A fast and simple population code for orientation in primate V1

Orientation tuning has been a classic model for understanding single-neuron computation in the neocortex. However, little is known about how orientation can be read out from the activity of neural populations, in particular in alert animals. Our study is a first step toward that goal. We recorded from up to 20 well isolated single neurons in the primary visual cortex of alert macaques simultaneously and applied a simple, neurally plausible decoder to read out the population code. We focus on two questions: First, what are the time course and the timescale at which orientation can be read out from the population response? Second, how complex does the decoding mechanism in a downstream neuron have to be to reliably discriminate between visual stimuli with different orientations? We show that the neural ensembles in primary visual cortex of awake macaques represent orientation in a way that facilitates a fast and simple readout mechanism: With an average latency of 30-80 ms, the population code can be read out instantaneously with a short integration time of only tens of milliseconds, and neither stimulus contrast nor correlations need to be taken into account to compute the optimal synaptic weight pattern. Our study shows that-similar to the case of single-neuron computation-the representation of orientation in the spike patterns of neural populations can serve as an exemplary case for understanding the computations performed by neural ensembles underlying visual processing during behavior.

Fast recruitment of recurrent inhibition in the cat visual cortex

Neurons of the same column in L4 of the cat visual cortex are likely to share the same sensory input from the same region of the visual field. Using visually-guided patch clamp recordings we investigated the biophysical properties of the synapses of neighboring layer 4 neurons. We recorded synaptic connections between all types of excitatory and inhibitory neurons in L4. The E–E, E–I, and I–E connections had moderate CVs and failure rates. However, E–I connections had larger amplitudes, faster rise-times, and shorter latencies. Identification of the sites of putative synaptic contacts together with compartmental simulations on 3D reconstructed cells, suggested that E–I synapses tended to be located on proximal dendritic branches, which would explain their larger EPSP amplitudes and faster kinetics. Excitatory and inhibitory synapses were located at the same distance on distal dendrites of excitatory neurons. We hypothesize that this co-localization and the fast recruitment of local inhibition provides an efficient means of modulating excitation in a precisely timed way.

[Specific modulation of neuronal responses to light of different intensities by sound in the rabbit's primary visual cortex]

Changes in activity of 92 neurons in the primary visual cortex of four rabbits (Orictolagus cuniculus) were analyzed. In the first series of experiments, we recorded discharges of 63 neurons in response to replacement of visual stimuli in pairs (pairs of 0.28 - 1, 1 - 3, 3 - 6, 6 - 8.5, 8.5 - 14, 14 - 17, 17 - 20 cd/m2). Then the same stimuli were presented simultaneously with sound (70 dB, 2000 Hz, 40 ms). Neurons did not respond directly to the sound. Two groups of neurons were found. In the first group of neurons (31%), responses to the complex "light and sound" (40-100 ms from the moment of substitution of stimuli) increased on average by 41% (p < 0.0001) under conditions of the lowest stimuli intensities. With increasing light intensities, discharges to the complex were reduced to the background level of responses to light and even lower. The second group of neurons (19%) showed the opposite properties: at low intensities, responses to the complex were comparable to responses to light (or even lower). At high intensities (14-20 cd/m2), discharges to the complex were significantly (p < 0.05) different from the responses to light (20% and higher, up to 39%). In the second series of experiments, we reconstructed vector sensory spaces on the basis of responses of 29 neurons to light of different intensities and eight complexes of "light and sound." It was found that the sound had also a dual effect on the sensory space of complexes. Some neurons showed an enhancement of the angular distance between the two lowest light intensities (0.28 and 1 cd/m2). Other neurons showed an increase in the angular distance between the highest intensities. Such changes in the space structure are consistent with the groups of neurons revealed in the first two series of the experiments. Comparison of the dynamics of neuronal responses and the amplitudes of evoked potentials under the same conditions of stimulation revealed their considerable similarity. Thus, modulation of neuronal activity in the visual cortex by sound is a complex nonlinear process.

Fast coding of orientation in primary visual cortex

Understanding how populations of neurons encode sensory information is a major goal of systems neuroscience. Attempts to answer this question have focused on responses measured over several hundred milliseconds, a duration much longer than that frequently used by animals to make decisions about the environment. How reliably sensory information is encoded on briefer time scales, and how best to extract this information, is unknown. Although it has been proposed that neuronal response latency provides a major cue for fast decisions in the visual system, this hypothesis has not been tested systematically and in a quantitative manner. Here we use a simple ‘race to threshold’ readout mechanism to quantify the information content of spike time latency of primary visual (V1) cortical cells to stimulus orientation. We find that many V1 cells show pronounced tuning of their spike latency to stimulus orientation and that almost as much information can be extracted from spike latencies as from firing rates measured over much longer durations. To extract this information, stimulus onset must be estimated accurately. We show that the responses of cells with weak tuning of spike latency can provide a reliable onset detector. We find that spike latency information can be pooled from a large neuronal population, provided that the decision threshold is scaled linearly with the population size, yielding a processing time of the order of a few tens of milliseconds. Our results provide a novel mechanism for extracting information from neuronal populations over the very brief time scales in which behavioral judgments must sometimes be made.

How can humans and animals make complex decisions on time scales as short as 100 ms? The information required for such decisions is coded in neural activity and should be read out on a very brief time scale. Traditional approaches to coding of neural information rely on the number of electrical pulses, or spikes, that neurons fire in a certain time window. Although this type of code is likely to be used by the brain for higher cognitive tasks, it may be too slow for fast decisions. Here, we explore an alternative code which is based on the latency of spikes with respect to a reference signal. By analyzing the simultaneous responses of many cells in monkey visual cortex, we show that information about the orientation of visual stimuli can be extracted reliably from spike latencies on very short time scales.

Properties of V1 neurons tuned to conjunctions of visual features: application of the V1 saliency hypothesis to visual search behavior

From a computational theory of V1, we formulate an optimization problem to investigate neural properties in the primary visual cortex (V1) from human reaction times (RTs) in visual search. The theory is the V1 saliency hypothesis that the bottom-up saliency of any visual location is represented by the highest V1 response to it relative to the background responses. The neural properties probed are those associated with the less known V1 neurons tuned simultaneously or conjunctively in two feature dimensions. The visual search is to find a target bar unique in color (C), orientation (O), motion direction (M), or redundantly in combinations of these features (e.g., CO, MO, or CM) among uniform background bars. A feature singleton target is salient because its evoked V1 response largely escapes the iso-feature suppression on responses to the background bars. The responses of the conjunctively tuned cells are manifested in the shortening of the RT for a redundant feature target (e.g., a CO target) from that predicted by a race between the RTs for the two corresponding single feature targets (e.g., C and O targets). Our investigation enables the following testable predictions. Contextual suppression on the response of a CO-tuned or MO-tuned conjunctive cell is weaker when the contextual inputs differ from the direct inputs in both feature dimensions, rather than just one. Additionally, CO-tuned cells and MO-tuned cells are often more active than the single feature tuned cells in response to the redundant feature targets, and this occurs more frequently for the MO-tuned cells such that the MO-tuned cells are no less likely than either the M-tuned or O-tuned neurons to be the most responsive neuron to dictate saliency for an MO target.

