Orientation tuning of input conductance, excitation, and inhibition in cat primary visual cortex

Estimation of excitatory and inhibitory synaptic conductance variations in motoneurons during locomotor-like rhythmic activity

The rhythmic activity of motoneurons (MNs) that underlies locomotion in mammals is generated by synaptic inputs from the locomotor network in the spinal cord. Thus, the quantitative estimation of excitatory and inhibitory synaptic conductances is essential to understand the mechanism by which the network generates the functional motor output. Conductance estimation is obtained from the voltage-current relationship measured by voltage-clamp- or current-clamp-recording with knowledge of the leak parameters of the recorded neuron. However, it is often difficult to obtain sufficient data to estimate synaptic conductances due to technical difficulties in electrophysiological experiments using in vivo or in vitro preparations. To address this problem, we estimated the average variations in excitatory and inhibitory synaptic conductance during a locomotion cycle from a single voltage trace without measuring the leak parameters. We found that the conductance variations can be accurately reconstructed from a voltage trace of 10 cycles by analyzing synthetic data generated from a computational model. Next, the conductance variations were estimated from mouse spinal MNs in vitro during drug-induced-locomotor-like activity. We found that the peak of excitatory conductance occurred during the depolarizing phase of the locomotor cycle, whereas the peak of inhibitory conductance occurred during the hyperpolarizing phase. These results suggest that the locomotor-like activity is generated by push-pull modulation via excitatory and inhibitory synaptic inputs.

Assessment of Methods for the Intracellular Blockade of GABAA Receptors

Selective blockade of inhibitory synaptic transmission onto specific neurons is a useful tool for dissecting the excitatory and inhibitory synaptic components of ongoing network activity. To achieve this, intracellular recording with a patch solution capable of blocking GABA_A receptors has advantages over other manipulations, such as pharmacological application of GABAergic antagonists or optogenetic inhibition of populations of interneurones, in that the majority of inhibitory transmission is unaffected and hence the remaining network activity preserved. Here, we assess three previously described methods to block inhibition: intracellular application of the molecules picrotoxin, 4,4’-dinitro-stilbene-2,2’-disulphonic acid (DNDS) and 4,4’-diisothiocyanostilbene-2,2’-disulphonic acid (DIDS). DNDS and picrotoxin were both found to be ineffective at blocking evoked, monosynaptic inhibitory postsynaptic currents (IPSCs) onto mouse CA1 pyramidal cells. An intracellular solution containing DIDS and caesium fluoride, but lacking nucleotides ATP and GTP, was effective at decreasing the amplitude of IPSCs. However, this effect was found to be independent of DIDS, and the absence of intracellular nucleotides, and was instead due to the presence of fluoride ions in this intracellular solution, which also blocked spontaneously occurring IPSCs during hippocampal sharp waves. Critically, intracellular fluoride ions also caused a decrease in both spontaneous and evoked excitatory synaptic currents and precluded the inclusion of nucleotides in the intracellular solution. Therefore, of the methods tested, only fluoride ions were effective for intracellular blockade of IPSCs but this approach has additional cellular effects reducing its selectivity and utility.

Efficient codes and balanced networks

Recent years have seen a growing interest in inhibitory interneurons and their circuits. A striking property of cortical inhibition is how tightly it balances excitation. Inhibitory currents not only match excitatory currents on average, but track them on a millisecond time scale, whether they are caused by external stimuli or spontaneous fluctuations. We review, together with experimental evidence, recent theoretical approaches that investigate the advantages of such tight balance for coding and computation. These studies suggest a possible revision of the dominant view that neurons represent information with firing rates corrupted by Poisson noise. Instead, tight excitatory/inhibitory balance may be a signature of a highly cooperative code, orders of magnitude more precise than a Poisson rate code. Moreover, tight balance may provide a template that allows cortical neurons to construct high-dimensional population codes and learn complex functions of their inputs.

