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In the classic model of the primary visual cortex, upper-layer complex cells are driven by feedforward inputs from layer 4 simple cells. Based on spike cross-correlation, previousin vivowork has suggested that this connection is strong and dense, with a high probability of connection (50%) and significant strength in connected pairs. A much sparser projection has been found in brain slices, however, with the probability of layer 4 cells connecting to layer 2/3 cells being relatively low (10%). Here, we explore this connectionin vivoin the cat primary visual cortex by recording simultaneously spikes of layer 4 simple cells and the membrane potential (Vm) of layer 2/3 complex cells. By triggering the average of the complex cell'sVmon the spikes of the simple cell (Vm-STA), we found functional coupling to be very common during visual stimulation: the simple cell's spikes tended to occur near the troughs of the complex cell'sVmfluctuations and were, on average, followed by a significant (∼1 mV) fast-rising (10 ms) depolarization in the complex cell. In the absence of visual stimulation, however, when single simple cells were activated electrically through the recording electrode, no significant depolarization, or at most a very weak input (0.1–0.2 mV), was detected in the complex cell. We suggest that the functional coupling observed during visual stimulation arises from coordinated or nearly synchronous activity among a large population of simple cells, only a small fraction of which are presynaptic to the recorded complex cell.

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 Wiesel's feedforward model when the model incorporates realistic neuronal and synaptic mechanisms, including threshold, synaptic depression, response variability, and the membrane time constant.

SummaryContrast invariant orientation tuning in simple cells of the visual cortex depends critically on contrast dependent trial-to-trial variability in their membrane potential responses. This observation raises the question of whether this variability originates from within the cortical circuit or the feedforward inputs from the lateral geniculate nucleus (LGN). To distinguish between these two sources of variability, we first measured membrane potential responses while inactivating the surrounding cortex, and found that response variability was nearly unaffected. We then studied variability in the LGN, including contrast dependence, and the trial-to-trial correlation in responses between nearby neurons. Variability decreased significantly with contrast, whereas correlation changed little. When these experimentally measured parameters of variability were applied to a feedforward model of simple cells that included realistic mechanisms of synaptic integration, contrast-dependent, orientation independent variability emerged in the membrane potential responses. Analogous mechanisms might contribute to the stimulus dependence and propagation of variability throughout the neocortex.

In contrast to neurons of the lateral geniculate nucleus (LGN), neurons in the primary visual cortex (V1) are selective for the direction of visual motion. Cortical direction selectivity could emerge from the spatiotemporal configuration of inputs from thalamic cells, from intracortical inhibitory interactions, or from a combination of thalamic and intracortical interactions. To distinguish between these possibilities, we studied the effect of adaptation (prolonged visual stimulation) on the direction selectivity of intracellularly recorded cortical neurons. It is known that adaptation selectively reduces the responses of cortical neurons, while largely sparing the afferent LGN input. Adaptation can therefore be used as a tool to dissect the relative contribution of afferent and intracortical interactions to the generation of direction selectivity. In both simple and complex cells, adaptation caused a hyperpolarization of the resting membrane potential (−2.5 mV, simple cells, −0.95 mV complex cells). In simple cells, adaptation in either direction only slightly reduced the visually evoked depolarization; this reduction was similar for preferred and null directions. In complex cells, adaptation strongly reduced visual responses in a direction-dependent manner: the reduction was largest when the stimulus direction matched that of the adapting motion. As a result, adaptation caused changes in the direction selectivity of complex cells: direction selectivity was reduced after preferred direction adaptation and increased after null direction adaptation. Because adaptation in the null direction enhanced direction selectivity rather than reduced it, it seems unlikely that inhibition from the null direction is the primary mechanism for creating direction selectivity.

SummaryIn what regime does the cortical circuit operate? Our intracellular studies of surround suppression in cat primary visual cortex (V1) provide strong evidence on this question. Although suppression has been thought to arise from an increase in lateral inhibition, we find that the inhibition that cells receive is reduced, not increased, by a surround stimulus. Instead, suppression is mediated by a withdrawal of excitation. Thalamic recordings and previous work show that these effects cannot be explained by a withdrawal of thalamic input. We find in theoretical work that this behavior can only arise if V1 operates as an inhibition-stabilized network (ISN), in which excitatory recurrence alone is strong enough to destabilize visual responses but feedback inhibition maintains stability. We confirm two strong tests of this scenario experimentally and show through simulation that observed cell-to-cell variability in surround effects, from facilitation to suppression, can arise naturally from variability in the ISN.

