Synaptic integration in striate cortical simple cells

Cortical mechanisms of visual brightness

The primary visual cortex signals the onset of light and dark stimuli with ON and OFF cortical pathways. Here, we demonstrate that both pathways generate similar response increments to large homogeneous surfaces and their response average increases with surface brightness. We show that, in cat visual cortex, response dominance from ON or OFF pathways is bimodally distributed when stimuli are smaller than one receptive field center but unimodally distributed when they are larger. Moreover, whereas small bright stimuli drive opposite responses from ON and OFF pathways (increased versus suppressed activity), large bright surfaces drive similar response increments. We show that this size-brightness relation emerges because strong illumination increases the size of light surfaces in nature and both ON and OFF cortical neurons receive input from ON thalamic pathways. We conclude that visual scenes are perceived as brighter when the average response increments from ON and OFF cortical pathways become stronger.

Mazade et al. find that the visual cortex encodes brightness differently for small than large stimuli. Bright small stimuli drive cortical pathways signaling lights and suppress cortical pathways signaling darks. Conversely, large surfaces drive response increments from both pathways and appear brightest when the response average is strongest.

Horizontal connectivity in V1: Prediction of coherence in contour and motion integration

This study demonstrates the functional importance of the Surround context relayed laterally in V1 by the horizontal connectivity, in controlling the latency and the gain of the cortical response to the feedforward visual drive. We report here four main findings: 1) a centripetal apparent motion sequence results in a shortening of the spiking latency of V1 cells, when the orientation of the local inducer and the global motion axis are both co-aligned with the RF orientation preference; 2) this contextual effects grows with visual flow speed, peaking at 150–250°/s when it matches the propagation speed of horizontal connectivity (0.15–0.25 mm/ms); 3) For this speed range, the axial sensitivity of V1 cells is tilted by 90° to become co-aligned with the orientation preference axis; 4) the strength of modulation by the surround context correlates with the spatiotemporal coherence of the apparent motion flow. Our results suggest an internally-generated binding process, linking local (orientation /position) and global (motion/direction) features as early as V1. This long-range diffusion process constitutes a plausible substrate in V1 of the human psychophysical bias in speed estimation for collinear motion. Since it is demonstrated in the anesthetized cat, this novel form of contextual control of the cortical gain and phase is a built-in property in V1, whose expression does not require behavioral attention and top-down control from higher cortical areas. We propose that horizontal connectivity participates in the propagation of an internal “prediction” wave, shaped by visual experience, which links contour co-alignment and global axial motion at an apparent speed in the range of saccade-like eye movements.

Coding of chromatic spatial contrast by macaque V1 neurons

Color perception relies on comparisons between adjacent lights, but how the brain performs these comparisons is poorly understood. To elucidate the underlying neural mechanisms, we recorded spiking responses of individual V1 neurons in macaque monkeys to pairs of stimuli within the classical receptive field (RF). We estimated the spatial-chromatic RF of each neuron and then presented customized colored edges using a novel closed-loop technique. We found that many double-opponent (DO) cells, which have spatially and chromatically opponent RFs, responded to chromatic contrast as a weighted sum, akin to how other V1 cells responded to luminance contrast. Yet other neurons integrated chromatic signals non-linearly, confirming that linear signal integration is not an obligate property of V1 neurons. The functional similarity of cone-opponent DO cells and cone non-opponent simple cells suggests that these two groups may share a common underlying neural circuitry, promotes the construction of image-computable models for full-color image representation, and sheds new light on V1 complex cells.

The physiological basis for contrast opponency in motion computation in Drosophila

In Drosophila, direction-selective neurons implement a mechanism of motion computation similar to cortical neurons, using contrast-opponent receptive fields with ON and OFF subfields. It is not clear how the presynaptic circuitry of direction-selective neurons in the OFF pathway supports this computation if all major inputs are OFF-rectified neurons. Here, we reveal the biological substrate for motion computation in the OFF pathway. Three interneurons, Tm2, Tm9 and CT1, provide information about ON stimuli to the OFF direction-selective neuron T5 across its receptive field, supporting a contrast-opponent receptive field organization. Consistent with its prominent role in motion detection, variability in Tm9 receptive field properties transfers to T5, and calcium decrements in Tm9 in response to ON stimuli persist across behavioral states, while spatial tuning is sharpened by active behavior. Together, our work shows how a key neuronal computation is implemented by its constituent neuronal circuit elements to ensure direction selectivity.

Spatial receptive field structure of double-opponent cells in macaque V1

The spatial processing of color is important for visual perception. Double-opponent (DO) cells likely contribute to this processing by virtue of their spatially opponent and cone-opponent receptive fields (RFs). However, the representation of visual features by DO cells in the primary visual cortex of primates is unclear because the spatial structure of their RFs has not been fully characterized. To fill this gap, we mapped the RFs of DO cells in awake macaques with colorful, dynamic white noise patterns. The spatial RF of each neuron was fitted with a Gabor function and three versions of the difference of Gaussians (DoG) function. The Gabor function provided the more accurate description for most DO cells, a result that is incompatible with a center-surround RF organization. A nonconcentric version of the DoG function, in which the RFs have a circular center and a crescent-shaped surround, performed nearly as well as the Gabor model thus reconciling results from previous reports. For comparison, we also measured the RFs of simple cells. We found that the superiority of the Gabor fits over DoG fits was slightly more decisive for simple cells than for DO cells. The implications of these results on biological image processing and visual perception are discussed.NEW & NOTEWORTHY Double-opponent cells in macaque area V1 respond to spatial chromatic contrast in visual scenes. What information they carry is debated because their receptive field organization has not been characterized thoroughly. Using white noise analysis and statistical model comparisons, De and Horwitz show that many double-opponent receptive fields can be captured by either a Gabor model or a center-with-an-asymmetric-surround model but not by a difference of Gaussians model.

Predicting neuronal dynamics with a delayed gain control model

Visual neurons respond to static images with specific dynamics: neuronal responses sum sub-additively over time, reduce in amplitude with repeated or sustained stimuli (neuronal adaptation), and are slower at low stimulus contrast. Here, we propose a simple model that predicts these seemingly disparate response patterns observed in a diverse set of measurements-intracranial electrodes in patients, fMRI, and macaque single unit spiking. The model takes a time-varying contrast time course of a stimulus as input, and produces predicted neuronal dynamics as output. Model computation consists of linear filtering, expansive exponentiation, and a divisive gain control. The gain control signal relates to but is slower than the linear signal, and this delay is critical in giving rise to predictions matched to the observed dynamics. Our model is simpler than previously proposed related models, and fitting the model to intracranial EEG data uncovers two regularities across human visual field maps: estimated linear filters (temporal receptive fields) systematically differ across and within visual field maps, and later areas exhibit more rapid and substantial gain control. The model is further generalizable to account for dynamics of contrast-dependent spike rates in macaque V1, and amplitudes of fMRI BOLD in human V1.