Itinerancy between attractor states in neural systems

Converging evidence from neural, perceptual and simulated data suggests that discrete attractor states form within neural circuits through learning and development. External stimuli may bias neural activity to one attractor state or cause activity to transition between several discrete states. Evidence for such transitions, whose timing can vary across trials, is best accrued through analyses that avoid any trial-averaging of data. One such method, hidden Markov modeling, has been effective in this context, revealing state transitions in many neural circuits during many tasks. Concurrently, modeling efforts have revealed computational benefits of stimulus processing via transitions between attractor states. This review describes the current state of the field, with comments on how its perceived limitations have been addressed.

Inhibition Controls Asynchronous States of Neuronal Networks

Computations in cortical circuits require action potentials from excitatory and inhibitory neurons. In this mini-review, I first provide a quick overview of findings that indicate that GABAergic neurons play a fundamental role in coordinating spikes and generating synchronized network activity. Next, I argue that these observations helped popularize the notion that network oscillations require a high degree of spike correlations among interneurons which, in turn, produce synchronous inhibition of the local microcircuit. The aim of this text is to discuss some recent experimental and computational findings that support a complementary view: one in which interneurons participate actively in producing asynchronous states in cortical networks. This requires a proper mixture of shared excitation and inhibition leading to asynchronous activity between neighboring cells. Such contribution from interneurons would be extremely important because it would tend to reduce the spike correlation between neighboring pyramidal cells, a drop in redundancy that could enhance the information-processing capacity of neural networks.

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.

NMDA Receptors Multiplicatively Scale Visual Signals and Enhance Directional Motion Discrimination in Retinal Ganglion Cells

Postsynaptic responses in many CNS neurons are typically small and variable, often making it difficult to distinguish physiologically relevant signals from background noise. To extract salient information, neurons are thought to integrate multiple synaptic inputs and/or selectively amplify specific synaptic activation patterns. Here, we present evidence for a third strategy: directionally selective ganglion cells (DSGCs) in the mouse retina multiplicatively scale visual signals via a mechanism that requires both nonlinear NMDA receptor (NMDAR) conductances in DSGC dendrites and directionally tuned inhibition provided by the upstream retinal circuitry. Postsynaptic multiplication enables DSGCs to discriminate visual motion more accurately in noisy visual conditions without compromising directional tuning. These findings demonstrate a novel role for NMDARs in synaptic processing and provide new insights into how synaptic and network features interact to accomplish physiologically relevant neural computations.

Effects of GABAA kinetics on cortical population activity: computational studies and physiological confirmations

Voltage-sensitive dye (VSD) imaging produces an unprecedented real-time and high-resolution mesoscopic signal to measure the cortical population activity. We have previously shown that the neuronal compartments contributions to the signal are dynamic and stimulus-dependent (Chemla S, Chavane F. Neuroimage 53: 420-438, 2010). Moreover, the VSD signal can also be strongly affected by the network state, such as in anesthetized vs. awake preparations. Here, we investigated the impact of the network state, through GABAA receptors modulation, on the VSD signal using a computational approach. We therefore systematically measured the effect of the GABAA-mediated inhibitory postsynaptic potentials (IPSPs) decay time constant (τG) on our modeled VSD response to an input stimulus of increasing strength. Our simulations suggest that τG strongly modulates the dynamics of the VSD signal, affecting the amplitude, input response function, and the transient balance of excitation and inhibition. We confirmed these predictions experimentally on awake and anesthetized monkeys, comparing VSD responses to drifting gratings stimuli of various contrasts. Lastly, one in vitro study has suggested that GABAA receptors may also be directly affected by the VSDs themselves (Mennerick S, Chisari M, Shu H, Taylor A, Vasek M, Eisenman L, Zorumski C. J Neurosci 30: 2871-2879, 2010). Our modeling approach suggests that the type of modulation described in this study would actually have a negligible influence on the population response. This study highlights that functional results acquired with different techniques and network states must be compared with caution. Biophysical models are proposed here as an adequate tool to delineate the domain of VSD data interpretation.