A model of the response of visual area V2 to combinations of orientations

The role of the V2 area in visual processing is still almost entirely unexplored. Recently, several studies revealed the tuning of V2 neurons in the macaque to stimuli consisting of two segments with different orientations. By measuring orientation tuning inside subunits of the overall receptive field, units with non uniform orientation selectivity have been found. In this work, the emergence of a computational organization supporting similar responses is explored, using an artificial model of cortical maps. This model, called LISSOM (Laterally Interconnected Synergetically Self-Organizing Map) includes excitatory and inhibitory lateral connections. In this simulation two LISSOM maps are arranged as V1 and V2 areas. In the first area, the classical domains of orientation selectivity develop, while in V2 most neurons become sensitive to pairs of orientations. The overall activation of these units depend on the presence of oriented segments at a finer grain than the whole receptive fields, with complex nonlinear interactions.

Color tuning in alert macaque V1 assessed with fMRI and single-unit recording shows a bias toward daylight colors

Colors defined by the two intermediate directions in color space, "orange-cyan" and "lime-magenta," elicit the same spatiotemporal average response from the two cardinal chromatic channels in the lateral geniculate nucleus (LGN). While we found LGN functional magnetic resonance imaging (fMRI) responses to these pairs of colors were statistically indistinguishable, primary visual cortex (V1) fMRI responses were stronger to orange-cyan. Moreover, linear combinations of single-cell responses to cone-isolating stimuli of V1 cone-opponent cells also yielded stronger predicted responses to orange-cyan over lime-magenta, suggesting these neurons underlie the fMRI result. These observations are consistent with the hypothesis that V1 recombines LGN signals into "higher-order" mechanisms tuned to noncardinal color directions. In light of work showing that natural images and daylight samples are biased toward orange-cyan, our findings further suggest that V1 is adapted to daylight. V1, especially double-opponent cells, may function to extract spatial information from color boundaries correlated with scene-structure cues, such as shadows lit by ambient blue sky juxtaposed with surfaces reflecting sunshine.

Lumping and splitting the neural circuitry of visual attention

Shifts of gaze and of covert attention rely on tightly linked yet divergent neural mechanisms. In this issue of Neuron, Gregoriou et al. (2012) provide interesting evidence that different functional classes of neurons within the frontal eye field contribute uniquely to these two functions.

The mechanism of orientation selectivity in primary visual cortex without a functional map

Neurons in primary visual cortex (V1) display substantial orientation selectivity even in species where V1 lacks an orientation map, such as in mice and rats. The mechanism underlying orientation selectivity in V1 with such a salt-and-pepper organization is unknown; it is unclear whether a connectivity that depends on feature similarity is required, or a random connectivity suffices. Here we argue for the latter. We study the response to a drifting grating of a network model of layer 2/3 with random recurrent connectivity and feedforward input from layer 4 neurons with random preferred orientations. We show that even though the total feedforward and total recurrent excitatory and inhibitory inputs all have a very weak orientation selectivity, strong selectivity emerges in the neuronal spike responses if the network operates in the balanced excitation/inhibition regime. This is because in this regime the (large) untuned components in the excitatory and inhibitory contributions approximately cancel. As a result the untuned part of the input into a neuron as well as its modulation with orientation and time all have a size comparable to the neuronal threshold. However, the tuning of the F0 and F1 components of the input are uncorrelated and the high-frequency fluctuations are not tuned. This is reflected in the subthreshold voltage response. Remarkably, due to the nonlinear voltage-firing rate transfer function, the preferred orientation of the F0 and F1 components of the spike response are highly correlated.

A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model

Simple cells in primary visual cortex are believed to extract local contour information from a visual scene. The 2D Gabor function (GF) model has gained particular popularity as a computational model of a simple cell. However, it short-cuts the LGN, it cannot reproduce a number of properties of real simple cells, and its effectiveness in contour detection tasks has never been compared with the effectiveness of alternative models. We propose a computational model that uses as afferent inputs the responses of model LGN cells with center-surround receptive fields (RFs) and we refer to it as a Combination of Receptive Fields (CORF) model. We use shifted gratings as test stimuli and simulated reverse correlation to explore the nature of the proposed model. We study its behavior regarding the effect of contrast on its response and orientation bandwidth as well as the effect of an orthogonal mask on the response to an optimally oriented stimulus. We also evaluate and compare the performances of the CORF and GF models regarding contour detection, using two public data sets of images of natural scenes with associated contour ground truths. The RF map of the proposed CORF model, determined with simulated reverse correlation, can be divided in elongated excitatory and inhibitory regions typical of simple cells. The modulated response to shifted gratings that this model shows is also characteristic of a simple cell. Furthermore, the CORF model exhibits cross orientation suppression, contrast invariant orientation tuning and response saturation. These properties are observed in real simple cells, but are not possessed by the GF model. The proposed CORF model outperforms the GF model in contour detection with high statistical confidence (RuG data set: p<10(-4), and Berkeley data set: p<10(-4)). The proposed CORF model is more realistic than the GF model and is more effective in contour detection, which is assumed to be the primary biological role of simple cells.

[Alpha- and beta-oscillations of dynamics of the area and weight of receptive fields of cat striate neurons under conditions of classical and combined mapping]

In 22 acute experiments in anesthetized and immobilized adult cats the dynamics of 83 receptive fields (RF) of 47 striate neurons was studied by temporal slices method. Classical mapping revealed wave-like changes in the area and weight of neuronal RFs. Special mathematical analysis showed that such changes represented a sum of a slow non-oscillatory and comparatively fast components. The slow component was a biphasic up and down RF dynamics. In most cases, the oscillation frequencies were within the alpha- and beta- EEG frequency ranges. When the RF center was activated additionally during combined mapping, the oscillations frequencies remained unchanged, but the duration and amplitude of non-oscillatory component substantially decreased. Mechanisms underlying the RF dynamics and its functional significance are discussed.