Spatial Asymmetry and Short-Term Suppression Underlie Direction Selectivity of Synaptic Excitation in the Mouse Visual Cortex

Direction selectivity (DS) of neuronal responses is fundamental for motion detection. With in vivo whole-cell voltage-clamp recordings from layer (L)4 neurons in the mouse visual cortex, we observed a strong correlation between DS and spatial asymmetry in the distribution of excitatory input strengths. This raises an interesting possibility that the latter may contribute to DS. The preferred direction of excitatory input was found from the stronger to weaker side of its spatial receptive field. A simple linear summation of asymmetrically distributed excitatory responses to stationary flash stimuli however failed to predict the correct directionality: it at best resulted in weak DS with preferred direction opposite to what was observed experimentally. Further studies with sequential 2 flash-bar stimulation revealed a short-term suppression of excitatory input evoked by the late bar. More importantly, the level of the suppression positively correlated with the relative amplitude of the early-bar response. Implementing this amplitude-dependent suppressive interaction can successfully predict DS of excitatory input. Our results suggest that via nonlinear temporal interactions, the spatial asymmetry can be transformed into differential temporal integration of inputs under opposite directional movements. This mechanism may contribute to the DS of excitatory inputs to L4 neurons.

Toward a Biologically Plausible Model of LGN-V1 Pathways Based on Efficient Coding

Increasing evidence supports the hypothesis that the visual system employs a sparse code to represent visual stimuli, where information is encoded in an efficient way by a small population of cells that respond to sensory input at a given time. This includes simple cells in primary visual cortex (V1), which are defined by their linear spatial integration of visual stimuli. Various models of sparse coding have been proposed to explain physiological phenomena observed in simple cells. However, these models have usually made the simplifying assumption that inputs to simple cells already incorporate linear spatial summation. This overlooks the fact that these inputs are known to have strong non-linearities such the separation of ON and OFF pathways, or separation of excitatory and inhibitory neurons. Consequently these models ignore a range of important experimental phenomena that are related to the emergence of linear spatial summation from non-linear inputs, such as segregation of ON and OFF sub-regions of simple cell receptive fields, the push-pull effect of excitation and inhibition, and phase-reversed cortico-thalamic feedback. Here, we demonstrate that a two-layer model of the visual pathway from the lateral geniculate nucleus to V1 that incorporates these biological constraints on the neural circuits and is based on sparse coding can account for the emergence of these experimental phenomena, diverse shapes of receptive fields and contrast invariance of orientation tuning of simple cells when the model is trained on natural images. The model suggests that sparse coding can be implemented by the V1 simple cells using neural circuits with a simple biologically plausible architecture.

Neural coding: A single neuron's perspective

What any sensory neuron knows about the world is one of the cardinal questions in Neuroscience. Information from the sensory periphery travels across synaptically coupled neurons as each neuron encodes information by varying the rate and timing of its action potentials (spikes). Spatiotemporally correlated changes in this spiking regimen across neuronal populations are the neural basis of sensory representations. In the somatosensory cortex, however, spiking of individual (or pairs of) cortical neurons is only minimally informative about the world. Recent studies showed that one solution neurons implement to counteract this information loss is adapting their rate of information transfer to the ongoing synaptic activity by changing the membrane potential at which spike is generated. Here we first introduce the principles of information flow from the sensory periphery to the primary sensory cortex in a model sensory (whisker) system, and subsequently discuss how the adaptive spike threshold gates the intracellular information transfer from the somatic post-synaptic potential to action potentials, controlling the information content of communication across somatosensory cortical neurons.

Does experience provide a permissive or instructive influence on the development of direction selectivity in visual cortex?

In principle, the development of sensory receptive fields in cortex could arise from experience-independent mechanisms that have been acquired through evolution, or through an online analysis of the sensory experience of the individual animal. Here we review recent experiments that suggest that the development of direction selectivity in carnivore visual cortex requires experience, but also suggest that the experience of an individual animal cannot greatly influence the parameters of the direction tuning that emerges, including direction angle preference and speed tuning. The direction angle preference that a neuron will acquire can be predicted from small initial biases that are present in the naïve cortex prior to the onset of visual experience. Further, experience with stimuli that move at slow or fast speeds does not alter the speed tuning properties of direction-selective neurons, suggesting that speed tuning preferences are built in. Finally, unpatterned optogenetic activation of the cortex over a period of a few hours is sufficient to produce the rapid emergence of direction selectivity in the naïve ferret cortex, suggesting that information about the direction angle preference that cells will acquire must already be present in the cortical circuit prior to experience. These results are consistent with the idea that experience has a permissive influence on the development of direction selectivity.

Thalamocortical Circuits and Functional Architecture

The thalamocortical pathway is the main route of communication between the eye and the cerebral cortex. During embryonic development, thalamocortical afferents travel to L4 and are sorted by receptive field position, eye of origin, and contrast polarity (i.e., preference for light or dark stimuli). In primates and carnivores, this sorting involves numerous afferents, most of which sample a limited region of the binocular field. Devoting abundant thalamocortical resources to process a limited visual field has a clear advantage: It allows many stimulus combinations to be sampled at each spatial location. Moreover, the sampling efficiency can be further enhanced by organizing the afferents in a cortical grid for eye input and contrast polarity. We argue that thalamocortical interactions within this eye-polarity grid can be used to represent multiple stimulus combinations found in nature and to build an accurate cortical map for multidimensional stimulus space.

Rapid Rebalancing of Excitation and Inhibition by Cortical Circuitry

Excitation is balanced by inhibition to cortical neurons across a wide range of conditions. To understand how this relationship is maintained, we broadly suppressed the activity of parvalbumin-expressing (PV+) inhibitory neurons and asked how this affected the balance of excitation and inhibition throughout auditory cortex. Activating archaerhodopsin in PV+ neurons effectively suppressed them in layer 4. However, the resulting increase in excitation outweighed Arch suppression and produced a net increase in PV+ activity in downstream layers. Consequently, suppressing PV+ neurons did not reduce inhibition to principal neurons (PNs) but instead resulted in a tightly coordinated increase in both excitation and inhibition. The increase in inhibition constrained the magnitude of PN spiking responses to the increase in excitation and produced nonlinear changes in spike tuning. Excitatory-inhibitory rebalancing is mediated by strong PN-PV+ connectivity within and between layers and is likely engaged during normal cortical operation to ensure balance in downstream neurons.

Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells

Despite half-a-century of research since the seminal work of Hubel and Wiesel, the role of the dorsal lateral geniculate nucleus (dLGN) in shaping the visual signals is not properly understood. Placed on route from retina to primary visual cortex in the early visual pathway, a striking feature of the dLGN circuit is that both the relay cells (RCs) and interneurons (INs) not only receive feedforward input from retinal ganglion cells, but also a prominent feedback from cells in layer 6 of visual cortex. This feedback has been proposed to affect synchronicity and other temporal properties of the RC firing. It has also been seen to affect spatial properties such as the center-surround antagonism of thalamic receptive fields, i.e., the suppression of the response to very large stimuli compared to smaller, more optimal stimuli. Here we explore the spatial effects of cortical feedback on the RC response by means of a a comprehensive network model with biophysically detailed, single-compartment and multicompartment neuron models of RCs, INs and a population of orientation-selective layer 6 simple cells, consisting of pyramidal cells (PY). We have considered two different arrangements of synaptic feedback from the ON and OFF zones in the visual cortex to the dLGN: phase-reversed (‘push-pull’) and phase-matched (‘push-push’), as well as different spatial extents of the corticothalamic projection pattern. Our simulation results support that a phase-reversed arrangement provides a more effective way for cortical feedback to provide the increased center-surround antagonism seen in experiments both for flashing spots and, even more prominently, for patch gratings. This implies that ON-center RCs receive direct excitation from OFF-dominated cortical cells and indirect inhibitory feedback from ON-dominated cortical cells. The increased center-surround antagonism in the model is accompanied by spatial focusing, i.e., the maximum RC response occurs for smaller stimuli when feedback is present.

The functional role of the dorsal lateral geniculate nucleus (dLGN), placed on route from retina to primary visual cortex in the early visual pathway, is still poorly understood. A striking feature of the dLGN circuit is that dLGN cells not only receive feedforward input from the retina, but also a prominent feedback from cells in the visual cortex. It has been seen in experiments that cortical feedback modifies the spatial properties of dLGN cells in response to visual stimuli. In particular, it has been shown to increase the center-surround antagonism for flashing-spot and patch-grating visual stimuli, i.e., the suppression of responses to very large stimuli compared to smaller stimuli. Here we investigate the putative mechanisms behind this feature by means of a comprehensive network model of biophysically detailed neuron models for RCs and INs in the dLGN and orientation-selective cortical cells providing the feedback. Our results support that the experimentally observed feedback effects may be due to a phase-reversed (‘push-pull’) arrangement of the cortical feedback where ON-symmetry RCs receive (indirect) inhibitory feedback from ON-dominated cortical cell and excitation from OFF-dominated cortical cells, and vice versa for OFF-symmetry RCs.

Mechanisms of Orientation Selectivity in the Primary Visual Cortex

The mechanisms underlying the emergence of orientation selectivity in the visual cortex have been, and continue to be, the subjects of intense scrutiny. Orientation selectivity reflects a dramatic change in the representation of the visual world: Whereas afferent thalamic neurons are generally orientation insensitive, neurons in the primary visual cortex (V1) are extremely sensitive to stimulus orientation. This profound change in the receptive field structure along the visual pathway has positioned V1 as a model system for studying the circuitry that underlies neural computations across the neocortex. The neocortex is characterized anatomically by the relative uniformity of its circuitry despite its role in processing distinct signals from region to region. A combination of physiological, anatomical, and theoretical studies has shed some light on the circuitry components necessary for generating orientation selectivity in V1. This targeted effort has led to critical insights, as well as controversies, concerning how neural circuits in the neocortex perform computations.

Synaptic Mechanisms of Feature Coding in the Visual Cortex of Awake Mice

The synaptic mechanisms of feature coding in the visual cortex are poorly understood, particularly in awake animals. The ratio between excitation (E) and inhibition (I) might be constant across stimulus space, controlling only the gain and timing of neuronal responses, or it might change, directly contributing to feature coding. Whole-cell recordings in L2/3 of awake mice revealed that the E/I ratio systematically declines with increasing stimulus contrast or size. Suppressing somatostatin (SOM) neurons enhanced the E and I underlying size tuning, explaining SOM neurons' role in surround suppression. These data imply that contrast and size tuning result from a combination of a changing E/I ratio and the tuning of total synaptic input. Furthermore, they provide experimental support in awake animals for the "Stabilized Supralinear Network," a model that explains diverse cortical phenomena, and suggest that a decreasing E/I ratio with increasing cortical drive could contribute to many different cortical computations.

Cornu Ammonis Regions-Antecedents of Cortical Layers?

Studying neocortex and hippocampus in parallel, we are struck by the similarities. All three to four layered allocortices and the six layered mammalian neocortex arise in the pallium. All receive and integrate multiple cortical and subcortical inputs, provide multiple outputs and include an array of neuronal classes. During development, each cell positions itself to sample appropriate local and distant inputs and to innervate appropriate targets. Simpler cortices had already solved the need to transform multiple coincident inputs into serviceable outputs before neocortex appeared in mammals. Why then do phylogenetically more recent cortices need multiple pyramidal cell layers? A simple answer is that more neurones can compute more complex functions. The dentate gyrus and hippocampal CA regions-which might be seen as hippocampal antecedents of neocortical layers-lie side by side, albeit around a tight bend. Were the millions of cells of rat neocortex arranged in like fashion, the surface area of the CA pyramidal cell layers would be some 40 times larger. Even if evolution had managed to fold this immense sheet into the space available, the distances between neurones that needed to be synaptically connected would be huge and to maintain the speed of information transfer, massive, myelinated fiber tracts would be needed. How much more practical to stack the "cells that fire and wire together" into narrow columns, while retaining the mechanisms underlying the extraordinary precision with which circuits form. This demonstrably efficient arrangement presents us with challenges, however, not the least being to categorize the baffling array of neuronal subtypes in each of five "pyramidal layers." If we imagine the puzzle posed by this bewildering jumble of apical dendrites, basal dendrites and axons, from many different pyramidal and interneuronal classes, that is encountered by a late-arriving interneurone insinuating itself into a functional circuit, we can perhaps begin to understand why definitive classification, covering every aspect of each neurone's structure and function, is such a challenge. Here, we summarize and compare the development of these two cortices, the properties of their neurones, the circuits they form and the ordered, unidirectional flow of information from one hippocampal region, or one neocortical layer, to another.

Functional implications of orientation maps in primary visual cortex

Stimulus orientation in the primary visual cortex of primates and carnivores is mapped as iso-orientation domains radiating from pinwheel centres, where orientation preferences of neighbouring cells change circularly. Whether this orientation map has a function is currently debated, because many mammals, such as rodents, do not have such maps. Here we show that two fundamental properties of visual cortical responses, contrast saturation and cross-orientation suppression, are stronger within cat iso-orientation domains than at pinwheel centres. These differences develop when excitation (not normalization) from neighbouring oriented neurons is applied to different cortical orientation domains and then balanced by inhibition from un-oriented neurons. The functions of the pinwheel mosaic emerge from these local intra-cortical computations: Narrower tuning, greater cross-orientation suppression and higher contrast gain of iso-orientation cells facilitate extraction of object contours from images, whereas broader tuning, greater linearity and less suppression of pinwheel cells generate selectivity for surface patterns and textures.