Subcellular Imbalances in Synaptic Activity

The dynamic interactions between synaptic excitation and inhibition (E/I) shape membrane potential fluctuations and determine patterns of neuronal outputs; however, the spatiotemporal organization of these interactions within a single cell is poorly understood. Here, we investigated the relationship between local synaptic excitation and global inhibition in hippocampal pyramidal neurons using functional dendrite imaging in combination with whole-cell recordings of inhibitory postsynaptic currents. We found that the sums of spine inputs over dendritic trees were counterbalanced by a proportional amount of somatic inhibitory inputs. This online E/I correlation was maintained in dendritic segments that were longer than 50 μm. However, at the single spine level, only 22% of the active spines were activated with inhibitory inputs. This inhibition-coupled activity occurred mainly in the spines with large heads. These results shed light on a microscopic E/I-balancing mechanism that operates at selected synapses and that may increase the accuracy of neural information.

Weak signal amplification and detection by higher-order sensory neurons

Sensory systems must extract behaviorally relevant information and therefore often exhibit a very high sensitivity. How the nervous system reaches such high sensitivity levels is an outstanding question in neuroscience. Weakly electric fish (Apteronotus leptorhynchus/albifrons) are an excellent model system to address this question because detailed background knowledge is available regarding their behavioral performance and its underlying neuronal substrate. Apteronotus use their electrosense to detect prey objects. Therefore, they must be able to detect electrical signals as low as 1 μV while using a sensory integration time of <200 ms. How these very weak signals are extracted and amplified by the nervous system is not yet understood. We studied the responses of cells in the early sensory processing areas, namely, the electroreceptor afferents (EAs) and pyramidal cells (PCs) of the electrosensory lobe (ELL), the first-order electrosensory processing area. In agreement with previous work we found that EAs cannot encode very weak signals with a spike count code. However, PCs can encode prey mimic signals by their firing rate, revealing a huge signal amplification between EAs and PCs and also suggesting differences in their stimulus encoding properties. Using a simple leaky integrate-and-fire (LIF) model we predict that the target neurons of PCs in the midbrain torus semicircularis (TS) are able to detect very weak signals. In particular, TS neurons could do so by assuming biologically plausible convergence rates as well as very simple decoding strategies such as temporal integration, threshold crossing, and combining the inputs of PCs.

Effect of Contrast on Visual Spatial Summation in Different Cell Categories in Cat Primary Visual Cortex

Multiple cell classes have been found in the primary visual cortex, but the relationship between cell types and spatial summation has seldom been studied. Parvalbumin-expressing inhibitory interneurons can be distinguished from pyramidal neurons based on their briefer action potential durations. In this study, we classified V1 cells into fast-spiking units (FSUs) and regular-spiking units (RSUs) and then examined spatial summation at high and low contrast. Our results revealed that the excitatory classical receptive field and the suppressive non-classical receptive field expanded at low contrast for both FSUs and RSUs, but the expansion was more marked for the RSUs than for the FSUs. For most V1 neurons, surround suppression varied as the contrast changed from high to low. However, FSUs exhibited no significant difference in the strength of suppression between high and low contrast, although the overall suppression decreased significantly at low contrast for the RSUs. Our results suggest that the modulation of spatial summation by stimulus contrast differs across populations of neurons in the cat primary visual cortex.

Synaptic Basis for Differential Orientation Selectivity between Complex and Simple Cells in Mouse Visual Cortex

