal, the local field potential (LFP), and are also likely related to global network state phenomena which have been observed in a number of neural systems. Moreover, we show that attributing a component of cell firing to these network fluctuations via explicit modeling of the LFP improves the recovery of cell properties. This suggests that the impact of network fluctuations may be estimated using the LFP, and that a portion of this network activity is unrelated to the stimulus and instead reflects ongoing cortical activity. Thus, the LFP acts as an easily accessible bridge between the network state and the spiking activity.

Changes in the response rate and response variability of area V4 neurons during saccade preparatio

Event Abstract Back to Event Changes in the response rate and response variability of area V4 neurons during saccade preparatio Nicholas Steinmetz1* and Tirin Moore1 1 Stanford University, United States The visually driven responses of macaque area V4 neurons are modulated during the preparation of saccadic eye movements, but the relationship between presaccadic modulation in area V4 and saccade preparation is poorly understood. Recent neurophysiological studies suggest that the variability across trials of spiking responses provides a more reliable signature of motor preparation than mean firing rate across trials. We compared the dynamics of the response rate and the variability in the rate across trials for area V4 neurons during the preparation of visually guided saccades. As in previous reports, we found that the mean firing rate of V4 neurons was enhanced when saccades were prepared to stimuli within a neuron’s receptive field (RF) in comparison with saccades to a non-RF location. Further, we found robust decreases in response variability prior to saccades and found that these decreases predicted saccadic reaction times for saccades both to RF and non-RF stimuli. Importantly, response variability predicted reaction time whether or not there were any accompanying changes in mean firing rate. In addition to predicting saccade direction, the mean firing rate could also predict reaction time, but only for saccades directed to the RF stimuli. These results demonstrate that response variability of area V4 neurons, like mean response rate, provides a signature of saccade preparation. However, the two signatures reflect complementary aspects of that preparation. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session I Citation: Steinmetz N and Moore T (2010). Changes in the response rate and response variability of area V4 neurons during saccade preparatio. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00090 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 20 Feb 2010; Published Online: 20 Feb 2010. * Correspondence: Nicholas Steinmetz, Stanford University, Stanford, United States, nick.steinmetz@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Nicholas Steinmetz Tirin Moore Google Nicholas Steinmetz Tirin Moore Google Scholar Nicholas Steinmetz Tirin Moore PubMed Nicholas Steinmetz Tirin Moore Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Changes in the Response Rate and Response Variability of Area V4 Neurons During the Preparation of Saccadic Eye Movements

The visually driven responses of macaque area V4 neurons are modulated during the preparation of saccadic eye movements, but the relationship between presaccadic modulation in area V4 and saccade preparation is poorly understood. Recent neurophysiological studies suggest that the variability across trials of spiking responses provides a more reliable signature of motor preparation than mean firing rate across trials. We compared the dynamics of the response rate and the variability in the rate across trials for area V4 neurons during the preparation of visually guided saccades. As in previous reports, we found that the mean firing rate of V4 neurons was enhanced when saccades were prepared to stimuli within a neuron's receptive field (RF) in comparison with saccades to a non-RF location. Further, we found robust decreases in response variability prior to saccades and found that these decreases predicted saccadic reaction times for saccades both to RF and non-RF stimuli. Importantly, response variability predicted reaction time whether or not there were any accompanying changes in mean firing rate. In addition to predicting saccade direction, the mean firing rate could also predict reaction time, but only for saccades directed to the RF stimuli. These results demonstrate that response variability of area V4 neurons, like mean response rate, provides a signature of saccade preparation. However, the two signatures reflect complementary aspects of that preparation.

Dissociation of Neural Mechanisms Underlying Orientation Processing in Humans

Orientation selectivity is a fundamental, emergent property of neurons in early visual cortex, and the discovery of that property has dramatically shaped how we conceptualize visual processing. However, much remains unknown about the neural substrates of this basic building block of perception, and what is known primarily stems from animal physiology studies. To probe the neural concomitants of orientation processing in humans, we employed repetitive transcranial magnetic stimulation (rTMS), which can significantly attenuate neuronal spiking activity, hemodynamic responses, and local field potentials within a focused cortical region. Using rTMS to suppress neural responses evoked by stimuli falling within a local region of the visual field, we were able to dissociate two distinct components of the neural circuitry underlying orientation processing: selectivity and contextual effects. Orientation selectivity gauged by masking was unchanged by rTMS, whereas an otherwise robust orientation repulsion illusion was weakened after rTMS. This dissociation implies that orientation processing in humans relies on distinct mechanisms, only one of which was impacted by rTMS. These results are consistent with models positing that orientation selectivity is governed by patterns of convergence of thalamic afferents onto cortical neurons, with intracortical activity then shaping population responses amongst those cortical neurons.

Poisson or Not Poisson: Differences in Spike Train Statistics between Parietal Cortical Areas

The variability of neuronal responses is proportional to the mean in many brain areas, which suggests that neural responses might follow a Poisson distribution. In this issue of Neuron, Maimon and Assad document a surprising violation of Poisson firing. Specifically, they show that there are differences in the amount of periodic structure in spike trains across cortical areas, with multimodal sensory areas being more regular than visual areas.