A tritan Waldo would be easier to detect in the periphery than a red/green one: evidence from visual search

In a color naming task from 0° to 55° eccentricity, we found that red/green performance (n=10 subjects) declines around 40° eccentricity, 5° earlier than does tritan performance (main effect of color, p=0.009; eccentricity, p<0.001; interaction, p=0.005). In a feature visual search task (e.g., red target dot among green distractor dots; twelve 2.5° diameter dots; 0, 20, and 45° eccentricity; 12 subjects), performance was significantly more impaired for red/green than for tritan stimuli, especially in the periphery (main effect of color, p=0.007; eccentricity, p<0.001; interaction, p=0.003). This effect occurred even following a rod bleach. Our results are consistent with influences from both the retina (especially random rather than selective peripheral cone input to midget ganglion cells for red/green perception, and selective cone input to small bistratified cells for tritan perception) and the cortex (differential cortical magnification across the two chromatic axes).

Persistent cortical plasticity by upregulation of chondroitin 6-sulfation

Cortical plasticity is most evident during a critical period in early life, but the mechanisms that restrict plasticity after the critical period are poorly understood. We found that a developmental increase in the 4-sulfation/6-sulfation (4S/6S) ratio of chondroitin sulfate proteoglycans (CSPGs), which are components of the brain extracellular matrix, leads to the termination of the critical period for ocular dominance plasticity in the mouse visual cortex. Condensation of CSPGs into perineuronal nets that enwrapped synaptic contacts on parvalbumin-expressing interneurons was prevented by cell-autonomous overexpression of chondroitin 6-sulfation, which maintains a low 4S/6S ratio. Furthermore, the increase in the 4S/6S ratio was required for the accumulation of Otx2, a homeoprotein that activates the development of parvalbumin-expressing interneurons, and for functional maturation of the electrophysiological properties of these cells. Our results indicate that the critical period for cortical plasticity is regulated by the 4S/6S ratio of CSPGs, which determines the maturation of parvalbumin-expressing interneurons.

Getting a handle on how the brain generates complexity

Sensory processing in cortex across modalities appears to rely on a "simple-to-complex" hierarchical computational strategy in which neurons at later levels in the hierarchy combine inputs from earlier levels to create more complex neuronal selectivities. The specifics of this process are still poorly understood, however. In this issue of Network, Plebe shows how computational modeling of experimental data on neuronal tuning in secondary visual cortex can help us understand how the brain increases neuronal tuning complexity across the visual cortical hierarchy.

Spatiotemporal receptive fields of cells in V1 are optimally shaped for stimulus velocity estimation

In recent literature, particularly interesting stimulus velocity-selective behaviors were found in the response properties of neurons belonging to the primary visual cortex (V1). In this work, 93 simple and complex cell receptive fields were obtained from the recordings of different experiments made on cats (DeAngelis, Blanche, Touryan) with reverse correlation and the spike-triggered covariance methods and then fitted with a three-dimensional Gabor model, so that cells are seen as minimizers of the Heisenberg uncertainty principle over both space and time. Analysis of the model parameters' cortical distribution suggests that V1 is spatiotemporally organized to maximize the resolution on the stimulus velocity measure.

Morphometric variability of nicotinamide adenine dinucleotide phosphate diaphorase neurons in the primary sensory areas of the rat

Even though there is great regional variation in the distribution of inhibitory neurons in the mammalian isocortex, relatively little is known about their morphological differences across areal borders. To obtain a better understanding of particularities of inhibitory circuits in cortical areas that correspond to different sensory modalities, we investigated the morphometric differences of a subset of inhibitory neurons reactive to the enzyme nicotinamide adenine dinucleotide phosphate diaphorase (NADPH-d) within the primary auditory (A1), somatosensory (S1), and visual (V1) areas of the rat. One hundred and twenty NADPH-d-reactive neurons from cortical layer IV (40 cells in each cortical area) were reconstructed using the Neurolucida system. We collected morphometric data on cell body area, dendritic field area, number of dendrites per branching order, total dendritic length, dendritic complexity (Sholl analysis), and fractal dimension. To characterize different cell groups based on morphology, we performed a cluster analysis based on the previously mentioned parameters and searched for correlations among these variables. Morphometric analysis of NADPH-d neurons allowed us to distinguish three groups of cells, corresponding to the three analyzed areas. S1 neurons have a higher morphological complexity than those found in both A1 and V1. The difference among these groups, based on cluster analysis, was mainly related to the size and complexity of dendritic branching. A principal component analysis (PCA) applied to the data showed that area of dendritic field and fractal dimension are the parameters mostly responsible for dataset variance among the three areas. Our results suggest that the nitrergic cortical circuitry of primary sensory areas of the rat is differentially specialized, probably reflecting peculiarities of both habit and behavior of the species.

The organization of the human cerebellum estimated by intrinsic functional connectivity

The cerebral cortex communicates with the cerebellum via polysynaptic circuits. Separate regions of the cerebellum are connected to distinct cerebral areas, forming a complex topography. In this study we explored the organization of cerebrocerebellar circuits in the human using resting-state functional connectivity MRI (fcMRI). Data from 1,000 subjects were registered using nonlinear deformation of the cerebellum in combination with surface-based alignment of the cerebral cortex. The foot, hand, and tongue representations were localized in subjects performing movements. fcMRI maps derived from seed regions placed in different parts of the motor body representation yielded the expected inverted map of somatomotor topography in the anterior lobe and the upright map in the posterior lobe. Next, we mapped the complete topography of the cerebellum by estimating the principal cerebral target for each point in the cerebellum in a discovery sample of 500 subjects and replicated the topography in 500 independent subjects. The majority of the human cerebellum maps to association areas. Quantitative analysis of 17 distinct cerebral networks revealed that the extent of the cerebellum dedicated to each network is proportional to the network's extent in the cerebrum with a few exceptions, including primary visual cortex, which is not represented in the cerebellum. Like somatomotor representations, cerebellar regions linked to association cortex have separate anterior and posterior representations that are oriented as mirror images of one another. The orderly topography of the representations suggests that the cerebellum possesses at least two large, homotopic maps of the full cerebrum and possibly a smaller third map.