In the primary visual cortex (V1), orientation-selective neurons can be categorized into simple and complex cells primarily based on their receptive field (RF) structures. In mouse V1, although previous studies have examined the excitatory/inhibitory interplay underlying orientation selectivity (OS) of simple cells, the synaptic bases for that of complex cells have remained obscure. Here, by combining in vivo loose-patch and whole-cell recordings, we found that complex cells, identified by their overlapping on/off subfields, had significantly weaker OS than simple cells at both spiking and subthreshold membrane potential response levels. Voltage-clamp recordings further revealed that although excitatory inputs to complex and simple cells exhibited a similar degree of OS, inhibition in complex cells was more narrowly tuned than excitation, whereas in simple cells inhibition was more broadly tuned than excitation. The differential inhibitory tuning can primarily account for the difference in OS between complex and simple cells. Interestingly, the differential synaptic tuning correlated well with the spatial organization of synaptic input: the inhibitory visual RF in complex cells was more elongated in shape than its excitatory counterpart and also was more elongated than that in simple cells. Together, our results demonstrate that OS of complex and simple cells is differentially shaped by cortical inhibition based on its orientation tuning profile relative to excitation, which is contributed at least partially by the spatial organization of RFs of presynaptic inhibitory neurons.

SIGNIFICANCE STATEMENT

Simple and complex cells, two classes of principal neurons in the primary visual cortex (V1), are generally thought to be equally selective for orientation. In mouse V1, we report that complex cells, identified by their overlapping on/off subfields, has significantly weaker orientation selectivity (OS) than simple cells. This can be primarily attributed to the differential tuning selectivity of inhibitory synaptic input: inhibition in complex cells is more narrowly tuned than excitation, whereas in simple cells inhibition is more broadly tuned than excitation. In addition, there is a good correlation between inhibitory tuning selectivity and the spatial organization of inhibitory inputs. These complex and simple cells with differential degree of OS may provide functionally distinct signals to different downstream targets.

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.

Parvalbumin Interneurons: All Forest, No Trees

There has been a surge of interest in how inhibitory neurons influence the output of local circuits in the brain. In this issue of Neuron, Scholl et al. (2015) provide a compelling argument for what one class of inhibitory neurons actually does.

Balance or imbalance: inhibitory circuits for direction selectivity in the auditory system

The auditory system detects and processes dynamic sound information transmitted in the environment. Other than the basic acoustic parameters, such as frequency, amplitude and phase, the time-varying changes of these parameters must also be encoded in our brain. Frequency-modulated (FM) sound is socially and environmentally significant, and the direction of FM sweeps is essential for animal communication and human speech. Many auditory neurons selectively respond to the directional change of such FM signals. In the past half century, our knowledge of auditory representation and processing has been updated frequently, due to technological advancement. Recently, in vivo whole-cell voltage clamp recordings have been applied to different brain regions in sensory systems. These recordings illustrate the synaptic mechanisms underlying basic sensory information processing and provide profound insights toward our understanding of neural circuits for complex signal analysis. In this review, we summarize the major findings of direction selectivity at several key auditory regions and emphasize on the recent discoveries on the synaptic mechanisms for direction selectivity in the auditory system. We conclude this review by describing promising technical developments in dissecting neural circuits and future directions in the study of complex sound analysis.

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.

Attention and normalization circuits in macaque V1

Attention affects neuronal processing and improves behavioural performance. In extrastriate visual cortex these effects have been explained by normalization models, which assume that attention influences the circuit that mediates surround suppression. While normalization models have been able to explain attentional effects, their validity has rarely been tested against alternative models. Here we investigate how attention and surround/mask stimuli affect neuronal firing rates and orientation tuning in macaque V1. Surround/mask stimuli provide an estimate to what extent V1 neurons are affected by normalization, which was compared against effects of spatial top down attention. For some attention/surround effect comparisons, the strength of attentional modulation was correlated with the strength of surround modulation, suggesting that attention and surround/mask stimulation (i.e. normalization) might use a common mechanism. To explore this in detail, we fitted multiplicative and additive models of attention to our data. In one class of models, attention contributed to normalization mechanisms, whereas in a different class of models it did not. Model selection based on Akaike's and on Bayesian information criteria demonstrated that in most cells the effects of attention were best described by models where attention did not contribute to normalization mechanisms. This demonstrates that attentional influences on neuronal responses in primary visual cortex often bypass normalization mechanisms.