Stimulus orientation in the primary visual cortex of primates and carnivores is mapped into a geometrical mosaic but the functional implications of these maps remain debated. Here the authors reveal an association between the structure of cortical orientation maps in cats, and the functions of local cortical circuits in processing patterns and contours.

Functional dissection of inhibitory microcircuits in the visual cortex

Cerebral cortex contains various types of GABAergic neurons exerting local inhibition. Although the number of GABAergic inhibitory neurons is much smaller than glutamatergic excitatory neurons, they show greater diversity in their morphological and physiological properties. Genetic markers for distinct sub-classes of GABAergic neurons have been identified, and technical advances achieved in the past few decades have brought about a demonstration of a unique function of each sub-class of GABAergic neurons in the cortex. In particular, visual processing in the cortex requires inhibitory function of various GABAergic neurons. Here, we summarize current understandings on the function of inhibitory neurons in the cortex, especially focusing on their roles in visual processing.

Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1

Neurons in the primary visual cortex are known for responding vigorously but with high variability to classical stimuli such as drifting bars or gratings. By contrast, natural scenes are encoded more efficiently by sparse and temporal precise spiking responses. We used a conductance-based model of the visual system in higher mammals to investigate how two specific features of the thalamo-cortical pathway, namely push-pull receptive field organization and fast synaptic depression, can contribute to this contextual reshaping of V1 responses. By comparing cortical dynamics evoked respectively by natural vs. artificial stimuli in a comprehensive parametric space analysis, we demonstrate that the reliability and sparseness of the spiking responses during natural vision is not a mere consequence of the increased bandwidth in the sensory input spectrum. Rather, it results from the combined impacts of fast synaptic depression and push-pull inhibition, the later acting for natural scenes as a form of “effective” feed-forward inhibition as demonstrated in other sensory systems. Thus, the combination of feedforward-like inhibition with fast thalamo-cortical synaptic depression by simple cells receiving a direct structured input from thalamus composes a generic computational mechanism for generating a sparse and reliable encoding of natural sensory events.

How inhibitory circuits in the thalamus serve vision

Inhibitory neurons dominate the intrinsic circuits in the visual thalamus. Interneurons in the lateral geniculate nucleus innervate relay cells and each other densely to provide powerful inhibition. The visual sector of the overlying thalamic reticular nucleus receives input from relay cells and supplies feedback inhibition to them in return. Together, these two inhibitory circuits influence all information transmitted from the retina to the primary visual cortex. By contrast, relay cells make few local connections. This review explores the role of thalamic inhibition from the dual perspectives of feature detection and information theory. For example, we describe how inhibition sharpens tuning for spatial and temporal features of the stimulus and how it might enhance image perception. We also discuss how inhibitory circuits help to reduce redundancy in signals sent downstream and, at the same time, are adapted to maximize the amount of information conveyed to the cortex.

Dissecting estimation of conductances in subthreshold regimes

We study the influence of subthreshold activity in the estimation of synaptic conductances. It is known that differences between actual conductances and the estimated ones using linear regression methods can be huge in spiking regimes, so caution has been taken to remove spiking activity from experimental data before proceeding to linear estimation. However, not much attention has been paid to the influence of ionic currents active in the non-spiking regime where such linear methods are still profusely used. In this paper, we use conductance-based models to test this influence using several representative mechanisms to induce ionic subthreshold activity. In all the cases, we show that the currents activated during subthreshold activity can lead to significant errors when estimating synaptic conductance linearly. Thus, our results add a new warning message when extracting conductance traces from intracellular recordings and the conclusions concerning neuronal activity that can be drawn from them. Additionally, we present, as a proof of concept, an alternative method that takes into account the main nonlinear effects of specific ionic subthreshold currents. This method, based on the quadratization of the subthreshold dynamics, allows us to reduce the relative errors of the estimated conductances by more than one order of magnitude. In experimental conditions, under appropriate fitting to canonical models, it could be useful to obtain better estimations as well even under the presence of noise.

Orientation selectivity in cat primary visual cortex: local and global measurement

In this study, we investigated orientation selectivity in cat primary visual cortex (V1) and its relationship with various parameters. We found a strong correlation between circular variance (CV) and orthogonal-topreferred response ratio (O/P ratio), and a moderate correlation between tuning width and O/P ratio. Moreover, the suppression far from the peak that accounted for the lower CV in cat V1 cells also contributed to the narrowing of the tuning width of cells. We also studied the dependence of orientation selectivity on the modulation ratio for each cell, which is consistent with robust entrainment of the neuronal response to the phase of the drifting grating stimulus. In conclusion, the CV (global measure) and tuning width (local measure) are signifi cantly correlated with the modulation ratio.

A direct translaminar inhibitory circuit tunes cortical output

Anatomical and physiological experiments have outlined a blueprint for the feed-forward flow of activity in cortical circuits: signals are thought to propagate primarily from the middle cortical layer, L4, up to L2/3, and down to the major cortical output layer, L5. Pharmacological manipulations, however, have contested this model and suggested that L4 may not be critical for sensory responses of neurons in either superficial or deep layers. To address these conflicting models we reversibly manipulated L4 activity in awake, behaving mice using cell-type specific optogenetics. In contrast to both prevailing models, we show that activity in L4 directly suppresses L5, in part by activating deep, fast spiking inhibitory neurons. Our data suggest that the net impact of L4 activity is to sharpen the spatial representations of L5 neurons. Thus we establish a novel translaminar inhibitory circuit in the sensory cortex that acts to enhance the feature selectivity of cortical output.

Contrast invariance of orientation tuning in cat primary visual cortex neurons depends on stimulus size

KEY POINTS

The process of orientation tuning is an important and well-characterized feature of neurons in primary visual cortex. The combination of ascending and descending circuits involved is not only relevant to understanding visual processing but the function of neocortex in general. The classic feed-forward model of orientation tuning predicts a broadening effect due to increasing contrast; yet, experimental results consistently report contrast invariance. We show here that contrast invariance actually depends on stimulus size such that large stimuli extending beyond the neuron's receptive field engage circuits that promote invariance, whereas optimally sized, smaller stimuli result in contrast variance that is more in line with the classical orientation tuning model. These results illustrate the importance of optimizing stimulus parameters to best reflect the sensory pathways under study and provide new clues about different circuits that may be involved in variant and invariant response properties.

ABSTRACT

Selective response to stimulus orientation is a key feature of neurons in primary visual cortex, yet the underlying mechanisms generating orientation tuning are not fully understood. The combination of feed-forward and cortical mechanisms involved is not only relevant to understanding visual processing but the function of neocortex in general. The classic feed-forward model predicts that orientation tuning should broaden considerably with increasing contrast; however, experimental results consistently report contrast invariance. We show here, in primary visual cortex of anaesthetized cats under neuromuscular blockade, that contrast invariance occurs when visual stimuli are large enough to include the extraclassical surround (ECS), which is likely to involve circuits of suppression that may not be entirely feed-forward in origin. On the other hand, when stimulus size is optimized to the classical receptive field of each neuron, the population average shows a statistically significant 40% increase in tuning width at high contrast, demonstrating that contrast variance of orientation tuning can occur. Conversely, our results also suggest that the phenomenon of contrast invariance relies in part on the presence of the ECS. Moreover, these results illustrate the importance of optimizing stimulus parameters to best reflect the neural pathways under study.