A previous study has suggested that complex cells perform a MAX-like operation on their inputs: when two bar stimuli are presented within the receptive field, regardless of their relative separation, the cell's response is similar in amplitude to the larger of the responses elicited by the individual stimuli. This description of complex cells seems at odds with the classical energy model in which complex cells receive input from multiple simple cells with overlapping receptive fields. The energy model predicts, and experiments have confirmed, that bar stimuli should facilitate or suppress one another depending on their relative separation. We have recorded intracellularly from a population of complex cells and studied their responses to paired bar stimuli in detail. A wide range of behavior was observed, from the more classical separation-dependent interactions to purely MAX-like responses. We also found that the more MAX-like a cell was, the broader its spatial-frequency tuning as measured with drifting gratings. These observations are consistent with energy models in which classical complex cells receive input from simple cells with similar preferred spatial frequencies, and MAX-like complex cells receive input from simple cells with disparate preferred spatial frequencies. Generalized energy models, then, can account for diverse modes of computation in cortical complex cells.

SummarySimple 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.

Patch clamp recording in vivo allows an investigator to study intracellular membrane potentials in an intact organism (as opposed to cells in culture or acute brain slices). This technique is a reliable method of obtaining high-quality intracellular recordings from neurons, regardless of their size, in several parts of the mammalian brain. This chapter will describe the principles and practice of performing patch clamp experiments in vivo, beginning with a brief history of the technological developments that have made this technique possible.

How can neural activity propagate through cortical networks built with weak, stochastic synapses? We find precise repetitions of spontaneous patterns of synaptic inputs in neocortical neurons in vivo and in vitro. These patterns repeat after minutes, maintaining millisecond accuracy. Calcium imaging of slices reveals reactivation of sequences of cells during the occurrence of repeated intracellular synaptic patterns. The spontaneous activity drifts with time, engaging different cells. Sequences of active neurons have distinct spatial structures and are repeated in the same order over tens of seconds, revealing modular temporal dynamics. Higher order sequences are replayed with compressed timing.

Feedforward models of visual cortex appear to be inconsistent with a well-known property of cortical cells: contrast invariance of orientation tuning. The models' fixed threshold broadens orientation tuning as contrast increases, whereas in real cells tuning width is invariant with contrast. We have compared the orientation tuning of spike and membrane potential responses in single cells. Both are contrast invariant, yet a threshold-linear relation applied to the membrane potential accurately predicts the orientation tuning of spike responses. The key to this apparent paradox lies in the noisiness of the membrane potential. Responses that are subthreshold on average are still capable of generating spikes on individual trials. Unlike the iceberg effect, contrast invariance remains intact even as threshold narrows orientation selectivity. Noise may, by extension, smooth the average relation between membrane potential and spike rate throughout the brain.

The input conductance of cells in the cat primary visual cortex (V1) has been shown recently to grow substantially during visual stimulation. Because increasing conductance can have a divisive effect on the synaptic input, theoretical proposals have ascribed to it specific functions. According to the veto model, conductance increases would serve to sharpen orientation tuning by increasing most at off-optimal orientations. According to the normalization model, conductance increases would control the cell's gain, by being independent of stimulus orientation and by growing with stimulus contrast. We set out to test these proposals and to determine the visual properties and possible synaptic origin of the conductance increases. We recorded the membrane potential of cat V1 cells while injecting steady currents and presenting drifting grating patterns of varying contrast and orientation. Input conductance grew with stimulus contrast by 20–300%, generally more in simple cells (40–300%) than in complex cells (20–120%), and in simple cells was strongly modulated in time. Conductance was invariably maximal for stimuli of the preferred orientation. Thus conductance changes contribute to a gain control mechanism, but the strength of this gain control does not depend uniquely on contrast. By assuming that the conductance changes are entirely synaptic, we further derived the excitatory and inhibitory synaptic conductances underlying the visual responses. In simple cells, these conductances were often arranged in push-pull: excitation increased when inhibition decreased and vice versa. Excitation and inhibition had similar preferred orientations and did not appear to differ in tuning width, suggesting that the intracortical synaptic inputs to simple cells of cat V1 originate from cells with similar orientation tuning. This finding is at odds with models where orientation tuning in simple cells is achieved by inhibition at off-optimal orientations or sharpened by inhibition that is more broadly tuned than excitation.

Membrane potentials of cortical neurons fluctuate between a hyperpolarized ('down') state and a depolarized ('up') state which may be separated by up to 30 mV, reflecting rapid but infrequent transitions between two patterns of synaptic input. Here we show that such fluctuations may contribute to representation of visual stimuli by cortical cells. In complex cells of anesthetized cats, where such fluctuations are most prominent, prolonged visual stimulation increased the probability of the up state. This probability increase was related to stimulus strength: its dependence on stimulus orientation and contrast matched each cell's averaged membrane potential. Thus large fluctuations in membrane potential are not simply noise on which visual responses are superimposed, but may provide a substrate for encoding sensory information.