Inhibition facilitates direction selectivity in a noisy cortical environment

In a broad class of models, direction selectivity in primary visual cortical neurons arises from the linear summation of spatially offset and temporally lagged inputs combined with a spike threshold. Here, we characterize the robustness of this class of models to input noise and background activity that is uncorrelated with the visual stimulus. When only excitatory inputs were considered, moderate levels of noise substantially degraded direction selectivity. By contrast, the inclusion of inhibition produced a direction-selective neuron even at high noise levels. Moreover, if inhibitory inputs were tuned, mirroring excitatory inputs but lagging by a fixed delay, they promoted a highly direction-selective response by suppressing all excitatory inputs in the null direction while minimally affecting excitatory inputs in the preferred direction. Additionally, tuned inhibition strongly reduced trial-by-trial variability, such that the neuron produced a consistent direction-selective response to multiple presentation of the same stimulus. This work illustrates how inhibition could be used by cortical circuits to reliably extract information on a single-trial basis from feed-forward inputs in a noisy, high-background context.

Tasks for inhibitory interneurons in intact brain circuits

Synaptic inhibition, brought about by a rich variety of interneuron types, counters excitation, modulates the gain, timing, tuning, bursting properties of principal cell firing, and exerts selective filtering of synaptic excitation. At the network level, it allows for coordinating transient interactions among the principal cells to form cooperative assemblies for efficient transmission of information and routing of excitatory activity across networks, typically in the form of brain oscillations. Recent techniques based on targeted expression of neuronal activity modulators, such as optogenetics, allow physiological identification and perturbation of specific interneuron subtypes in the intact brain. Combined with large-scale recordings or imaging techniques, these approaches facilitate our understanding of the multiple roles of inhibitory interneurons in shaping circuit functions.

The effects of short-term synaptic depression at thalamocortical synapses on orientation tuning in cat V1

We examine the effects of short-term synaptic depression on the orientation tuning of the LGN input to simple cells in cat primary visual cortex (V1). The total LGN input has an untuned component as well as a tuned component, both of which grow with stimulus contrast. The untuned component is not visible in the firing rate responses of the simple cells. The suppression of the contribution of the untuned input component to firing rate responses is key to establishing orientation selectivity and its invariance with stimulus contrast. It has been argued that synaptic depression of LGN inputs could contribute to the selective suppression of the untuned component and thus contribute to the tuning observed in simple cells. We examine this using a model fit to the depression observed at thalamocortical synapses in-vivo, and compare this to an earlier model fit based on in-vitro observations. We examine the tuning of both the conductance and the firing rate induced in simple cells by the net LGN input. We find that depression causes minimal suppression of the untuned component. The primary effect of depression is to cause the contrast response curve to saturate at lower contrasts without differentially affecting the tuned vs. untuned components. This effect is slightly weaker for in-vivo vs. in-vitro parameters. Thus, synaptic depression of LGN inputs does not appreciably contribute to the orientation tuning of V1 simple cells.

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.

A review of the mechanisms by which attentional feedback shapes visual selectivity

The glut of information available for the brain to process at any given moment necessitates an efficient attentional system that can 'pick and choose' what information receives prioritized processing. A growing body of work, spanning numerous methodologies and species, reveals that one powerful way in which attending to an item separates the wheat from the chaff is by altering a basic response property in the brain: neuronal selectivity. Selectivity is a cornerstone response property, largely dictating our ability to represent and interact with the environment. Although it is likely that selectivity is altered throughout many brain areas, here we focus on how directing attention to an item affects selectivity in the visual system, where this response property is generally more well characterized. First, we review the neural architecture supporting selectivity, and then discuss the various changes that could occur in selectivity for an attended item. In a survey of the literature, spanning neurophysiology, neuroimaging and psychophysics, we reveal that there is general convergence regarding the manner with which selectivity is shaped by attentional feedback. In a nutshell, the literature suggests that the type of changes in selectivity that manifest appears to depend on the type of attention being deployed: whereas directing spatial attention towards an item only alters spatial selectivity, directing feature-based attention can alter the selectivity of attended features.