Local field potentials are local events in the mouse auditory cortex

Local field potentials (LFPs) and spikes (SPKs) sampled at the thalamocortical recipient layers represent the inputs from the thalamus and outputs to other layers. Previous studies have shown that SPK‐constructed receptive fields (RF_SPK) of cortical neurons are much smaller than LFP‐constructed RFs (RF_LFP). The difference in cortical RF_LFP and RF_SPK is therefore a plausible indication of local networking. The presence of a boarder RF_LFP appears due to contamination, to some degree, from remote sites. Our studies of the mouse primary auditory cortex show that the best frequencies and minimum thresholds of RF_SPK and RF_LFP were similar. We also observed that the RF_LFP area was only slightly larger than the RF_SPK area, a very different finding from previous reports. The bandwidth of RF_LFP was slightly broader than that of RF_SPK at all levels. These data do not support the explanation that bioelectrical signals from distant sites impact on cortical LFP through volume conduction. That the cortical LFP represents a local event is further supported by comparisons of RF_SPK and RF_LFP after cortical inhibition by muscimol and cortical disinhibition by bicuculine. We conclude that the difference between RF_SPK (output of cortical neurons) and RF_LFP (input of cortical neurons) results from intracortical processing, including cortical lateral inhibition and excitation.

Plasticity of cortical excitatory-inhibitory balance

Synapses are highly plastic and are modified by changes in patterns of neural activity or sensory experience. Plasticity of cortical excitatory synapses is thought to be important for learning and memory, leading to alterations in sensory representations and cognitive maps. However, these changes must be coordinated across other synapses within local circuits to preserve neural coding schemes and the organization of excitatory and inhibitory inputs, i.e., excitatory-inhibitory balance. Recent studies indicate that inhibitory synapses are also plastic and are controlled directly by a large number of neuromodulators, particularly during episodes of learning. Many modulators transiently alter excitatory-inhibitory balance by decreasing inhibition, and thus disinhibition has emerged as a major mechanism by which neuromodulation might enable long-term synaptic modifications naturally. This review examines the relationships between neuromodulation and synaptic plasticity, focusing on the induction of long-term changes that collectively enhance cortical excitatory-inhibitory balance for improving perception and behavior.

Inhibitory and excitatory spike-timing-dependent plasticity in the auditory cortex

Synapses are plastic and can be modified by changes in spike timing. Whereas most studies of long-term synaptic plasticity focus on excitation, inhibitory plasticity may be critical for controlling information processing, memory storage, and overall excitability in neural circuits. Here we examine spike-timing-dependent plasticity (STDP) of inhibitory synapses onto layer 5 neurons in slices of mouse auditory cortex, together with concomitant STDP of excitatory synapses. Pairing pre- and postsynaptic spikes potentiated inhibitory inputs irrespective of precise temporal order within ∼10 ms. This was in contrast to excitatory inputs, which displayed an asymmetrical STDP time window. These combined synaptic modifications both required NMDA receptor activation and adjusted the excitatory-inhibitory ratio of events paired with postsynaptic spiking. Finally, subthreshold events became suprathreshold, and the time window between excitation and inhibition became more precise. These findings demonstrate that cortical inhibitory plasticity requires interactions with co-activated excitatory synapses to properly regulate excitatory-inhibitory balance.

A coding transformation for temporally structured sounds within auditory cortical neurons

Although the coding transformation between visual thalamus and cortex has been known for over 50 years, whether a similar transformation occurs between auditory thalamus and cortex has remained elusive. Such a transformation may occur for time-varying sounds, such as music or speech. Most subcortical neurons explicitly encode the temporal structure of sounds with the temporal structure of their activity, but many auditory cortical neurons instead use a rate code. The mechanisms for this transformation from temporal code to rate code have remained unknown. Here we report that the membrane potential of rat auditory cortical neurons can show stimulus synchronization to rates up to 500 Hz, even when the spiking output does not. Synaptic inputs to rate-coding neurons arose in part from temporal-coding neurons but were transformed by voltage-dependent properties and push-pull excitatory-inhibitory interactions. This suggests that the transformation from temporal to rate code can be observed within individual cortical neurons.

Imbalance of excitation and inhibition at threshold level in the auditory cortex

The interplay of cortical excitation and inhibition is a fundamental feature of cortical information processing. Excitation and inhibition in single cortical neurons are balanced in their response to optimal sensory stimulation due to thalamocortical feedforward microcircuitry. It is unclear whether the balance between cortical excitation and inhibition is maintained at the threshold stimulus level. Using in vivo whole-cell patch-clamp recording of thalamocortical recipient neurons in the primary auditory cortex of mice, we examined the tone-evoked excitatory and inhibitory postsynaptic currents at threshold levels. Similar to previous reports, tone induced excitatory postsynaptic currents when the membrane potentials were held at 70 mV and inhibitory postsynaptic currents when the membrane potentials were held at 0 mV on single cortical neurons. This coupled excitation and inhibition is not demonstrated when threshold-level tone stimuli are presented. In most cases, tone induced only excitatory postsynaptic current. The best frequencies of excitatory and inhibitory responses were often different and thresholds of inhibitory responses were mostly higher than those of excitatory responses. Our data suggest that the excitatory and inhibitory inputs to single cortical neurons are imbalanced at the threshold level. This imbalance may result from the inherent dynamics of thalamocortical feedforward microcircuitry.

T-type calcium channels promote predictive homeostasis of input-output relations in thalamocortical neurons of lateral geniculate nucleus

A general theory views the function of all neurons as prediction, and one component of this theory is that of “predictive homeostasis” or “prediction error.” It is well established that sensory systems adapt so that neuronal output maintains sensitivity to sensory input, in accord with information theory. Predictive homeostasis applies the same principle at the cellular level, where the challenge is to maintain membrane excitability at the optimal homeostatic level so that spike generation is maximally sensitive to small gradations in synaptic drive. Negative feedback is a hallmark of homeostatic mechanisms, as exemplified by depolarization-activated potassium channels. In contrast, T-type calcium channels exhibit positive feedback that appears at odds with the theory. In thalamocortical neurons of lateral geniculate nucleus (LGN), T-type channels are capable of causing bursts of spikes with an all-or-none character in response to excitation from a hyperpolarized potential. This “burst mode” would partially uncouple visual input from spike output and reduce the information spikes convey about gradations in visual input. However, past observations of T-type-driven bursts may have resulted from unnaturally high membrane excitability. Here we have mimicked within rat brain slices the patterns of synaptic conductance that occur naturally during vision. In support of the theory of predictive homeostasis, we found that T-type channels restored excitability toward its homeostatic level during periods of hyperpolarization. Thus, activation of T-type channels allowed two retinal input spikes to cause one output spike on average, and we observed almost no instances in which output count exceeded input count (a “burst”). T-type calcium channels therefore help to maintain a single optimal mode of transmission rather than creating a second mode. More fundamentally our results support the general theory, which seeks to predict the properties of a neuron's ion channels and synapses given knowledge of natural patterns of synaptic input.