Jagadeesh, Bharathi, Heidi Sue Wheat, Leonid L. Kontsevich, Christopher W. Tyler, and David Ferster. Direction selectivity of synaptic potentials in simple cells of the cat visual cortex. J. Neurophysiol. 78: 2772–2789, 1997. The direction selectivity of simple cells in the visual cortex is generated at least in part by nonlinear mechanisms. If a neuron were spatially linear, its responses to moving stimuli could be predicted accurately from linear combinations of its responses to stationary stimuli presented at different positions within the receptive field. In extracellular recordings, this has not been found to be the case. Although the extracellular experiments demonstrate the presence of a nonlinearity, the cellular process underlying the nonlinearity, whether an early synaptic mechanism such as a shunting inhibition or simply the spike threshold at the output, is not known. To differentiate between these possibilities, we have recorded intracellularly from simple cells of the intact cat with the whole cell patch technique. A linear model of direction selectivity was used to analyze the synaptic potentials evoked by stationary sine-wave gratings. The model predicted the responses of cells to moving gratings with considerable accuracy. The degree of direction selectivity and the time course of the responses to moving gratings were both well matched by the model. The direction selectivity of the synaptic potentials was considerably smaller than that of the intracellularly recorded action potential, indicating that a nonlinear mechanism such as threshold enhances the direction selectivity of the cell's output over that of its synaptic inputs. At the input stage, however, the cells apparently sum their synaptic inputs in a highly linear fashion. A more constrained test of linearity of synaptic summation based on principal component analysis was applied to the responses of direction-selective cells to stationary gratings. The analysis confirms that the summation in these cells is highly linear. The principal component analysis is consistent with a model in which direction selectivity in cortical simple cells is generated by only two subunits, each with a different receptive-field position and response time course. The response time course for each of the two subunits is derived for four analyzed cells. Each derived subunit is linear in spatial summation, suggesting that the neurons that comprise each subunit are either geniculate X-cells or receive their primary synaptic input from X-cells. The amplitude of the response of each subunit is linearly related to the contrast of the stimulus. The subunits are nonlinear in the time domain, however: the response to a stationary stimulus whose contrast is modulated sinusoidally in time is nonsinusoidal. The principal component analysis does not exclude models of direction selectivity based on more than two subunits, but such higher-order models would have to include the constraint that the extra subunits form a smooth continuum of interpolation between the properties derived from the two subunit solution.

MORE than 30 years after Hubel and Wiesel1 first described orientation selectivity in the mammalian visual cortex, the mechanism that gives rise to this property is still controversial. Hubel and Wiesel1 proposed a simple model for the origin of orientation tuning, in which the circularly symmetrical receptive fields of neurons in the lateral geniculate nucleus that excite a cortical simple cell are arranged in rows. Since this model was proposed, several experiments2–6 and neuronal simulations7,8 have suggested that the connectivity between the lateral geniculate nucleus and the cortex is not well organized in an orientation-specific fashion, and that orientation tuning arises instead from extensive interactions within the cortex. To test these models we have recorded visually evoked synaptic potentials in simple cells while cooling the cortex9, which largely inactivates the cortical network, but leaves geniculate synaptic input functional. We report that the orientation tuning of these potentials is almost unaffected by cooling the cortex, in agreement with Hubel and Wiesel's original proposal1.

The developing mammalian visual cortex adapts to visual stimuli at birth by reorganizing itself into ocular dominance columns. In his Perspective, [Ferster][1] discusses two new reports ([Hensch and Stryker][2]; [ Fagiolini et al .][3]) that reveal the part played by GABAergic inhibitory interneurons in the establishment of ocular dominance columns in the visual cortex of cats and mice shortly after birth. [1]: http://www.sciencemag.org/cgi/content/full/303/5664/1619 [2]: http://www.sciencemag.org/cgi/content/short/303/5664/1678 [3]: http://www.sciencemag.org/cgi/content/short/303/5664/1681

Recent extra- and intracellular recordings from simple cells in the striate cortex have led to the development of detailed models of how novel receptive field properties are generated by the neuronal circuitry of the cortex. The first stage in assembling simple receptive fields appears to be a linear combination of visual inputs. The output of the linear stage is then normalized by a contrast gain control mechanism. Finally, a threshold and expansive nonlinearity in the spike-generating mechanism enhances selectivity for stimulus features such as orientation, direction, and spatial frequency.