Aging affects response variability of V1 and MT neurons in rhesus monkeys

Visual function declines with age. Much of the decline may result from functional degradation in central visual areas. To investigate the physiological mechanisms underlying visual function declines during normal aging, we compared the response variability of cells in primary visual cortex (V1) and middle temporal visual area (MT) in young adult and very old macaque monkeys using single-neuron in vivo electrophysiology. We found that mean response and response variability in both V1 and MT of old monkeys are significantly higher than in young monkeys. And response-to-noise ratio in old monkeys is significantly lower than in young ones. The results are consistent with an age-related degradation of inhibitory intracortical circuits. The neural changes described here could contribute to declines in visual function during senescence.

Different sources of nitric oxide mediate neurovascular coupling in the lateral geniculate nucleus of the cat

Understanding the link between neuronal responses (NRs) and metabolic signals is fundamental to our knowledge of brain function and it is a milestone in our efforts to interpret data from modern non invasive optical techniques such as fMRI, which are based on the close coupling between metabolic demand of active neurons and local changes in blood flow. The challenge is to unravel the link. Here we show, using spectrophotometry to record oxyhaemoglobin and methemoglobin (surrogate markers of cerebral flow and nitric oxide levels respectively) together with extracellular neuronal recordings in vivo and applying a multiple polynomial regression model, that the markers are able to predict up about 80% of variability in NR. Furthermore, we show that the coupling between blood flow and neuronal activity is heavily influenced by nitric oxide (NO). While NRs show the typical saturating response, blood flow shows a linear behaviour during contrast-response curves, with nitric oxide from different sources acting differently for low and high intensity.

Towards a biophysical basis of spike based inference

Event Abstract Back to Event Towards a biophysical basis of spike based inference Cells in the retina, thalamus, and sensory cortices integrate inputs from thousands of synaptic afferents. Sensory stimulation varies over time and in response to specific events in this stream, these cells emit spikes. What are the generic dynamical properties of sensory neurons allowing them to extract information from their input and signal it efficiently? How can these properties be realized by biophysical mechanisms? We previously derived dynamical equations for an optimal “Bayesian” spiking neuron. We used a generative model (GM) to describe how synaptic input is caused by the dynamic stimulus. This formulation allowed us to derive in a principled way an equation for optimalsynaptic integration. In addition, we supposed that the model is self consistent, i.e. that the same GM can be used to estimate the stimulus from the output spike train. This implied a spike generation mechanism based on an adapting firing threshold. Since the model is designed to optimally transmit information, it can be used as a tool to understand neural processing. It provides a normative yardstick against which to compare empirical spike data. Furthermore, it helps to understand how different components in the complex neural dynamics affect sensory processing. The Bayesian model, however, is motivated by optimality principles rather than biological realism. In order to bridge the gap between normative and biophysical models, we analyzed its dynamics for different input regimes where the dynamics can be described by mechanisms known from biophysical models. Our results suggest that elements necessary to instantiate optimal information transfer are (a) a leak current adjusting the time constant to the temporal stimulus statistics, (b) a voltage-dependent depolarizing term preventing the membrane potential to become too hyperpolarized, and (c) spike based adaptation increasing the membrane conductance after each spike. Signatures of these elements have been found in thalamic and cortical neurons: besides a variety of leak currents, many cells show hyperpolarisation activated depolarizing I_h currents and Ca2+ controlled K-currents represent a significant source of spike based adaptation. Such detailed descriptions contrast with stochastic models of sensory neurons like the Linear Nonlinear Poisson model. We argue that Bayesian spiking neurons are both more efficient at transferring information and provide additional insight into biophysical detail. Nevertheless, they are efficient to simulate and account for the apparent Poisson-like response variability of cortical neurons. Our analysis provides important links between biophysical mechanisms and GMs describing the stimulus statistics e.g. how often stimuli appear and how long they last. The probabilistic interpretation clarifies the functional significance of different currents and how they enable cells to adapt to different input regimes. Showing how information transmission breaks down if specific components are blocked forms a basis to understand how natural neuromodulators or pharmacological substances acting on the corresponding channels affect sensory processing. By putting emphasis on the inference task as the ”problem to be solved” and linking it to more phenomenological descriptions of neural dynamics we can advance our understanding of the link between the biophysics of sensory neurons and perception. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Towards a biophysical basis of spike based inference. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.268 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 Feb 2009; Published Online: 04 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Electrical stimulation therapies for CNS disorders and pain are mediated by competition between different neuronal networks in the brain