A push-pull CORF model of a simple cell with antiphase inhibition improves SNR and contour detection

We propose a computational model of a simple cell with push-pull inhibition, a property that is observed in many real simple cells. It is based on an existing model called Combination of Receptive Fields or CORF for brevity. A CORF model uses as afferent inputs the responses of model LGN cells with appropriately aligned center-surround receptive fields, and combines their output with a weighted geometric mean. The output of the proposed model simple cell with push-pull inhibition, which we call push-pull CORF, is computed as the response of a CORF model cell that is selective for a stimulus with preferred orientation and preferred contrast minus a fraction of the response of a CORF model cell that responds to the same stimulus but of opposite contrast. We demonstrate that the proposed push-pull CORF model improves signal-to-noise ratio (SNR) and achieves further properties that are observed in real simple cells, namely separability of spatial frequency and orientation as well as contrast-dependent changes in spatial frequency tuning. We also demonstrate the effectiveness of the proposed push-pull CORF model in contour detection, which is believed to be the primary biological role of simple cells. We use the RuG (40 images) and Berkeley (500 images) benchmark data sets of images with natural scenes and show that the proposed model outperforms, with very high statistical significance, the basic CORF model without inhibition, Gabor-based models with isotropic surround inhibition, and the Canny edge detector. The push-pull CORF model that we propose is a contribution to a better understanding of how visual information is processed in the brain as it provides the ability to reproduce a wider range of properties exhibited by real simple cells. As a result of push-pull inhibition a CORF model exhibits an improved SNR, which is the reason for a more effective contour detection.

Neuronal and perceptual differences in the temporal processing of darks and lights

Visual information is mediated by two major thalamic pathways that signal light decrements (OFF) and increments (ON) in visual scenes, the OFF pathway being faster than the ON. Here, we demonstrate that this OFF temporal advantage is transferred to visual cortex and has a correlate in human perception. OFF-dominated cortical neurons in cats responded ∼3 ms faster to visual stimuli than ON-dominated cortical neurons, and dark-mediated suppression in ON-dominated neurons peaked ∼14 ms faster than light-mediated suppression in OFF-dominated neurons. Consistent with the neuronal differences, human observers were 6-14 ms faster at detecting darks than lights and better at discriminating dark than light flickers. Neuronal and perceptual differences both vanished if backgrounds were biased toward darks. Our results suggest that the cortical OFF pathway is faster than the ON pathway at increasing and suppressing visual responses, and these differences have parallels in the human visual perception of lights and darks.

Sensory stimulation shifts visual cortex from synchronous to asynchronous states

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

Genetic disruption of the On visual pathway affects cortical orientation selectivity and contrast sensitivity in mice

The retina signals stimulus contrast via parallel On and Off pathways and sends the information to higher visual centers. Here we study the role of the On pathway using mice that have null mutations in the On-specific GRM6 receptor in the retina (Pinto LH, Vitaterna MH, Shimomura K, Siepka SM, Balannik V, McDearmon EL, Omura C, Lumayag S, Invergo BM, Brandon M, Glawe B, Cantrell DR, Donald R, Inayat S, Olvera MA, Vessey KA, Kirstan A, McCall MA, Maddox D, Morgans CW, Young B, Pletcher MT, Mullins RF, Troy JB, Takahashi JS. Vis Neurosci 24: 111-123, 2007; Maddox DM, Vessey KA, Yarbrough GL, Invergo BM, Cantrell DR, Inayat S, Balannik V, Hicks WL, Hawes NL, Byers S, Smith RS, Hurd R, Howell D, Gregg RG, Chang B, Naggert JK, Troy JB, Pinto LH, Nishina PM, McCall MA. J Physiol 586: 4409-4424, 2008). In these "nob" mice, single unit recordings in the primary visual cortex (V1) reveal degraded selectivity for orientations due to an increased response at nonpreferred orientations. Contrast sensitivity in the nob mice is reduced with severe deficits at low contrast, consistent with the phenotype of night blindness in human patients with mutations in Grm6. These cortical deficits can be largely explained by reduced input drive and increased response variability seen in nob V1. Interestingly, increased variability is also observed in the superior colliculus of these mice but does not affect its tuning properties. Further, the increased response variability in the nob mice is traced to the retina, a result phenocopied by acute pharmacological blockade of the On pathway in wild-type retina. Together, our results suggest that the On and Off pathways normally interact to increase response reliability in the retina, which in turn propagates to various central visual targets and affects their functional properties.

Binocular integration and disparity selectivity in mouse primary visual cortex

Signals from the two eyes are first integrated in primary visual cortex (V1). In many mammals, this binocular integration is an important first step in the development of stereopsis, the perception of depth from disparity. Neurons in the binocular zone of mouse V1 receive inputs from both eyes, but it is unclear how that binocular information is integrated and whether this integration has a function similar to that found in other mammals. Using extracellular recordings, we demonstrate that mouse V1 neurons are tuned for binocular disparities, or spatial differences, between the inputs from each eye, thus extracting signals potentially useful for estimating depth. The disparities encoded by mouse V1 are significantly larger than those encoded by cat and primate. Interestingly, these larger disparities correspond to distances that are likely to be ecologically relevant in natural viewing, given the stereo-geometry of the mouse visual system. Across mammalian species, it appears that binocular integration is a common cortical computation used to extract information relevant for estimating depth. As such, it is a prime example of how the integration of multiple sensory signals is used to generate accurate estimates of properties in our environment.

Top-down modulation on perceptual decision with balanced inhibition through feedforward and feedback inhibitory neurons

Recent physiological studies have shown that neurons in various regions of the central nervous systems continuously receive noisy excitatory and inhibitory synaptic inputs in a balanced and covaried fashion. While this balanced synaptic input (BSI) is typically described in terms of maintaining the stability of neural circuits, a number of experimental and theoretical studies have suggested that BSI plays a proactive role in brain functions such as top-down modulation for executive control. Two issues have remained unclear in this picture. First, given the noisy nature of neuronal activities in neural circuits, how do the modulatory effects change if the top-down control implements BSI with different ratios between inhibition and excitation? Second, how is a top-down BSI realized via only excitatory long-range projections in the neocortex? To address the first issue, we systematically tested how the inhibition/excitation ratio affects the accuracy and reaction times of a spiking neural circuit model of perceptual decision. We defined an energy function to characterize the network dynamics, and found that different ratios modulate the energy function of the circuit differently and form two distinct functional modes. To address the second issue, we tested BSI with long-distance projection to inhibitory neurons that are either feedforward or feedback, depending on whether these inhibitory neurons do or do not receive inputs from local excitatory cells, respectively. We found that BSI occurs in both cases. Furthermore, when relying on feedback inhibitory neurons, through the recurrent interactions inside the circuit, BSI dynamically and automatically speeds up the decision by gradually reducing its inhibitory component in the course of a trial when a decision process takes too long.

Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo

Action Potential (APs) patterns of sensory cortex neurons encode a variety of stimulus features, but how can a neuron change the feature to which it responds? Here, we show that in vivo a spike-timing-dependent plasticity (STDP) protocol—consisting of pairing a postsynaptic AP with visually driven presynaptic inputs—modifies a neurons' AP-response in a bidirectional way that depends on the relative AP-timing during pairing. Whereas postsynaptic APs repeatedly following presynaptic activation can convert subthreshold into suprathreshold responses, APs repeatedly preceding presynaptic activation reduce AP responses to visual stimulation. These changes were paralleled by restructuring of the neurons response to surround stimulus locations and membrane-potential time-course. Computational simulations could reproduce the observed subthreshold voltage changes only when presynaptic temporal jitter was included. Together this shows that STDP rules can modify output patterns of sensory neurons and the timing of single-APs plays a crucial role in sensory coding and plasticity.

DOI:http://dx.doi.org/10.7554/eLife.00012.001

Nerve cells, called neurons, are one of the core components of the brain and form complex networks by connecting to other neurons via long, thin ‘wire-like’ processes called axons. Axons can extend across the brain, enabling neurons to form connections—or synapses—with thousands of others. It is through these complex networks that incoming information from sensory organs, such as the eye, is propagated through the brain and encoded.

The basic unit of communication between neurons is the action potential, often called a ‘spike’, which propagates along the network of axons and, through a chemical process at synapses, communicates with the postsynaptic neurons that the axon is connected to. These action potentials excite the neuron that they arrive at, and this excitatory process can generate a new action potential that then propagates along the axon to excite additional target neurons. In the visual areas of the cortex, neurons respond with action potentials when they ‘recognize’ a particular feature in a scene—a process called tuning. How a neuron becomes tuned to certain features in the world and not to others is unclear, as are the rules that enable a neuron to change what it is tuned to. What is clear, however, is that to understand this process is to understand the basis of sensory perception.

Memory storage and formation is thought to occur at synapses. The efficiency of signal transmission between neurons can increase or decrease over time, and this process is often referred to as synaptic plasticity. But for these synaptic changes to be transmitted to target neurons, the changes must alter the number of action potentials. Although it has been shown in vitro that the efficiency of synaptic transmission—that is the strength of the synapse—can be altered by changing the order in which the pre- and postsynaptic cells are activated (referred to as ‘Spike-timing-dependent plasticity’), this has never been shown to have an effect on the number of action potentials generated in a single neuron in vivo. It is therefore unknown whether this process is functionally relevant.

Now Pawlak et al. report that spike-timing-dependent plasticity in the visual cortex of anaesthetized rats can change the spiking of neurons in the visual cortex. They used a visual stimulus (a bar flashed up for half a second) to activate a presynaptic cell, and triggered a single action potential in the postsynaptic cell a very short time later. By repeatedly activating the cells in this way, they increased the strength of the synaptic connection between the two neurons. After a small number of these pairing activations, presenting the visual stimulus alone to the presynaptic cell was enough to trigger an action potential (a suprathreshold response) in the postsynaptic neuron—even though this was not the case prior to the pairing.

This study shows that timing rules known to change the strength of synaptic connections—and proposed to underlie learning and memory—have functional relevance in vivo, and that the timing of single action potentials can change the functional status of a cortical neuron.

DOI:http://dx.doi.org/10.7554/eLife.00012.002

Extracellular Ca2+ fluctuations in vivo affect afterhyperpolarization potential and modify firing patterns of neocortical neurons

Neocortical neurons can be classified in four major electrophysiological types according to their pattern of discharge: regular-spiking (RS), intrinsically-bursting (IB), fast-rhythmic-bursting (FRB), and fast-spiking (FS). Previously, we have shown that these firing patterns are not fixed and can change as a function of membrane potential and states of vigilance. Other studies have reported that extracellular calcium concentration ([Ca(2+)]o) fluctuates as a function of the phase of the cortical slow oscillation. In the present study we investigated how spontaneous and induced changes in [Ca(2+)]o affect the properties of action potentials (APs) and firing patterns in cortical neurons in vivo. Intracellular recordings were performed in cats anesthetized with ketamine-xylazine during spontaneous [Ca(2+)]o fluctuation and while changing [Ca(2+)]o with reverse microdialysis. When [Ca(2+)]o fluctuated spontaneously according to the phase of the slow oscillation, we found an increase of the firing threshold and a decrease of the afterhyperpolarization (AHP) amplitude during the depolarizing (active, up) phase of the slow oscillation and some neurons also changed their firing pattern as compared with the hyperpolarizing (silent, down) phase. Induced changes in [Ca(2+)]o significantly affected the AP properties in all neurons. The AHP amplitude was increased in high calcium conditions and decreased in low calcium conditions, in particular the earliest components. Modulation of spike AHP resulted in notable modulation of intrinsic firing pattern and some RS neurons revealed burst firing when [Ca(2+)]o was decreased. We also found an increase in AHP amplitude in high [Ca(2+)]o with in vitro preparation. We suggest that during spontaneous network oscillations in vivo, the dynamic changes of firing patterns depend partially on fluctuations of the [Ca(2+)]o.

Network and neuronal membrane properties in hybrid networks reciprocally regulate selectivity to rapid thalamocortical inputs

Rapidly changing environments require rapid processing from sensory inputs. Varying deflection velocities of a rodent's primary facial vibrissa cause varying temporal neuronal activity profiles within the ventral posteromedial thalamic nucleus. Local neuron populations in a single somatosensory layer 4 barrel transform sparsely coded input into a spike count based on the input's temporal profile. We investigate this transformation by creating a barrel-like hybrid network with whole cell recordings of in vitro neurons from a cortical slice preparation, embedding the biological neuron in the simulated network by presenting virtual synaptic conductances via a conductance clamp. Utilizing the hybrid network, we examine the reciprocal network properties (local excitatory and inhibitory synaptic convergence) and neuronal membrane properties (input resistance) by altering the barrel population response to diverse thalamic input. In the presence of local network input, neurons are more selective to thalamic input timing; this arises from strong feedforward inhibition. Strongly inhibitory (damping) network regimes are more selective to timing and less selective to the magnitude of input but require stronger initial input. Input selectivity relies heavily on the different membrane properties of excitatory and inhibitory neurons. When inhibitory and excitatory neurons had identical membrane properties, the sensitivity of in vitro neurons to temporal vs. magnitude features of input was substantially reduced. Increasing the mean leak conductance of the inhibitory cells decreased the network's temporal sensitivity, whereas increasing excitatory leak conductance enhanced magnitude sensitivity. Local network synapses are essential in shaping thalamic input, and differing membrane properties of functional classes reciprocally modulate this effect.