One of the more prosaic but necessary features of almost any information processing system is gain control. All such systems must have some way to adjust the relationship between input, which can vary dramatically depending on changes in the environment, and output, which is almost always required to remain within a limited range of amplitudes. While the volume control on a radio or the brightness control on a computer monitor are not the most exciting or highly touted features, imagine such devices without these forms of gain control. Many an engineer can attest to the large effort required to design automatic gain controls in telephones, cameras, and radio transmitters.

Intracellular recordings from simple cells of the cat visual cortex were used to test linear models for the generation of selectivity for the direction of visual motion. Direction selectivity has been thought to arise in part from nonlinear processes, as suggested by previous experiments that were based on extracellular recordings of action potentials. In intracellular recordings, however, the fluctuations in membrane potential evoked by moving stimuli were accurately predicted by the linear summation of responses to stationary stimuli. Nonlinear mechanisms were not required.

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.

Neurons of the primary visual cortex of most mammals are highly selective for the orientation of a contour in visual space. Many neurons are also selective for the direction of motion and for binocular disparity (stimulus depth). In this article we describe these receptive field properties and possible mechanisms by which the cortical circuitry creates them.

X- and Y-mediated input to areas 17 and 18 of the cat visual cortex was studied using current-source-density analysis of field potentials evoked by stimulation of the optic nerves. A cuff-shaped electrode was used for stimulation so that Y axons, by virtue of their larger diameters, would have lower electrical thresholds than X axons. The effect in each cortical area of activating Y axons alone could therefore be determined by low-&litude stimulation of the optic nerves. Current-source densities were calculated by two separate methods. (1) In five experiments, field potentials were measured sequentially at different cortical depths with a single tungsten electrode. Current densities were then calculated by computer. (2) In two experiments, current densities were derived in real time from field potentials recorded simultaneously from three sites with a multi-electrode probe. The calculation was performed by an analog circuit specially designed for this purpose. This method has several advantages over the standard, single-electrode method. At stimulus strengths sufficient to activate the majority of Y axons in the optic nerves, but subthreshold to most X axons, the field potentials evoked in area 17 changed little from layer to layer. When the current-source-density analysis was applied to these potentials, no significant sources or sinks were detectable. Only when the stimulus strength was raised to the point that both X and Y axons were activated by the stimulus were any current sources or sinks detected in area 17. The currents were similar in time course and laminar pattern to those recorded after stimulation of the optic chiasm. In area 18, large sources and sinks were evoked by stimulation of Y axons alone. These currents changed little when the stimulus strength was increased to activate X axons as well. Area 18, therefore, in contrast to area 17, seems to be dominated by Y input and receives little X input. These results support the conclusions of the accompanying paper in which synaptic potentials were recorded intracellularly from cortical neutrons. The intracellular experiments failed to show substantial Y input to area 17. The projections of X and Y axons may therefore be much more highly segregated into areas 17 and 18 than previously thought. Alternatively, the nature of the Y input to area 17 may be very different from that to area 18 in that it cannot be easily detected with intracellular or current-source-density techniques.

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.

Neurons in primary visual cortex exhibit several nonlinearities in their responses to visual stimuli, including response decrements to repeated stimuli, contrast-dependent phase advance, contrast saturation, and cross-orientation suppression. Thalamocortical synaptic depression has been implicated in these phenomena but has not been examined directly in visual cortexin vivo. We assessed depression of visual thalamocortical synapsesin vivousing 20-100 Hz trains of electrical stimuli delivered to the LGN. Cortical cells receiving direct input from the LGN, identified by short latency and low jitter of LGN-evoked PSPs, showed moderate reductions in PSP amplitude during the fastest trains. Cells receiving indirect input from the thalamus via other cortical excitatory neurons show a marked reduction in PSP amplitude during a train, which could be explained either by synaptic depression in corticocortical synapses or by an inhibition-mediated suppression of the firing of their afferents. Reducing spontaneous activity in the LGN (by retinal blockade) unmasked additional depression at the thalamocortical synapse but only for the first stimulus in the train. That is, the first PSP was increased in amplitude relative to the unblocked condition, but subsequent responses were essentially unchanged. Thus, the synapses are maintained at significant levels of depression by spontaneous activity. These findings constrain the role that thalamocortical depression can play in shaping cortical responses to visual stimuli.

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.