A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields

Sparse coding algorithms trained on natural images can accurately predict the features that excite visual cortical neurons, but it is not known whether such codes can be learned using biologically realistic plasticity rules. We have developed a biophysically motivated spiking network, relying solely on synaptically local information, that can predict the full diversity of V1 simple cell receptive field shapes when trained on natural images. This represents the first demonstration that sparse coding principles, operating within the constraints imposed by cortical architecture, can successfully reproduce these receptive fields. We further prove, mathematically, that sparseness and decorrelation are the key ingredients that allow for synaptically local plasticity rules to optimize a cooperative, linear generative image model formed by the neural representation. Finally, we discuss several interesting emergent properties of our network, with the intent of bridging the gap between theoretical and experimental studies of visual cortex.

In a sparse coding model, individual input stimuli are represented by the activities of model neurons, the majority of which are inactive in response to any particular stimulus. For a given class of stimuli, the neurons are optimized so that the stimuli can be faithfully represented with the minimum number of co-active units. This has been proposed as a model for visual cortex. While it has previously been demonstrated that sparse coding model neurons, when trained on natural images, learn to represent the same features as do neurons in primate visual cortex, it remains to be demonstrated that this can be achieved with physiologically realistic plasticity rules. In particular, learning in cortex appears to occur by the modification of synaptic connections between neurons, which must depend only on information available locally, at the synapse, and not, for example, on the properties of large numbers of distant cells. We provide the first demonstration that synaptically local plasticity rules are sufficient to learn a sparse image code, and to account for the observed response properties of visual cortical neurons: visual cortex actually could learn a sparse image code.

Computational modeling of the dynamics of simple and complex cells in primary visual cortex

We review our work on computational modeling of the mammalian visual cortex. In particular, we explain the network mechanism of how simple and complex cells arise in a large scale neuronal network model of primary visual cortex. The simple cells are so-called because they respond approximately linearly to visual stimulus, whereas the complex cells exhibit nonlinear response to visual stimulation. Our model reproduces qualitatively the experimentally observed distributions of simple and complex cells.

A reaction-diffusion model to capture disparity selectivity in primary visual cortex

Decades of experimental studies are available on disparity selective cells in visual cortex of macaque and cat. Recently, local disparity map for iso-orientation sites for near-vertical edge preference is reported in area 18 of cat visual cortex. No experiment is yet reported on complete disparity map in V1. Disparity map for layer IV in V1 can provide insight into how disparity selective complex cell receptive field is organized from simple cell subunits. Though substantial amounts of experimental data on disparity selective cells is available, no model on receptive field development of such cells or disparity map development exists in literature. We model disparity selectivity in layer IV of cat V1 using a reaction-diffusion two-eye paradigm. In this model, the wiring between LGN and cortical layer IV is determined by resource an LGN cell has for supporting connections to cortical cells and competition for target space in layer IV. While competing for target space, the same type of LGN cells, irrespective of whether it belongs to left-eye-specific or right-eye-specific LGN layer, cooperate with each other while trying to push off the other type. Our model captures realistic 2D disparity selective simple cell receptive fields, their response properties and disparity map along with orientation and ocular dominance maps. There is lack of correlation between ocular dominance and disparity selectivity at the cell population level. At the map level, disparity selectivity topography is not random but weakly clustered for similar preferred disparities. This is similar to the experimental result reported for macaque. The details of weakly clustered disparity selectivity map in V1 indicate two types of complex cell receptive field organization.

Rapid dynamics of contrast responses in the cat primary visual cortex

The visual information we receive during natural vision changes rapidly and continuously. The visual system must adapt to the spatiotemporal contents of the environment in order to efficiently process the dynamic signals. However, neuronal responses to luminance contrast are usually measured using drifting or stationary gratings presented for a prolonged duration. Since motion in our visual field is continuous, the signals received by the visual system contain an abundance of transient components in the contrast domain. Here using a modified reverse correlation method, we studied the properties of responses of neurons in the cat primary visual cortex to different contrasts of grating stimuli presented statically and transiently for 40 ms, and showed that neurons can effectively discriminate the rapidly changing contrasts. The change in the contrast response function (CRF) over time mainly consisted of an increment in contrast gain (CRF shifts to left) in the developing phase of temporal responses and a decrement in response gain (CRF shifts downward) in the decay phase. When the distribution range of stimulus contrasts was increased, neurons demonstrated decrement in contrast gain and response gain. Our results suggest that contrast gain control (contrast adaptation) and response gain control mechanisms are well established during the first tens of milliseconds after stimulus onset and may cooperatively mediate the rapid dynamic responses of visual cortical neurons to the continuously changing contrast. This fast contrast adaptation may play a role in detecting contrast contours in the context of visual scenes that are varying rapidly.

Loss of sensory input causes rapid structural changes of inhibitory neurons in adult mouse visual cortex

A fundamental property of neuronal circuits is the ability to adapt to altered sensory inputs. It is well established that the functional synaptic changes underlying this adaptation are reflected by structural modifications in excitatory neurons. In contrast, the degree to which structural plasticity in inhibitory neurons accompanies functional changes is less clear. Here, we use two-photon imaging to monitor the fine structure of inhibitory neurons in mouse visual cortex after deprivation induced by retinal lesions. We find that a subset of inhibitory neurons carry dendritic spines, which form glutamatergic synapses. Removal of visual input correlates with a rapid and lasting reduction in the number of inhibitory cell spines. Similar to the effects seen for dendritic spines, the number of inhibitory neuron boutons dropped sharply after retinal lesions. Together, these data suggest that structural changes in inhibitory neurons may precede structural changes in excitatory circuitry, which ultimately result in functional adaptation following sensory deprivation.

Experience-dependent expression of miR-132 regulates ocular dominance plasticity

miR-132 is a CREB-induced microRNA that is involved in dendritic spine plasticity. We found that visual experience regulated histone post-translational modifications at a CRE locus that is important for miR-212 and miR-132 cluster transcription, and regulated miR-132 expression in the visual cortex of juvenile mice. Monocular deprivation reduced miR-132 expression in the cortex contralateral to the deprived eye. Counteracting this miR-132 reduction with an infusion of chemically modified miR-132 mimic oligonucleotides completely blocked ocular dominance plasticity.

Sensory competition in the face processing areas of the human brain

The concurrent presentation of multiple stimuli in the visual field may trigger mutually suppressive interactions throughout the ventral visual stream. While several studies have been performed on sensory competition effects among non-face stimuli relatively little is known about the interactions in the human brain for multiple face stimuli. In the present study we analyzed the neuronal basis of sensory competition in an event-related functional magnetic resonance imaging (fMRI) study using multiple face stimuli. We varied the ratio of faces and phase-noise images within a composite display with a constant number of peripheral stimuli, thereby manipulating the competitive interactions between faces. For contralaterally presented stimuli we observed strong competition effects in the fusiform face area (FFA) bilaterally and in the right lateral occipital area (LOC), but not in the occipital face area (OFA), suggesting their different roles in sensory competition. When we increased the spatial distance among pairs of faces the magnitude of suppressive interactions was reduced in the FFA. Surprisingly, the magnitude of competition depended on the visual hemifield of the stimuli: ipsilateral stimulation reduced the competition effects somewhat in the right LOC while it increased them in the left LOC. This suggests a left hemifield dominance of sensory competition. Our results support the sensory competition theory in the processing of multiple faces and suggests that sensory competition occurs in several cortical areas in both cerebral hemispheres.

