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Dive into the research topics where Nicholas J. Priebe is active.

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Featured researches published by Nicholas J. Priebe.


Nature Neuroscience | 2010

Stimulus onset quenches neural variability: a widespread cortical phenomenon

Mark M. Churchland; Byron M. Yu; John P. Cunningham; Leo P. Sugrue; Marlene R. Cohen; Greg Corrado; William T. Newsome; Andy Clark; Paymon Hosseini; Benjamin B. Scott; David C. Bradley; Matthew A. Smith; Adam Kohn; J. Anthony Movshon; Katherine M. Armstrong; Tirin Moore; Steve W. C. Chang; Lawrence H. Snyder; Stephen G. Lisberger; Nicholas J. Priebe; Ian M. Finn; David Ferster; Stephen I. Ryu; Gopal Santhanam; Maneesh Sahani; Krishna V. Shenoy

Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the mean response, but few have examined the effect on response variability. We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests a rather general property of cortex, that its state is stabilized by an input.


Neuron | 2008

Inhibition, Spike Threshold, and Stimulus Selectivity in Primary Visual Cortex

Nicholas J. Priebe; David Ferster

Ever since Hubel and Wiesel described orientation selectivity in the visual cortex, the question of how precise selectivity emerges has been marked by considerable debate. There are essentially two views of how selectivity arises. Feed-forward models rely entirely on the organization of thalamocortical inputs. Feedback models rely on lateral inhibition to refine selectivity relative to a weak bias provided by thalamocortical inputs. The debate is driven by two divergent lines of evidence. On the one hand, many response properties appear to require lateral inhibition, including precise orientation and direction selectivity and crossorientation suppression. On the other hand, intracellular recordings have failed to find consistent evidence for lateral inhibition. Here we demonstrate a resolution to this paradox. Feed-forward models incorporating the intrinsic nonlinear properties of cortical neurons and feed-forward circuits (i.e., spike threshold, contrast saturation, and spike-rate rectification) can account for properties that have previously appeared to require lateral inhibition.


The Journal of Neuroscience | 2006

Tuning for Spatiotemporal Frequency and Speed in Directionally Selective Neurons of Macaque Striate Cortex

Nicholas J. Priebe; Stephen G. Lisberger; J. Anthony Movshon

We recorded the responses of direction-selective simple and complex cells in the primary visual cortex (V1) of anesthetized, paralyzed macaque monkeys. When studied with sine-wave gratings, almost all simple cells in V1 had responses that were separable for spatial and temporal frequency: the preferred temporal frequency did not change and preferred speed decreased as a function of the spatial frequency of the grating. As in previous recordings from the middle temporal visual area (MT), approximately one-quarter of V1 complex cells had separable responses to spatial and temporal frequency, and one-quarter were “speed tuned” in the sense that preferred speed did not change as a function of spatial frequency. Half fell between these two extremes. Reducing the contrast of the gratings caused the population of V1 complex cells to become more separable in their tuning for spatial and temporal frequency. Contrast dependence is explained by the contrast gain of the neurons, which was relatively higher for gratings that were either both of high or both of low temporal and spatial frequency. For stimuli that comprised two spatially superimposed sine-wave gratings, the preferred speeds and tuning bandwidths of V1 neurons could be predicted from the sum of the responses to the component gratings presented alone, unlike neurons in MT that showed nonlinear interactions. We conclude that spatiotemporal modulation of contrast gain creates speed tuning from separable inputs in V1 complex cells. Speed tuning in MT could be primarily inherited from V1, but processing that occurs after V1 and possibly within MT computes selective combinations of speed-tuned signals of special relevance for downstream perceptual and motor mechanisms.