We have examined the spatial integration properties of complex cells to determine whether some of their responses can be described by a maximum operation (MAX)-like computation, as suggested by Riesenhuber and Poggio's model of object recognition. Membrane potential was recorded from anesthetized cats while optimally oriented bars were presented, either alone or in pairs, in different parts of the cells' receptive field. In most cells, the membrane potential response to two bars presented simultaneously could not be predicted by the sum of the responses to individual bars. In many cells, however, the responses closely approximated a MAX-like model. That is, the response of the cell to two bars was similar to the larger of the two individual responses (“soft-MAX”). The degree of nonlinear summation varied from cell to cell and varied within single cells from one stimulus configuration to another but on average fit most closely to the MAX model. The firing response of the cells was also well predicted by the MAX-like model. The MAX-like behavior was independent of the distance between the bars (orthogonal to the preferred orientation), independent of the relative amplitude of the responses, and slightly less pronounced at low levels of contrast. This MAX-like behavior of a subset of complex cells may play an important role in invariant object recognition in clutter.

The origin of orientation selectivity in the responses of simple cells in cat visual cortex serves as a model problem for understanding cortical circuitry and computation. The feedforward model of Hubel and Wiesel [1] posits that this selectivity arises simply from the spatial organization of the receptive fields of thalamic inputs synapsing on each simple cell. Much evidence, including a number of recent intracellular studies, supports a primary role of the thalamic inputs in determining simple-cell response properties including orientation tuning. And yet, while the feedforward model seems to explain the broad outline of simple-cell properties, there are number of detailed aspects of the behavior of simple cells that have appeared not to be accounted for by the feedforward model. These properties include contrast invariance of orientation tuning, the exact relationship between receptive field geometry and orientation tuning, and the dynamics of orientation tuning. The apparent failures of the feedforward model have prompted the development of a class of models that rely on feedback circuitry within the cortex: Properly arranged feedback from excitatory connections within an orientation column and inhibitory connections to adjacent columns can account for many of the properties that the feedforward models miss. The feedforward and feedback models are different enough in character that they make radically different predictions about the nature of the computation performed by the cortex, the feedforward models acting more like passive filters, and the feedback models more actively shaping the representation of the retinal image. I will review a series of experiments designed to test these two models, and present evidence that the feedforward models, with little modification, can account in detail for the behavior of cortical simple cells.

Neurons in the primary visual cortex are highly selective for stimulus orientation, whereas their thalamic inputs are not. Much controversy has been focused on the mechanism by which cortical orientation selectivity arises. Although an increasing amount of evidence supports a linear model in which orientation selectivity is conferred upon visual cortical cells by the alignment of the receptive fields of their thalamic inputs, the controversy has recently been rekindled with the suggestion that late cortical input--delayed by multiple synapses--could lead to sharpening of orientation selectivity over time. Here we used intracellular recordings in vivo to examine temporal properties of the orientation-selective response to flashed gratings. Bayesian parameter estimation demonstrated that both preferred orientation and tuning width were stable throughout the response to a single stimulus.

Spike responses for many cells of cat primary visual cortex are optimized for the length of a drifting grating stimulus. Stimuli that are longer or shorter than this optimal length elicit submaximal spike responses. To investigate the mechanisms responsible for this length tuning, we have recorded intracellularly from visual cortical neurons in the cat while presenting drifting grating stimuli of varying lengths. We have found that the membrane potential responses of the cells also exhibit length tuning, but that the suppression of spike responses at lengths longer than the preferred is 30-50% stronger than the corresponding suppression of the membrane potential responses. This difference may be attributed to the effects of spike threshold. Furthermore, using steady injected currents, we have measured changes in the excitatory and inhibitory components of input conductance evoked by stimuli of different lengths. We find that, compared with optimal stimuli, long stimuli evoke both an increase in inhibitory conductance and a decrease in excitatory conductance. These two mechanisms differ in their contrast sensitivity, resulting in stronger end stopping and shorter optimal lengths for high-contrast stimuli. These patterns suggest that response suppression for long stimuli is generated by a combination of active inhibition from stimuli outside the excitatory receptive field, as well as decreased excitation from other cortical cells that are themselves end-inhibited.

A neuron can receive thousands of inputs that, together, tell it when to fire. New techniques can image the activity of many inputs, and shed light on how single neurons perform computations in response.

The origin of orientation selectivity in the responses of simple cells in cat visual cortex serves as a model problem for understanding cortical circuitry and computation. The feed-forward model posits that this selectivity arises simply from the arrangement of thalamic inputs to a simple cell. Much evidence, including a number of recent intracellular studies, supports a primary role of the thalamic inputs in determining simple cell response properties, including orientation tuning. This mechanism alone, however, cannot explain the invariance of orientation tuning to changes in stimulus contrast. Simple cells receive push-pull inhibition: ON inhibition in OFF subregions and vice versa. Addition of such inhibition to the feed-forward model can account for this contrast invariance, provided the inhibition is sufficiently strong. The predictions of “normalization” and “feedback” models are reviewed and compared with the predictions of this modified feed-forward model and with experimental results. The modified feed-forward and the feedback models ascribe fundamentally different functions to cortical processing.