[Activation of brain structures by fMRI data when viewing the video clips and recall of shown actions]

In 10 healthy volunteers, brain tomograms were recorded using an fMRI scanner with a 3T-field magnet. Emotionally neutral and emotionally involving videos were used for stimulation. Registration consisted of seven series: two series of viewing a fixation point and two kinds of video clips, one series of viewing of a succession of two types of video clips, two series of watching videos with subsequent recall, and two series of viewing the fixation point followed by recall of content online. Viewing content online caused the greatest degree of metabolism increase localized in the visual cortex. This activation was more pronounced during the presentation of an emotionally involving video clip. In addition to the occipital lobe, it actively involved the temporal, parietal and, to a lesser extent, the frontal cortex. Activation of the motor cortex was detected only in some subjects. The "interference" effect in which the increase in metabolism was minimal was observed during a consequent demonstration of video-clips. Immediate recall of video-clips after viewing caused the greatest activation of the posterior frontal and motor cortex. Delayed recall activated mostly the anterior frontal cortex. There was a decrease of metabolism in the visual cortex during the recall. The greatest reduction in the visual cortex was observed during immediate recall. Immediate and delayed recall activated the structures associated with the maintenance of consciousness, memory and a system of mirror neurons.

Fix your eyes in the space you could reach: neurons in the macaque medial parietal cortex prefer gaze positions in peripersonal space

Interacting in the peripersonal space requires coordinated arm and eye movements to visual targets in depth. In primates, the medial posterior parietal cortex (PPC) represents a crucial node in the process of visual-to-motor signal transformations. The medial PPC area V6A is a key region engaged in the control of these processes because it jointly processes visual information, eye position and arm movement related signals. However, to date, there is no evidence in the medial PPC of spatial encoding in three dimensions. Here, using single neuron recordings in behaving macaques, we studied the neural signals related to binocular eye position in a task that required the monkeys to perform saccades and fixate targets at different locations in peripersonal and extrapersonal space. A significant proportion of neurons were modulated by both gaze direction and depth, i.e., by the location of the foveated target in 3D space. The population activity of these neurons displayed a strong preference for peripersonal space in a time interval around the saccade that preceded fixation and during fixation as well. This preference for targets within reaching distance during both target capturing and fixation suggests that binocular eye position signals are implemented functionally in V6A to support its role in reaching and grasping.

[Dynamics of driven rhythm in neuronal assemblies in sensorimotor and visual cerebral cortices in rabbit]

Coincident activity of pairs of neurons in the sensorimotor and visual areas of the cerebral cortex was studied in naive, learning, and trained rabbits during the formation of a hidden excitation focus in their central nervous system (a defensive dominanta) of the rhythmic nature. In the trained rabbits (as compared to the naive animals), percent of neuronal pairs (both neighboring and distant) in whose coincident activity the rhythm of stimulation prevailed was higher. In the visual cortex, percent of such pairs was significantly higher only for the distant neurons. Analysis of interaction between neurons in the visual and sensorimotor cortices revealed increasing the number of neuronal pairs with the driven rhythm while training. Such an increase was observed when both sensorimotor and visual neurons were considered as leading.

The Ising decoder: reading out the activity of large neural ensembles

The Ising model has recently received much attention for the statistical description of neural spike train data. In this paper, we propose and demonstrate its use for building decoders capable of predicting, on a millisecond timescale, the stimulus represented by a pattern of neural activity. After fitting to a training dataset, the Ising decoder can be applied "online" for instantaneous decoding of test data. While such models can be fit exactly using Boltzmann learning, this approach rapidly becomes computationally intractable as neural ensemble size increases. We show that several approaches, including the Thouless-Anderson-Palmer (TAP) mean field approach from statistical physics, and the recently developed Minimum Probability Flow Learning (MPFL) algorithm, can be used for rapid inference of model parameters in large-scale neural ensembles. Use of the Ising model for decoding, unlike other problems such as functional connectivity estimation, requires estimation of the partition function. As this involves summation over all possible responses, this step can be limiting. Mean field approaches avoid this problem by providing an analytical expression for the partition function. We demonstrate these decoding techniques by applying them to simulated neural ensemble responses from a mouse visual cortex model, finding an improvement in decoder performance for a model with heterogeneous as opposed to homogeneous neural tuning and response properties. Our results demonstrate the practicality of using the Ising model to read out, or decode, spatial patterns of activity comprised of many hundreds of neurons.

Denoising two-photon calcium imaging data

Two-photon calcium imaging is now an important tool for in vivo imaging of biological systems. By enabling neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Proper analysis of calcium imaging data requires denoising, that is separating the signal from complex physiological noise. To analyze two-photon brain imaging data, we present a signal plus colored noise model in which the signal is represented as harmonic regression and the correlated noise is represented as an order autoregressive process. We provide an efficient cyclic descent algorithm to compute approximate maximum likelihood parameter estimates by combing a weighted least-squares procedure with the Burg algorithm. We use Akaike information criterion to guide selection of the harmonic regression and the autoregressive model orders. Our flexible yet parsimonious modeling approach reliably separates stimulus-evoked fluorescence response from background activity and noise, assesses goodness of fit, and estimates confidence intervals and signal-to-noise ratio. This refined separation leads to appreciably enhanced image contrast for individual cells including clear delineation of subcellular details and network activity. The application of our approach to in vivo imaging data recorded in the ferret primary visual cortex demonstrates that our method yields substantially denoised signal estimates. We also provide a general Volterra series framework for deriving this and other signal plus correlated noise models for imaging. This approach to analyzing two-photon calcium imaging data may be readily adapted to other computational biology problems which apply correlated noise models.