Location-dependent effects of inhibition on local spiking in pyramidal neuron dendrites

Cortical computations are critically dependent on interactions between pyramidal neurons (PNs) and a menagerie of inhibitory interneuron types. A key feature distinguishing interneuron types is the spatial distribution of their synaptic contacts onto PNs, but the location-dependent effects of inhibition are mostly unknown, especially under conditions involving active dendritic responses. We studied the effect of somatic vs. dendritic inhibition on local spike generation in basal dendrites of layer 5 PNs both in neocortical slices and in simple and detailed compartmental models, with equivalent results: somatic inhibition divisively suppressed the amplitude of dendritic spikes recorded at the soma while minimally affecting dendritic spike thresholds. In contrast, distal dendritic inhibition raised dendritic spike thresholds while minimally affecting their amplitudes. On-the-path dendritic inhibition modulated both the gain and threshold of dendritic spikes depending on its distance from the spike initiation zone. Our findings suggest that cortical circuits could assign different mixtures of gain vs. threshold inhibition to different neural pathways, and thus tailor their local computations, by managing their relative activation of soma- vs. dendrite-targeting interneurons.

The accuracy of membrane potential reconstruction based on spiking receptive fields

A common technique used to study the response selectivity of neurons is to measure the relationship between sensory stimulation and action potential responses. Action potentials, however, are only indirectly related to the synaptic inputs that determine the underlying, subthreshold, response selectivity. We present a method to predict membrane potential, the measurable result of the convergence of synaptic inputs, based on spike rate alone and then test its utility by comparing predictions to actual membrane potential recordings from simple cells in primary visual cortex. Using a noise stimulus, we found that spike rate receptive fields were in precise correspondence with membrane potential receptive fields (R(2) = 0.74). On average, spike rate alone could predict 44% of membrane potential fluctuations to dynamic noise stimuli, demonstrating the utility of this method to extract estimates of subthreshold responses. We also found that the nonlinear relationship between membrane potential and spike rate could also be extracted from spike rate data alone by comparing predictions from the noise stimulus with the actual spike rate. Our analysis reveals that linear receptive field models extracted from noise stimuli accurately reflect the underlying membrane potential selectivity and thus represent a method to generate estimates of the underlying average membrane potential from spike rate data alone.

A general principle of neural arbor branch density

The tree-like structures of a neuron that are responsible for distributing (axons) or collecting (dendrites) information over a region of the brain are called arbors. The size of the territory occupied by an arbor and the density of the arbor branches within that territory are important for computation because these factors determine what fraction of a neural map is sampled by a single cell and at what resolution [1]. Arbor territory size and branch density can vary by many orders of magnitude; however, we have identified a universal relationship between these two physical properties revealing a general neural architectural design principle. All of the arbors (axons and dendrites) we have studied (including fish retinal ganglion cells, rodent Purkinje cells, and the cortical arbors of various neural classes from rat, cat, monkey, and human) are found to be systematically less dense when they cover larger territories. This relationship can be described as a power law. Of several simple biological explanations explored, we find that this relationship is most consistent with a design principle that conserves the average number of connections between pairs of arbors of different sizes.

Broad inhibition sharpens orientation selectivity by expanding input dynamic range in mouse simple cells

Orientation selectivity (OS) is an emergent property in the primary visual cortex (V1). How OS arises from synaptic circuits remains unsolved. Here, in vivo whole-cell recordings in the mouse V1 revealed that simple cells received broadly tuned excitation and even more broadly tuned inhibition. Excitation and inhibition shared a similar orientation preference and temporally overlapped substantially. Neuron modeling and dynamic-clamp recording further revealed that excitatory inputs alone would result in membrane potential responses with significantly attenuated selectivity, due to a saturating input-output function of the membrane filtering. Inhibition ameliorated the attenuation of excitatory selectivity by expanding the input dynamic range and caused additional sharpening of output responses beyond unselectively suppressing responses at all orientations. This "blur-sharpening" effect allows selectivity conveyed by excitatory inputs to be better expressed, which may be a general mechanism underlying the generation of feature-selective responses in the face of strong excitatory inputs that are weakly biased.

Spikes with short inter-spike intervals in frog retinal ganglion cells are more correlated with their adjacent neurons' activities

Correlated firings among neurons have been extensively investigated; however, previous studies on retinal ganglion cell (RGC) population activities were mainly based on analyzing the correlated activities between the entire spike trains. In the present study, the correlation properties were explored based on burst-like activities and solitary spikes separately. The results indicate that: (1) burst-like activities were more correlated with other neurons' activities; (2) burst-like spikes correlated with their neighboring neurons represented a smaller receptive field than that of correlated solitary spikes. These results suggest that correlated burst-like spikes should be more efficient in signal transmission, and could encode more detailed spatial information.

The DIADEM data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions

The comprehensive characterization of neuronal morphology requires tracing extensive axonal and dendritic arbors imaged with light microscopy into digital reconstructions. Considerable effort is ongoing to automate this greatly labor-intensive and currently rate-determining process. Experimental data in the form of manually traced digital reconstructions and corresponding image stacks play a vital role in developing increasingly more powerful reconstruction algorithms. The DIADEM challenge (short for DIgital reconstruction of Axonal and DEndritic Morphology) successfully stimulated progress in this area by utilizing six data set collections from different animal species, brain regions, neuron types, and visualization methods. The original research projects that provided these data are representative of the diverse scientific questions addressed in this field. At the same time, these data provide a benchmark for the types of demands automated software must meet to achieve the quality of manual reconstructions while minimizing human involvement. The DIADEM data underwent extensive curation, including quality control, metadata annotation, and format standardization, to focus the challenge on the most substantial technical obstacles. This data set package is now freely released ( http://diademchallenge.org ) to train, test, and aid development of automated reconstruction algorithms.

An information integration model of the primary visual cortex under grating stimulations

During the course of information processing, a visual system extracts characteristic information of the visual image and integrates the spatial and temporal visual information simultaneously. In this study, we investigate the integration effect of neurons in the primary visual cortex (V1 area) under the grating stimulation. First, an information integration model was established based on the receptive field properties of the extracted features of the visual images features, the interaction between neurons and the nonlinear integration of those neurons. Then the neuropsychological experiments were designed both to provide parameters for the model and to verify its effect. The experimental results with factual visual image were largely consistent with the model's forecast output. This demonstrates that our model can truly reflect the integration effect of the primary visual system when being subjected to grating stimulations with different orientations. Our results indicate the primary visual system integrates the visual information in the following manner: it first extracts visual information through different types of receptive field, and then its neurons interact with each other in a non-linear manner, finally the neurons fire spikes recorded as responses to the visual stimulus.