We have investigated the relationship between membrane potential and firing rate in cat visual cortex and found that the spike threshold contributes substantially to the sharpness of orientation tuning. The half-width at half-height of the tuning of the spike responses was 23 ± 8°, compared with 38 ± 15° for the membrane potential responses. Direction selectivity was also greater in spike responses (direction index, 0.61 ± 0.35) than in membrane potential responses (0.28 ± 0.21).Threshold also increased the distinction between simple and complex cells, which is commonly based on the linearity of the spike responses to drifting sinusoidal gratings. In many simple cells, such stimuli evoked substantial elevations in the mean potential, which are nonlinear. Being subthreshold, these elevations would be hard to detect in the firing rate responses. Moreover, just as simple cells displayed various degrees of nonlinearity, complex cells displayed various degrees of linearity.We fitted the firing rates with a classic rectification model in which firing rate is zero at potentials below a threshold and grows linearly with the potential above threshold. When the model was applied to a low-pass-filtered version of the membrane potential (with spikes removed), the estimated values of threshold (−54.4 ± 1.4 mV) and linear gain (7.2 ± 0.6 spikes · sec−1 · mV−1) were similar across the population. The predicted firing rates matched the observed firing rates well and accounted for the sharpening of orientation tuning of the spike responses relative to that of the membrane potential.As it was for stimulus orientation, threshold was also independent of stimulus contrast. The rectification model accounted for the dependence of spike responses on contrast and, because of a stimulus-induced tonic hyperpolarization, for the response adaptation induced by prolonged stimulation. Because gain and threshold are unaffected by visual stimulation and by adaptation, we suggest that they are constant under all conditions.

The responsiveness of neurons in the primary visual cortex (V1) is substantially reduced after a few seconds of visual stimulation with an effective pattern. This phenomenon, called pattern adaptation, is uniquely cortical and is the likely substrate of a variety of perceptual after-effects. While adaptation to a given pattern reduces the responses of V1 neurons to all subsequently viewed test patterns, this reduction shows some specificity, being strongest when the adapting and test patterns are identical. This specificity may indicate that adaptation affects the interaction between groups of neurons that are jointly activated by the adapting stimulus. We investigated this possibility by studying the effects of adaptation to visual patterns containing one or both of two orientations--the preferred orientation for a cell, and the orientation orthogonal to it. Because neurons in the primary visual cortex are sharply tuned for orientation, stimulation with orthogonal orientations excites two largely distinct populations of neurons. With intracellular recordings of the membrane potential of cat V1 neurons, we found that adaptation to the orthogonal orientation alone does not evoke the hyperpolarization that is typical of adaptation to the preferred orientation. With extracellular recordings of the firing rate of macaque V1 neurons, we found that the responses were not reduced by adaptation to the orthogonal orientation alone nearly as much as by adaptation to the preferred orientation. In the macaque we also studied the effects of adaptation to plaids containing both the preferred and the orthogonal orientations. We found that adaptation to these stimuli could modify the interactions between orientations. It increased the amount of cross-orientation suppression displayed by some cells, even turning some cells that showed cross-orientation facilitation when adapted to a blank stimulus into cells that show cross-orientation suppression. This result suggests that pattern adaptation can affect the interaction between the groups of neurons tuned to the orthogonal orientations, either by increasing their mutual inhibition or by decreasing their mutual excitation.

Is thalamic input to the visual cortex strong and well tuned for orientation, as predicted by Hubel and Wiesel's (1962) model of orientation selectivity in simple cells? We directly measured the size of the thalamic input to single simple cells intracellularly by combining electrical stimulation of the cortex with a briefly flashed visual stimulus. In nearby cells, the electrical stimulation evoked a long-lasting inhibition that prevented them from firing in response to the visual stimulus. The visually evoked excitatory postsynaptic potentials (EPSPs) recorded during the period of cortical suppression, therefore, reflected largely the thalamic input. In 16 neurons that received monosynaptic input from the thalamus, cortical suppression left 46% of normal visual response on average (12%–86% in range). In those cells tested, this remaining visual response was as well tuned for orientation as the normal response to the visual stimulus alone. We conclude that the thalamic input to cortical simple cells with monosynaptic input from the thalamus is strong and well tuned in orientation, and that the intracortical input does not appear to sharpen orientation tuning in these cells.