[Orientation hypercolumns of the visual cortex: ring model]

A hypercolumn of the visual cortex is a functional unit formed of the neighbouring columns whose neurons respond to a stimulus of particular orientation. The function of the hypercolumn is to amplify the orientation tuning of visually evoked responses. According to the conventional simple model of a hypercolumn, neuronal populations with different orientation preferences are distributed on a ring. Every population is described by the frequency (FR) model. To determine the limitations of the FR-ring model, it was compared with a more detailed ring model, which takes into account the distribution of neurons of each population according to their voltage values. In the case of the leaky integrate-and-fire neurons, every neural population is described by the Fokker-Planck (FP) equation. The mapping of parameters was obtained. The simulations revealed differences in the behaviour of the two models. Contrary to the FR model, the model based on the Fokker-Planck equation reacts faster to a change in stimulus orientation. The Fokker-Planck ring model gives a steady-state solution in the form of waves of activity travelling on the ring, whereas the FR ring model presents amplitude instability for the same parameter set. The FR ring model reproduces the characteristic effects of the ring model: the virtual rotation and the symmetry breaking.

[The size of cells providing interhemispheric and intrahemispheric connections in the visual cortex of binocular vision-impaired cats]

The size (somatic area) of 658 cells located in layers 2/3 of cortical areas 17, 18 of both hemispheres in intact monocularly deprived and bilateral strabismic cats was measured. These cells were retrogradely labelled after injections of horseradish peroxidase into ocular dominance columns in areas 17, 18. In all groups of cats, the mean somatic area of callosal cells was significantly larger than the mean somatic area of intrahemispheric cells. It was found that the mean somatic area of callosal cells was increased by 26.6% in monocularly deprived cats and by 20.2% in strabismic cats in relation to the mean somatic area of callosal cells in intact cats. In addition, the mean somatic area of intrahemispheric cells in monocularly deprived cats was indistinguishable from the mean somatic area of intrahemispheric cells in strabismic cats and in intact cats. It is concluded that early binocular vision impairments produce enlargement of callosal cells' size in the visual cortex.

Mutual information and redundancy in spontaneous communication between cortical neurons

An important question in neural information processing is how neurons cooperate to transmit information. To study this question, we resort to the concept of redundancy in the information transmitted by a group of neurons and, at the same time, we introduce a novel concept for measuring cooperation between pairs of neurons called relative mutual information (RMI). Specifically, we studied these two parameters for spike trains generated by neighboring neurons from the primary visual cortex in the awake, freely moving rat. The spike trains studied here were spontaneously generated in the cortical network, in the absence of visual stimulation. Under these conditions, our analysis revealed that while the value of RMI oscillated slightly around an average value, the redundancy exhibited a behavior characterized by a higher variability. We conjecture that this combination of approximately constant RMI and greater variable redundancy makes information transmission more resistant to noise disturbances. Furthermore, the redundancy values suggest that neurons can cooperate in a flexible way during information transmission. This mostly occurs via a leading neuron with higher transmission rate or, less frequently, through the information rate of the whole group being higher than the sum of the individual information rates-in other words in a synergetic manner. The proposed method applies not only to the stationary, but also to locally stationary neural signals.

Cortical modulation of the transient visual response at thalamic level: a TMS study

The transient visual response of feline dorsal lateral geniculate nucleus (dLGN) cells was studied under control conditions and during the application of repetitive transcranial magnetic stimulation at 1 Hz (rTMS@1Hz) on the primary visual cortex (V1). The results show that rTMS@1Hz modulates the firing mode of Y cells, inducing an increase in burst spikes and a decrease in tonic firing. On the other hand, rTMS@1Hz modifies the spatiotemporal characteristics of receptive fields of X cells, inducing a delay and a decrease of the peak response, and a change of the surround/center amplitude ratio of RF profiles. These results indicate that V1 controls the activity of the visual thalamus in a different way in the X and Y pathways, and that this feedback control is consistent with functional roles associated with each cell type.

Timescales of multineuronal activity patterns reflect temporal structure of visual stimuli

The investigation of distributed coding across multiple neurons in the cortex remains to this date a challenge. Our current understanding of collective encoding of information and the relevant timescales is still limited. Most results are restricted to disparate timescales, focused on either very fast, e.g., spike-synchrony, or slow timescales, e.g., firing rate. Here, we investigated systematically multineuronal activity patterns evolving on different timescales, spanning the whole range from spike-synchrony to mean firing rate. Using multi-electrode recordings from cat visual cortex, we show that cortical responses can be described as trajectories in a high-dimensional pattern space. Patterns evolve on a continuum of coexisting timescales that strongly relate to the temporal properties of stimuli. Timescales consistent with the time constants of neuronal membranes and fast synaptic transmission (5–20 ms) play a particularly salient role in encoding a large amount of stimulus-related information. Thus, to faithfully encode the properties of visual stimuli the brain engages multiple neurons into activity patterns evolving on multiple timescales.

[Ensemble activity of visual and frontal cortex in the conditions of choice of different value food reinforcement in cats]

The method of "low choice" of food value reinforcement depending on delayed response to its reception demonstrated various strategy of behaviour, showing short or delayed responses of pressing pedal for reception of low or high value of food reinforcement. In trained cats, multiunit activity was recorded in investigated brain areas and functional interneuron interactions were subsequently analysed. Significant prevalence of interneuron interactions was revealed in "impulsive" animals in both cortical zones as well as intergroup differences during the pre-signal and signal periods in visual and frontal cortex.

[Retinotopic organization of the lateral suprasylvian sulcus' posterior-medial area as revealed by analysis of the pattern of cortico-cortical connection with the cat 17th area]

Using cortico-cortical connection analysis technique, the cat visual area PMLS (the area located on posterior medial wall of the lateral suprasylvian sulcus) retinotopic organization was investigated. A retrograde axonal tracer: horseradish peroxidase (HRP), was injected in the PMLS, and initial neurons were investigated in area 17. It was shown that after HRP injection in PMLS locus, where a central vision field is located, a labelled cell pattern in area 17 corresponded to the L. Palmer et al., 1978, retinotopic map. On the contrary, after HRP injection in PMLS locus, where an upper vision field must be located, as L. Palmer et al., 1978 predicted, initial neurons are visualized in area 17 loci where low visual periphery is displayed: -10 degrees to -60 degrees in vertical meridian and 40 degrees to 80 degrees in horizontal meridian. Such discrepancy in upper and lower visual field representation was also obtained in electrophysiological and topographic investigations by Grant, Shipp, 1991. The data suggest necessity of S. Grant and S. Shipp's retinotopic map use in the cat area PMLS morphofunctional investigation.