Equalizing excitation-inhibition ratios across visual cortical neurons

The relationship between synaptic excitation and inhibition (E/I ratio), two opposing forces in the mammalian cerebral cortex, affects many cortical functions like feature selectivity and gain^1,2. Individual pyramidal cells show stable E/I ratios in time despite fluctuating cortical activity levels because when excitation increases, inhibition increases proportionally through the increased recruitment of inhibitory neurons, a phenomenon referred to as excitation-inhibition balance^3–9. However, little is known about the distribution of E/I ratios across pyramidal cells. Through their highly divergent axons inhibitory neurons indiscriminately contact most neighboring pyramidal cells^10,11. Is inhibition homogeneously distributed or is it individually matched to the different amounts of excitation received by distinct pyramidal cells? Here we discover that pyramidal cells in layer 2/3 of mouse primary visual cortex (V1) each receive inhibition in a similar proportion to their excitation. As a consequence E/I ratios are equalized across pyramidal cells. This matched inhibition is mediated by parvalbumin-expressing (PV) but not somatostatin-expressing (SOM) inhibitory neurons and results from the independent adjustment of synapses originating from the same PV cell but targeting different pyramidal cells. Furthermore, this match is activity-dependent as it is disrupted by perturbing pyramidal cell activity. Thus, the equalization of E/I ratios across pyramidal cells reveals an unexpected degree of order in the spatial distribution of synaptic strengths and indicates that the relationship between cortex’s two opposing forces is stabilized not only in time but also in space.

The Touch and Zap method for in vivo whole-cell patch recording of intrinsic and visual responses of cortical neurons and glial cells

Whole-cell patch recording is an essential tool for quantitatively establishing the biophysics of brain function, particularly in vivo. This method is of particular interest for studying the functional roles of cortical glial cells in the intact brain, which cannot be assessed with extracellular recordings. Nevertheless, a reasonable success rate remains a challenge because of stability, recording duration and electrical quality constraints, particularly for voltage clamp, dynamic clamp or conductance measurements. To address this, we describe "Touch and Zap", an alternative method for whole-cell patch clamp recordings, with the goal of being simpler, quicker and more gentle to brain tissue than previous approaches. Under current clamp mode with a continuous train of hyperpolarizing current pulses, seal formation is initiated immediately upon cell contact, thus the "Touch". By maintaining the current injection, whole-cell access is spontaneously achieved within seconds from the cell-attached configuration by a self-limited membrane electroporation, or "Zap", as seal resistance increases. We present examples of intrinsic and visual responses of neurons and putative glial cells obtained with the revised method from cat and rat cortices in vivo. Recording parameters and biophysical properties obtained with the Touch and Zap method compare favourably with those obtained with the traditional blind patch approach, demonstrating that the revised approach does not compromise the recorded cell. We find that the method is particularly well-suited for whole-cell patch recordings of cortical glial cells in vivo, targeting a wider population of this cell type than the standard method, with better access resistance. Overall, the gentler Touch and Zap method is promising for studying quantitative functional properties in the intact brain with minimal perturbation of the cell's intrinsic properties and local network. Because the Touch and Zap method is performed semi-automatically, this approach is more reproducible and less dependent on experimenter technique.

Addition of visual noise boosts evoked potential-based brain-computer interface

Although noise has a proven beneficial role in brain functions, there have not been any attempts on the dedication of stochastic resonance effect in neural engineering applications, especially in researches of brain-computer interfaces (BCIs). In our study, a steady-state motion visual evoked potential (SSMVEP)-based BCI with periodic visual stimulation plus moderate spatiotemporal noise can achieve better offline and online performance due to enhancement of periodic components in brain responses, which was accompanied by suppression of high harmonics. Offline results behaved with a bell-shaped resonance-like functionality and 7–36% online performance improvements can be achieved when identical visual noise was adopted for different stimulation frequencies. Using neural encoding modeling, these phenomena can be explained as noise-induced input-output synchronization in human sensory systems which commonly possess a low-pass property. Our work demonstrated that noise could boost BCIs in addressing human needs.