The firing rate responses of neurons in the primary visual cortex grow with stimulus contrast, the variation in the luminance of an image relative to the mean luminance. These responses, however, are reduced after a cell is exposed for prolonged periods to high-contrast visual stimuli. This phenomenon, known as contrast adaptation, occurs in the cortex and is not present at earlier stages of visual processing. To investigate the cellular mechanisms underlying cortical adaptation, intracellular recordings were performed in the visual cortex of cats, and the effects of prolonged visual stimulation were studied. Surprisingly, contrast adaptation barely affected the stimulus-driven modulations in the membrane potential of cortical cells. Moreover, it did not produce sizable changes in membrane resistance. The major effect of adaptation, evident both in the presence and in the absence of a visual stimulus, was a tonic hyperpolarization. Adaptation affects a class of synaptic inputs, most likely excitatory in nature, that exert a tonic influence on cortical cells.

The electrical activity of neurons triggered by a stimulus is characterized by a large component of noise. In his Perspective, Ferster discusses new results which reveal that, in the cortex of the brain, noise is not just noise at all, but contains information reflecting the activity in other parts of the brain. Optical imaging of cortical neurons by Arieli et al ., reported in this week9s issue (p. 1868), provides this new conclusion about neural noise.

How does the brain code information? Ferster and Spruston weigh the evidence in favor of a rate code (that the rate of action-potential firing carries the key information) and a temporal code (in which the pattern of firing is crucial, not just the rate).

Postsynaptic inhibition can operate by two distinct mechanisms: (1) membrane hyperpolarization and (2) shunting of excitatory postsynaptic currents. The arithmetic operations--either addition or multiplication-- that synapses are able to perform during neuronal computations are determined by which of these two inhibitory mechanisms predominates. Hyperpolarizing IPSPs interact linearly with EPSPs; their negative and positive synaptic currents sum to produce a net change in membrane potential (Eccles, 1961). Shunting synapses interact nonlinearly with EPSPs; the shunt-induced increase in membrane conductance directly reduces the amplitude of EPSPs by a constant multiplicative factor (Fatt and Katz, 1953; Blomfield, 1974). This property of shunting inhibition has provided the basis for models of synaptic interaction in which shunting inhibition acts as an AND-NOT gate for excitatory inputs (Torre and Poggio, 1978; Koch et al., 1983). Using an in vivo variant of the whole-cell patch technique (Blanton et al., 1989), we have examined the effect of visually evoked inhibition on the size of EPSPs in cortical simple cells and found that the predominant inhibitory mechanism is hyperpolarization. We conclude that these inhibitory synapses operate primarily in the linear mode.

Publisher Summary This chapter focuses on a subset of cortical neurons—the simple cells of layer 4 in the visual cortex of the cat. Simple cells are the primary recipient of the major visual input from the thalamus—the relay cells of the lateral geniculate nucleus (LGN). Simple cells are highly sensitive to the orientation, direction and speed of motion, retinal disparity (depth), and spatial frequency (size) of a visual stimulus, while geniculate relay cells are largely insensitive to these stimulus features. The synapse between geniculocortical axons and the simple cells of layer 4 is, therefore, the point at which many of the interesting features of cortical neurons are generated for the first time. As a result, simple cells have been the focus of many models of cortical organization and function, some of which can be tested with intracellular methods. Intracellular recording has been employed in two types of experiments: with electrical stimulation of the visual pathways or with visual stimulation.

1. Nonlinearity of spatial summation in areas 17 and 18 of cat visual cortex was compared with the type of spatial nonlinearity that differentiates X and Y cells in the lateral geniculate nucleus (LGN) and retina. The comparisons were made to examine to what extent the information from X and Y cells may remain separated in higher visual centers. 2. Responses of simple cells in areas 17 and 18 were recorded while stationary, optimally oriented sinewave gratings were sinusoidally modulated within the receptive field of the cell. Both the spatial frequency and spatial phase of the stimulus were varied. 3. Y cells in the retina and LGN are defined by the presence of a specific form of spatial nonlinearity. When tested with contrast-modulated sinewave gratings of spatial frequencies about three-fold greater than the optimal, their responses are dominated by a frequency-doubled component. The amplitude of the frequency-doubled component is not dependent on the spatial phase of the stimulus. 4. Many simple cells in the cortex showed a form of spatial nonlinearity similar to the defining nonlinearity found in retinal and geniculate Y cells. A frequency-doubled response dominated at spatial frequencies more than threefold greater than the optimal spatial frequency. When this response was present, it was phase independent. 5. More than 50% of the simple cells in area 18 showed the Y-like spatial nonlinearity. Fewer than 10% of the simple cells in area 17 showed the Y-like spatial nonlinearity. 6. The virtual absence of Y-like nonlinearity in area 17 and its relative abundance in area 18 suggest that the functional separation between the parallel X and Y pathways remains distinct within areas 17 and 18 of cat visual cortex.