Neuron | 2005

Direction Selectivity of Excitation and Inhibition in Simple Cells of the Cat Primary Visual Cortex

Nicholas J. Priebe; David Ferster

Direction selectivity in simple cells of primary visual cortex, defined from their spike responses, cannot be predicted using linear models. It has been suggested that the shunting inhibition evoked by visual stimulation is responsible for the nonlinear component of direction selectivity. Cortical inhibition would suppress a neurons firing when stimuli move in the nonpreferred direction, but would allow responses to stimuli in the preferred direction. Models of direction selectivity based solely on input from the lateral geniculate nucleus, however, propose that the nonlinear response is caused by spike threshold. By extracting excitatory and inhibitory components of synaptic inputs from intracellular records obtained in vivo, we demonstrate that excitation and inhibition are tuned for the same direction, but differ in relative timing. Further, membrane potential responses combine in a linear fashion. Spike threshold, however, quantitatively accounts for the nonlinear component of direction selectivity, amplifying the direction selectivity of spike output relative to that of synaptic inputs.


Nature Neuroscience | 2004

The contribution of spike threshold to the dichotomy of cortical simple and complex cells.

Nicholas J. Priebe; Ferenc Mechler; Matteo Carandini; David Ferster

The existence of two classes of cells, simple and complex, discovered by Hubel and Wiesel in 1962, is one of the fundamental features of cat primary visual cortex. A quantitative measure used to distinguish simple and complex cells is the ratio between modulated and unmodulated components of spike responses to drifting gratings, an index that forms a bimodal distribution. We have found that the modulation ratio, when derived from the subthreshold membrane potential instead of from spike rate, is unimodally distributed, but highly skewed. The distribution of the modulation ratio as derived from spike rate can, in turn, be predicted quantitatively by the nonlinear properties of spike threshold applied to the skewed distribution of the subthreshold modulation ratio. Threshold also increases the spatial segregation of ON and OFF regions of the receptive field, a defining attribute of simple cells. The distinction between simple and complex cells is therefore enhanced by threshold, much like the selectivity for stimulus features such as orientation and direction. In this case, however, a continuous distribution in the spatial organization of synaptic inputs is transformed into two distinct classes of cells.


Neuron | 2007

The Emergence of Contrast-Invariant Orientation Tuning in Simple Cells of Cat Visual Cortex

Ian M. Finn; Nicholas J. Priebe; David Ferster

Simple cells in primary visual cortex exhibit contrast-invariant orientation tuning, in seeming contradiction to feed-forward models that rely on lateral geniculate nucleus (LGN) input alone. Contrast invariance has therefore been thought to depend on the presence of intracortical lateral inhibition. In vivo intracellular recordings instead suggest that contrast invariance can be explained by three properties of the excitatory pathway. (1) Depolarizations evoked by orthogonal stimuli are determined by the amount of excitation a cell receives from the LGN, relative to the excitation it receives from other cortical cells. (2) Depolarizations evoked by preferred stimuli saturate at lower contrasts than the spike output of LGN relay cells. (3) Visual stimuli evoke contrast-dependent changes in trial-to-trial variability, which lead to contrast-dependent changes in the relationship between membrane potential and spike rate. Thus, high-contrast, orthogonally oriented stimuli that evoke significant depolarizations evoke few spikes. Together these mechanisms, without lateral inhibition, can account for contrast-invariant stimulus selectivity.


Nature Neuroscience | 2006

Mechanisms underlying cross-orientation suppression in cat visual cortex

Nicholas J. Priebe; David Ferster

In simple cells of the cat primary visual cortex, null-oriented stimuli, which by themselves evoke no response, can completely suppress the spiking response to optimally oriented stimuli. This cross-orientation suppression has been interpreted as evidence for cross-orientation inhibition: synaptic inhibition among cortical cells with different preferred orientations. In intracellular recordings from simple cells, however, we found that cross-oriented stimuli suppressed, rather than enhanced, synaptic inhibition and, at the same time, suppressed synaptic excitation. Much of the suppression of excitation could be accounted for by the behavior of geniculate relay cells: contrast saturation and rectification in relay cell responses, when applied to a linear feed-forward model, predicted cross-orientation suppression of the modulation (F1) component of excitation evoked in simple cells. In addition, we found that the suppression of the spike output of simple cells was almost twice the suppression of their synaptic inputs. Thus, cross-orientation suppression, like orientation selectivity, is strongly amplified by threshold.