Excitatory and inhibitory STDP jointly tune feedforward neural circuits to selectively propagate correlated spiking activity

Spike-timing-dependent plasticity (STDP) has been well established between excitatory neurons and several computational functions have been proposed in various neural systems. Despite some recent efforts, however, there is a significant lack of functional understanding of inhibitory STDP (iSTDP) and its interplay with excitatory STDP (eSTDP). Here, we demonstrate by analytical and numerical methods that iSTDP contributes crucially to the balance of excitatory and inhibitory weights for the selection of a specific signaling pathway among other pathways in a feedforward circuit. This pathway selection is based on the high sensitivity of STDP to correlations in spike times, which complements a recent proposal for the role of iSTDP in firing-rate based selection. Our model predicts that asymmetric anti-Hebbian iSTDP exceeds asymmetric Hebbian iSTDP for supporting pathway-specific balance, which we show is useful for propagating transient neuronal responses. Furthermore, we demonstrate how STDPs at excitatory-excitatory, excitatory-inhibitory, and inhibitory-excitatory synapses cooperate to improve the pathway selection. We propose that iSTDP is crucial for shaping the network structure that achieves efficient processing of synchronous spikes.

Scaling down of balanced excitation and inhibition by active behavioral states in auditory cortex

Cortical sensory processing is modulated by behavioral and cognitive states. How the modulation is achieved through impacting synaptic circuits remains largely unknown. In awake mouse auditory cortex, we reported that sensory-evoked spike responses of layer 2/3 (L2/3) excitatory cells were scaled down with preserved sensory tuning when animals transitioned from quiescence to active behaviors, while L4 and thalamic responses were unchanged. Whole-cell voltage-clamp recordings further revealed that tone-evoked synaptic excitation and inhibition exhibited a robust functional balance. Changes of behavioral state caused scaling down of excitation and inhibition at an approximately equal level in L2/3 cells, but no synaptic changes in L4 cells. This laminar-specific gain control could be attributed to an enhancement of L1–mediated inhibitory tone, with L2/3 parvalbumin inhibitory neurons suppressed as well. Thus, L2/3 circuits can adjust the salience of output in accordance with momentary behavioral demands while maintaining the sensitivity and quality of sensory processing.

Firing clamp: a novel method for single-trial estimation of excitatory and inhibitory synaptic neuronal conductances

Understanding non-stationary neuronal activity as seen in vivo requires estimation of both excitatory and inhibitory synaptic conductances from a single trial of recording. For this purpose, we propose a new intracellular recording method, called “firing clamp.” Synaptic conductances are estimated from the characteristics of artificially evoked probe spikes, namely the spike amplitude and the mean subthreshold potential, which are sensitive to both excitatory and inhibitory synaptic input signals. The probe spikes, timed at a fixed rate, are evoked in the dynamic-clamp mode by injected meander-like current steps, with the step duration depending on neuronal membrane voltage. We test the method with perforated-patch recordings from isolated cells stimulated by external application or synaptic release of transmitter, and validate the method with simulations of a biophysically-detailed neuron model. The results are compared with the conductance estimates based on conventional current-clamp recordings.

Premotor spinal network with balanced excitation and inhibition during motor patterns has high resilience to structural division

Direct measurements of synaptic inhibition (I) and excitation (E) to spinal motoneurons can provide an important insight into the organization of premotor networks. Such measurements of flexor motoneurons participating in motor patterns in turtles have recently demonstrated strong concurrent E and I as well as stochastic membrane potentials and irregular spiking in the adult turtle spinal cord. These findings represent a departure from the widespread acceptance of feedforward reciprocal rate models for spinal motor function. The apparent discrepancy has been reviewed as an experimental artifact caused by the distortion of local networks in the transected turtle spinal cord. We tested this assumption in the current study by performing experiments to assess the integrity of motor functions in the intact spinal cord and the cord transected at segments D9/D10. Excitatory and inhibitory synaptic inputs to motoneurons were estimated during rhythmic motor activity and demonstrated primarily intense inputs that consisted of qualitatively similar mixed E/I before and after the transection. To understand this high functional resilience, we used mathematical modeling of networks with recurrent connectivity that could potentially explain the balanced E/I. Both experimental and modeling data support the concept of a locally balanced premotor network consisting of recurrent E/I connectivity, in addition to the well known reciprocal network activity. The multifaceted synaptic connections provide spinal networks with a remarkable ability to remain functional after structural divisions.