When a cortical neuron receives synaptic input from both eyes, do the synaptic pathways that mediate the input from each eye match? In this study, inputs from the two eyes were compared by measuring the latencies of EPSPs and IPSPs evoked by electrical stimulation of the two optic nerves. For binocular neurons, these latencies invariably matched closely, indicating that the pathways from the two eyes contain the same number of synapses; monosynaptic input from lamina A of the lateral geniculate nucleus (LGN) is always matched by monosynaptic input from lamina A1. Conversely, polysynaptic input from one eye, either excitatory or inhibitory, is invariably accompanied by similar input from the other eye. In addition, the match between the two eyes in latency indicates that for each eye a synaptic potential is mediated by the same type of afferent, either X or Y.Judging from intracellular recording, 75% of the neurons studied were binocular, that is, EPSPs could be evoked from either eye. In the remaining 25%, EPSPs could be evoked from only one eye, in agreement with extracellular receptive field studies in which 30% of cortical neurons are monocular.

In order to sense visual motion, the brain must determine not only where an object is, but where it was a few moments ago. To do so, the brain may depend in part on a small number of curiously shaped neurons deep in the visual cortex.

This study presents evidence that the X- and Y-cells described physiologically in the A laminae of the cat's dorsal lateral geniculate nucleus (LGN) are two morphologically distinct cell types recognizable in Golgi preparations. It is shown firstly that the three cell types seen in Golgi preparations of the A laminae (large and medium-sized principal cells and small interneurons-types 1,2 and 3 in the classification of Guillery, '66) may be identified in 1-mum Epon sections of osmicated material. While cell-diameter histograms prepared from serial 1-mum sections show a unimodal distribution of cell sizes, three populations can be distinguished if attention is paid to the presence or absence of large cytoplasmic inclusions (laminar bodies). These three populations consist of large cells lacking laminar bodies (Class I), medium-sized cells possessing laminar bodies (Class II) and small cells lacking them (Class III). That these three classes correspond to the three morphological types has been shown by (i) size comparisons, and (ii) direct demonstration of laminar bodies in the Golgi-impregnated cell bodies of Guillery's type 2 cells. Histograms prepared in this way for samples taken at various positions in the LGN show that the numbers of class II cells decline from the representation of the area centralis to the monocular segment. This decline is compensated by a corresponding rise in the numbers of class I cells. This pattern of distribution is similar to the physiologically observed distribution of X- and Y-cells, indicating that X-cells are likely to be class II cells and Y-cells class I cells. The cortical projections of the various cell types have been examined by the horseradish peroxidase method. Class II cells project to area 17 only. Most class I cells also project to area 17 only, but a few very large class I cells project to area 18. From our results, it appears that very few if any cells in the A laminae have branching axons supplying both 17 and 18. The class III cells do not project to the visual cortex, a finding consistent with their identification as interneurons. Class I and II cells are also found in lamina C and in the MIN. In both these regions there is a predominance of very large class I cells, which project to area 18. Laminae Cl-C3 contain small cells lacking laminar bodies. These cells may project to both areas 17 and 18 with branching axons. They are likely to correspond to Guillery's type 4 cells (small relay cells confined to the C laminae) and to the physiologically described W-cells. Long-term monocular deprivation causes cell shrinkage which is much more severe for class I than for class II cells. There is in addition a decrease in the relative numbers of class I cells. This decrease is found in binocular deprivation also. These observations provide an anatomical basis for the reported loss of Y-cells from deprived laminae of the LGN. It is suggested that the effects of deprivation on Y-cells may be accounted for in terms of competition for synaptic space.

The receptive fields of simple cells in the cat visual cortex are, by definition, divided into ON and OFF subfields. There is little doubt that each subfield is generated by excitatory input from geniculate neurons of the appropriate center type: ON subfields by ON-center cells, and OFF subfields by OFF-center cells. In intracellular records, ON subfields can be detected as regions in which light elicits a barrage of EPSPs, while in OFF subfields, turning a light off does the same. In addition, visual stimuli can evoke strong IPSPs, but these IPSPs have a receptive field spatially opponent to that of the EPSPs: Inhibition is evoked by turning a light off in an ON region and turning a light on in an OFF region. This inhibition probably arises from other cortical simple cells, and may contribute to such receptive-field properties as antagonism between subfields, binocular disparity sensitivity, and orientation selectivity.