The Journal of Neuroscience | 2004

Estimating Target Speed from the Population Response in Visual Area MT

Nicholas J. Priebe; Stephen G. Lisberger

To guide behavior, perceptual and motor systems must estimate properties of the sensory environment from the responses of populations of cortical neurons. In the domain of visual motion, estimates of target speed are derived from the responses of motion-sensitive neurons in the middle temporal (MT) area of the extrastriate visual cortex and are used to drive smooth pursuit eye movements and perceptual judgments of speed. We have asked how these behavioral systems estimate target speed from the population response in area MT. We found that increasing the spatial frequency of a sine wave grating caused decreases in the target speed estimated by both pursuit and perception and commensurate changes in the identity of the active neurons in area MT. Decreasing the contrast of a sine wave grating caused decreases in the target speed estimated by both pursuit and perception, while altering only the response amplitude of MT neurons and not the identity of the active neurons. Applying a modified vector-averaging computation to the population response measured in area MT allowed us to predict the effects of both spatial frequency and contrast on speed estimation for both perception and pursuit. The modification biased the speed estimation toward low target speeds when responses across the population of neurons were small.


Nature | 2014

Sensory stimulation shifts visual cortex from synchronous to asynchronous states.

Andrew Y. Y. Tan; Yuzhi Chen; Benjamin Scholl; Eyal Seidemann; Nicholas J. Priebe

In the mammalian cerebral cortex, neural responses are highly variable during spontaneous activity and sensory stimulation. To explain this variability, the cortex of alert animals has been proposed to be in an asynchronous high-conductance state in which irregular spiking arises from the convergence of large numbers of uncorrelated excitatory and inhibitory inputs onto individual neurons. Signatures of this state are that a neuron’s membrane potential (Vm) hovers just below spike threshold, and its aggregate synaptic input is nearly Gaussian, arising from many uncorrelated inputs. Alternatively, irregular spiking could arise from infrequent correlated input events that elicit large fluctuations in Vm (refs 5, 6). To distinguish between these hypotheses, we developed a technique to perform whole-cell Vm measurements from the cortex of behaving monkeys, focusing on primary visual cortex (V1) of monkeys performing a visual fixation task. Here we show that, contrary to the predictions of an asynchronous state, mean Vm during fixation was far from threshold (14 mV) and spiking was triggered by occasional large spontaneous fluctuations. Distributions of Vm values were skewed beyond that expected for a range of Gaussian input, but were consistent with synaptic input arising from infrequent correlated events. Furthermore, spontaneous fluctuations in Vm were correlated with the surrounding network activity, as reflected in simultaneously recorded nearby local field potential. Visual stimulation, however, led to responses more consistent with an asynchronous state: mean Vm approached threshold, fluctuations became more Gaussian, and correlations between single neurons and the surrounding network were disrupted. These observations show that sensory drive can shift a common cortical circuitry from a synchronous to an asynchronous state.


Neuron | 2012

Mechanisms of Neuronal Computation in Mammalian Visual Cortex

Nicholas J. Priebe; David Ferster

Orientation selectivity in the primary visual cortex (V1) is a receptive field property that is at once simple enough to make it amenable to experimental and theoretical approaches and yet complex enough to represent a significant transformation in the representation of the visual image. As a result, V1 has become an area of choice for studying cortical computation and its underlying mechanisms. Here we consider the receptive field properties of the simple cells in cat V1--the cells that receive direct input from thalamic relay cells--and explore how these properties, many of which are highly nonlinear, arise. We have found that many receptive field properties of V1 simple cells fall directly out of Hubel and Wiesels feedforward model when the model incorporates realistic neuronal and synaptic mechanisms, including threshold, synaptic depression, response variability, and the membrane time constant.

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Benjamin Scholl

University of Texas at Austin

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Andrew Y. Y. Tan

University of Texas at Austin

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Jagruti J. Pattadkal

University of Texas at Austin

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Kazuo Imaizumi

University of California

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