CNS neuronal networks are known to control normal physiological functions, including locomotion and respiration. Neuronal networks also mediate the pathophysiology of many CNS disorders. Stimulation therapies, including localized brain and vagus nerve stimulation, electroshock, and acupuncture, are proposed to activate "therapeutic" neuronal networks. These therapeutic networks are dormant prior to stimulatory treatments, but when the dormant networks are activated they compete with pathophysiological neuronal networks, disrupting their function. This competition diminishes the disease symptoms, providing effective therapy for otherwise intractable CNS disorders, including epilepsy, Parkinson's disease, chronic pain, and depression. Competition between stimulation-activated therapeutic networks and pathophysiological networks is a major mechanism mediating the therapeutic effects of stimulation. This network interaction is hypothesized to involve competition for "control" of brain regions that contain high proportions of conditional multireceptive (CMR) neurons. CMR regions, including brainstem reticular formation, amygdala, and cerebral cortex, have extensive connections to numerous brain areas, allowing these regions to participate potentially in many networks. The participation of CMR regions in any network is often variable, depending on the conditions affecting the organism, including vigilance states, drug treatment, and learning. This response variability of CMR neurons is due to the high incidence of excitatory postsynaptic potentials that are below threshold for triggering action potentials. These subthreshold responses can be brought to threshold by blocking inhibition or enhancing excitation via the paradigms used in stimulation therapies. Participation of CMR regions in a network is also strongly affected by pharmacological treatments (convulsant or anesthetic drugs) and stimulus parameters (strength and repetition rate). Many studies indicate that treatment of unanesthetized animals with antagonists (bicuculline or strychnine) of inhibitory neurotransmitter (GABA or glycine) receptors can cause CMR neurons to become consistently responsive to external inputs (e.g., peripheral nerve, sensory, or electrical stimuli in the brain) to which these neurons did not previously respond. Conversely, agents that enhance GABA-mediated inhibition (e.g., barbiturates and benzodiazepines) or antagonize glutamate-mediated excitation (e.g., ketamine) can cause CMR neurons to become unresponsive to inputs to which they responded previously. The responses of CMR neurons exhibit extensive short-term and long-term plasticity, which permits them to participate to a variable degree in many networks. Short-term plasticity subserves termination of disease symptoms, while long-term plasticity in CMR regions subserves symptom prevention. This network interaction hypothesis has value for future research in CNS disease mechanisms and also for identifying therapeutic targets in specific brain networks for more selective stimulation and pharmacological therapies.

Difficulty of Visual Search Modulates Neuronal Interactions and Response Variability in the Frontal Eye Field

The frontal eye field (FEF) is involved in selecting visual targets for eye movements. To understand how populations of FEF neurons interact during target selection, we recorded activity from multiple neurons simultaneously while macaques performed two versions of a visual search task. We used a multivariate analysis in a point process statistical framework to estimate the instantaneous firing rate and compare interactions among neurons between tasks. We found that FEF neurons were engaged in more interactions during easier visual search tasks compared with harder search tasks. In particular, eye movement-related neurons were involved in more interactions than visual-related neurons. In addition, our analysis revealed a decrease in the variability of spiking activity in the FEF beginning approximately 100 ms before saccade onset. The minimum in response variability occurred approximately 20 ms earlier for the easier search task compared with the harder one. This difference is positively correlated with the difference in saccade reaction times for the two tasks. These findings show that a multivariate analysis can provide a measure of neuronal interactions and characterize the spiking activity of FEF neurons in the context of a population of neurons.

Synaptic input statistics tune the variability and reproducibility of neuronal responses

Synaptic waveforms, constructed from excitatory and inhibitory presynaptic Poisson trains, are presented to living and computational neurons. We review how the average output of a neuron (e.g., the firing rate) is set by the difference between excitatory and inhibitory event rates while neuronal variability is set by their sum. We distinguish neuronal variability from reproducibility. Variability quantifies how much an output measure is expected to vary; for example, the interspike interval coefficient of variation quantifies the typical range of interspike intervals. Reproducibility quantifies the similarity of neuronal outputs in response to repeated presentations of identical stimuli. Although variability and reproducibility are conceptually distinct, we show that, for ideal current source synapses, reproducibility is defined entirely by variability. For physiologically realistic conductance-based synapses, however, reproducibility is distinct from variability and average output, set by the Poisson rate and the degree of synchrony within the synaptic waveform.

Increase of Neuronal Response Variability at Higher Processing Levels as Revealed by Simultaneous Recordings

A key problem for neuronal information processing is the variability of spike trains, something that is likely to constrain the encoding of sensory signals. We measured interspike-interval variability (coefficient of variation) as well as spike-count variability (Fano factor) in the metathoracic auditory system of locusts. We performed simultaneous intracellular recordings at the first three processing levels to establish identical physiological conditions. This allows us to assess whether variability is generated anew or is reduced during synaptic transmission and processing. Both the interspike-interval variability as well as the spike-count variability revealed similar trends and showed an increase from the periphery to higher processing levels. This result was confirmed by single-cell recordings. A comparison of ascending interneurons coding for sound direction and those encoding sound patterns showed that the latter respond more reliably to repeated stimulus presentations. In general, the variability of spiking responses was much lower than expected from a Poisson process. Furthermore, we observed a strong dependence of variability on the spike rate, which differed at the three levels investigated. The differences in spike rates account for most of the differences in variability observed between processing levels. For auditory receptors, we found a good agreement between the Fano factor and the squared coefficient of variation, suggesting similarities to a renewal process of spike generation at the periphery. At the level of interneurons, the Fano factor was lower than the squared coefficient of variation; this indicates a higher reliability than expected from the interspike-interval distribution.