Repetitive adaptation induces plasticity of spatial frequency tuning in cat primary visual cortex

Sensory neurons display transient changes in their response properties following prolonged exposure to an appropriate stimulus (adaptation). In adult cat primary visual cortex, spatial frequency-selective neurons shift their preferred spatial frequency (SF) after being adapted to a non-preferred SF. In anesthetized cats prepared for electrophysiological recordings in the visual cortex, we applied a non-preferred spatial frequency for two successive periods of adaptation (a recovery and interval of ∼90 min separated both phases of adaptation) in order to determine if a first adaptation retained an influence on a second adaptation. The first application of a non-preferred SF shifted the tuning curve of the cell mainly in the direction of the imposed SF. The results showed that attractive shifts occurred more frequently (68%) than repulsive (12%) changes in cortical cells. The increase of responsivity was band-limited and occurred around the imposed SF, while flanked responses remained unmodified in all conditions. After a recovery period allowing neurons to restore their original SF tuning curves, we carried out a second adaptation which produced four major results: (1) a higher proportion of repulsive shifts (31%) compared to attractive shifts (49%), (2) an increase of the magnitude of the attractive shifts, (3) an additional enhancement of the evoked firing rate for the newly acquired SF, and (4) for the acquired SF the variability coefficient decreased following the second adaptation. The supplementary response changes suggest that neurons in area 17 keep a "memory" trace of the previous stimulus properties. It also highlights the dynamic nature of basic neuronal properties in adult cortex since repeated adaptations modified both the spatial frequency tuning selectivity and the response strength to the preferred spatial frequency. These enhanced neuronal responses suggest that the range of adaptation-induced plasticity available to the visual system is broader than anticipated.

LFP spectral peaks in V1 cortex: network resonance and cortico-cortical feedback

This paper is about how cortical recurrent interactions in primary visual cortex (V1) together with feedback from extrastriate cortex can account for spectral peaks in the V1 local field potential (LFP). Recent studies showed that visual stimulation enhances the γ-band (25-90 Hz) of the LFP power spectrum in macaque V1. The height and location of the γ-band peak in the LFP spectrum were correlated with visual stimulus size. Extensive spatial summation, possibly mediated by feedback connections from extrastriate cortex and long-range horizontal connections in V1, must play a crucial role in the size dependence of the LFP. To analyze stimulus-effects on the LFP of V1 cortex, we propose a network model for the visual cortex that includes two populations of V1 neurons, excitatory and inhibitory, and also includes feedback to V1 from extrastriate cortex. The neural network model for V1 was a resonant system. The model's resonance frequency (ResF) was in the γ-band and varied up or down in frequency depending on cortical feedback. The model's ResF shifted downward with stimulus size, as in the real cortex, because increased size recruited more activity in extrastriate cortex and V1 thereby causing stronger feedback. The model needed to have strong local recurrent inhibition within V1 to obtain ResFs that agree with cortical data. Network resonance as a consequence of recurrent excitation and inhibition appears to be a likely explanation for γ-band peaks in the LFP power spectrum of the primary visual cortex.

Covert shift of attention modulates the ongoing neural activity in a reaching area of the macaque dorsomedial visual stream

Background

Attention is used to enhance neural processing of selected parts of a visual scene. It increases neural responses to stimuli near target locations and is usually coupled to eye movements. Covert attention shifts, however, decouple the attentional focus from gaze, allowing to direct the attention to a peripheral location without moving the eyes. We tested whether covert attention shifts modulate ongoing neuronal activity in cortical area V6A, an area that provides a bridge between visual signals and arm-motor control.

Methodology/Principal Findings

We performed single cell recordings from 3 Macaca Fascicularis trained to fixate straight-head, while shifting attention outward to a peripheral cue and inward again to the fixation point. We found that neurons in V6A are influenced by spatial attention. The attentional modulation occurs without gaze shifts and cannot be explained by visual stimulations. Visual, motor, and attentional responses can occur in combination in single neurons.

Conclusions/Significance

This modulation in an area primarily involved in visuo-motor transformation for reaching may form a neural basis for coupling attention to the preparation of reaching movements. Our results show that cortical processes of attention are related not only to eye-movements, as many studies have shown, but also to arm movements, a finding that has been suggested by some previous behavioral findings. Therefore, the widely-held view that spatial attention is tightly intertwined with—and perhaps directly derived from—motor preparatory processes should be extended to a broader spectrum of motor processes than just eye movements.

Morphological bases of suppressive and facilitative spatial summation in the striate cortex of the cat

In V1 of cats and monkeys, activity of neurons evoked by stimuli within the receptive field can be modulated by stimuli in the extra-receptive field (ERF). This modulating effect can be suppressive (S-ERF) or facilitatory (F-ERF) and plays different roles in visual information processing. Little is known about the cellular bases underlying the different types of ERF modulating effects. Here, we focus on the morphological differences between the S-ERF and F-ERF neurons. Single unit activities were recorded from V1 of the cat. The ERF properties of each neuron were assessed by area-response functions using sinusoidal grating stimuli. On completion of the functional tests, the cells were injected intracellularly with biocytin. The labeled cells were reconstructed and morphologically characterized in terms of the ERF modulation effects. We show that the vast majority of S-ERF neurons and F-ERF neurons are pyramidal cells and that the two types of cells clearly differ in the size of the soma, in complexity of dendrite branching, in spine size and density, and in the range of innervations of the axon collaterals. We propose that different pyramidal cell phenotypes reflect a high degree of specificity of neuronal connections associated with different types of spatial modulation.

Universality in the evolution of orientation columns in the visual cortex

The brain's visual cortex processes information concerning form, pattern, and motion within functional maps that reflect the layout of neuronal circuits. We analyzed functional maps of orientation preference in the ferret, tree shrew, and galago--three species separated since the basal radiation of placental mammals more than 65 million years ago--and found a common organizing principle. A symmetry-based class of models for the self-organization of cortical networks predicts all essential features of the layout of these neuronal circuits, but only if suppressive long-range interactions dominate development. We show mathematically that orientation-selective long-range connectivity can mediate the required interactions. Our results suggest that self-organization has canalized the evolution of the neuronal circuitry underlying orientation preference maps into a single common design.

Two-photon imaging of calcium in virally transfected striate cortical neurons of behaving monkey

Two-photon scanning microscopy has advanced our understanding of neural signaling in non-mammalian species and mammals. Various developments are needed to perform two-photon scanning microscopy over prolonged periods in non-human primates performing a behavioral task. In striate cortex in two macaque monkeys, cortical neurons were transfected with a genetically encoded fluorescent calcium sensor, memTNXL, using AAV1 as a viral vector. By constructing an extremely rigid and stable apparatus holding both the two-photon scanning microscope and the monkey's head, single neurons were imaged at high magnification for prolonged periods with minimal motion artifacts for up to ten months. Structural images of single neurons were obtained at high magnification. Changes in calcium during visual stimulation were measured as the monkeys performed a fixation task. Overall, functional responses and orientation tuning curves were obtained in 18.8% of the 234 labeled and imaged neurons. This demonstrated that the two-photon scanning microscopy can be successfully obtained in behaving primates.

Perceptual learning: cortical changes when cats learn a new trick

A new study has found that the tuning properties of neurons in the primary visual cortex of cats change as they learn an orientation-discrimination task, casting new light on the neuronal basis of perceptual learning.

Cortical layer V neurons in the auditory and visual cortices of normal, reeler, and yotari mice

Both in the Reelin-deficient reeler and Dab1-deficient yotari mice, layer V corticospinal tract neurons in the sensory-motor cortex are radially spread instead of being confined to a single cortical layer. In the present study, we examined distribution pattern of cortical layer V neurons in the visual and auditory cortices of reeler and yotari mice with the injection of HRP into the superior and inferior colliculi of the adult animals, respectively. After the injection of HRP into the superior colliculus of the normal mouse, retrogradely labeled cells were distributed in layer V of the visual cortex, while the similar injection of HRP in the reeler and yotari mice produced radial dispersion of retrograde labeling through all of the depths of the visual cortex of these mutant mice. Next, we injected HRP into the inferior colliculus of the normal, reeler and yotari mice. Retrogradely labeled neurons were distributed in layer V of the normal auditory cortex, whereas they were again radially scattered in the auditory cortex of the reeler and yotari mice. Taken together with the previous and present findings, layer V cortical efferent neurons are radially scattered in the sensory-motor, visual and auditory cortices of the reeler and yotari mice.

Response features of parvalbumin-expressing interneurons suggest precise roles for subtypes of inhibition in visual cortex

Inhibitory interneurons in the cerebral cortex include a vast array of subtypes, varying in their molecular signatures, electrophysiological properties, and connectivity patterns. This diversity suggests that individual inhibitory classes have unique roles in cortical circuits; however, their characterization to date has been limited to broad classifications including many subtypes. We used the Cre/LoxP system, specifically labeling parvalbumin(PV)-expressing interneurons in visual cortex of PV-Cre mice with red fluorescent protein (RFP), followed by targeted loose-patch recordings and two-photon imaging of calcium responses in vivo to characterize the visual receptive field properties of these cells. Despite their relative molecular and morphological homogeneity, we find that PV+ neurons have a diversity of feature-specific visual responses that include sharp orientation and direction-selectivity, small receptive fields, and band-pass spatial frequency tuning. These results suggest that subsets of parvalbumin interneurons are components of specific cortical networks and that perisomatic inhibition contributes to the generation of precise response properties.

Receptive-field properties of V1 and V2 neurons in mice and macaque monkeys

We report the results of extracellular single-unit recording experiments where we quantitatively analyzed the receptive-field (RF) properties of neurons in V1 and an adjacent extrastriate visual area (V2L) of anesthetized mice with emphasis on the RF center-surround organization. We compared the results with the RF center-surround organization of V1 and V2 neurons in macaque monkeys. If species differences in spatial scale are taken into consideration, mouse V1 and V2L neurons had remarkably fine stimulus selectivity, and the majority of response properties in V2L were not different from those in V1. The RF center-surround organization of mouse V1 neurons was qualitatively similar to that for macaque monkeys (i.e., the RF center is surrounded by extended suppressive regions). However, unlike in monkey V2, a significant proportion of cortical neurons, largely complex cells in V2L, did not exhibit quantifiable RF surround suppression. Simple cells had smaller RF centers than complex cells, and the prevalence and strength of surround suppression were greater in simple cells than in complex cells. These findings, particularly on the RF center-surround organization of visual cortical neurons, give new insights into the principles governing cortical circuits in the mouse visual cortex and should provide further impetus for the use of mice in studies on the genetic and molecular basis of RF development and synaptic plasticity.

The design and preparation of a flexible bio-chip for use as a visual prosthesis, and evaluation of its biological features

We aimed to design and manufacture a novel low-cost polyimide microelectrode array (MEA) chip for visual prosthesis research and to evaluate its biological features. A microelectrode array was developed, based on Flexible Printed Circuit Board (FPC) technology which enables electrical stimulation of the cortex. In an in vitro experiment, rat visual cortex cells were co-cultured with the chip and examined using scanning electron microscopy. Trypan blue exclusion and methyl blue tetrazolium tests showed that cell viability and survival rates (90-98%) did not significantly differ between the co-cultured chip group and the control group. In an in vivo experiment HE/Nissl staining performed to investigate the possibility of brain tissue degeneration around implanted MEAs showed no negative effects of the chip on visual cortical cells after 1 month in situ. The good functional characteristics and biocomptability suggest that such a low-cost device could have widespread application, particularly in countries with a large blind population and limited financial resources.

Impact of dendritic size and dendritic topology on burst firing in pyramidal cells

Neurons display a wide range of intrinsic firing patterns. A particularly relevant pattern for neuronal signaling and synaptic plasticity is burst firing, the generation of clusters of action potentials with short interspike intervals. Besides ion-channel composition, dendritic morphology appears to be an important factor modulating firing pattern. However, the underlying mechanisms are poorly understood, and the impact of morphology on burst firing remains insufficiently known. Dendritic morphology is not fixed but can undergo significant changes in many pathological conditions. Using computational models of neocortical pyramidal cells, we here show that not only the total length of the apical dendrite but also the topological structure of its branching pattern markedly influences inter- and intraburst spike intervals and even determines whether or not a cell exhibits burst firing. We found that there is only a range of dendritic sizes that supports burst firing, and that this range is modulated by dendritic topology. Either reducing or enlarging the dendritic tree, or merely modifying its topological structure without changing total dendritic length, can transform a cell's firing pattern from bursting to tonic firing. Interestingly, the results are largely independent of whether the cells are stimulated by current injection at the soma or by synapses distributed over the dendritic tree. By means of a novel measure called mean electrotonic path length, we show that the influence of dendritic morphology on burst firing is attributable to the effect both dendritic size and dendritic topology have, not on somatic input conductance, but on the average spatial extent of the dendritic tree and the spatiotemporal dynamics of the dendritic membrane potential. Our results suggest that alterations in size or topology of pyramidal cell morphology, such as observed in Alzheimer's disease, mental retardation, epilepsy, and chronic stress, could change neuronal burst firing and thus ultimately affect information processing and cognition.