A single GABAergic neuron mediates feedback of odor-evoked signals in the mushroom body of larval Drosophila

Inhibition has a central role in defining the selectivity of the responses of higher order neurons to sensory stimuli. However, the circuit mechanisms of regulation of these responses by inhibitory neurons are still unclear. In Drosophila, the mushroom bodies (MBs) are necessary for olfactory memory, and by implication for the selectivity of learned responses to specific odors. To understand the circuitry of inhibition in the calyx (the input dendritic region) of the MBs, and its relationship with MB excitatory activity, we used the simple anatomy of the Drosophila larval olfactory system to identify any inhibitory inputs that could contribute to the selectivity of MB odor responses. We found that a single neuron accounts for all detectable GABA innervation in the calyx of the MBs, and that this neuron has pre-synaptic terminals in the calyx and post-synaptic branches in the MB lobes (output axonal area). We call this neuron the larval anterior paired lateral (APL) neuron, because of its similarity to the previously described adult APL neuron. Reconstitution of GFP partners (GRASP) suggests that the larval APL makes extensive contacts with the MB intrinsic neurons, Kenyon Cells (KCs), but few contacts with incoming projection neurons (PNs). Using calcium imaging of neuronal activity in live larvae, we show that the larval APL responds to odors, in a manner that requires output from KCs. Our data suggest that the larval APL is the sole GABAergic neuron that innervates the MB input region and carries inhibitory feedback from the MB output region, consistent with a role in modulating the olfactory selectivity of MB neurons.

Large-scale axonal reorganization of inhibitory neurons following retinal lesions

The functional properties of adult cortical neurons are subject to alterations in sensory experience. Retinal lesions lead to remapping of cortical topography in the region of primary visual cortex representing the lesioned part of the retina, the lesion projection zone (LPZ), with receptive fields shifting to the intact parts of the retina. Neurons within the LPZ receive strengthened input from the surrounding region by growth of the plexus of excitatory long-range horizontal connections. Here, by combining cell type-specific labeling with a genetically engineered recombinant adeno-associated virus and in vivo two-photon microscopy in adult macaques, we showed that the remapping was also associated with alterations in the axonal arbors of inhibitory neurons, which underwent a parallel process of pruning and growth. The axons of inhibitory neurons located within the LPZ extended across the LPZ border, suggesting a mechanism by which new excitatory input arising from the peri-LPZ is balanced by reciprocal inhibition arising from the LPZ.

The function of metabotropic glutamate receptors in thalamus and cortex

Metabotropic glutamate receptors (mGluRs) are found throughout thalamus and cortex and are clearly important to circuit behavior in both structures, and so considering only participation of ionotropic glutamate receptors (e.g., [R,S]-α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid [AMPA] and N-methyl-d-aspartate receptors [NMDA] receptors) in glutamatergic processing would be an unfortunate oversimplification. These mGluRs are found both postsynaptically, on target cells of glutamatergic afferents, and presynaptically, on various synaptic terminals themselves, and when activated, they produce prolonged effects lasting at least hundreds of msec to several sec and perhaps longer. Two main types exist: activation of group I mGluRs causes postsynaptic depolarization, and group II, hyperpolarization. Both types are implicated in synaptic plasticity, both short term and long term. Their evident importance in functioning of thalamus and cortex makes it critical to develop a better understanding of how these receptors are normally activated, especially because they also seem implicated in a wide range of neurological and cognitive pathologies.

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.

GABAergic synapses: their plasticity and role in sensory cortex

The mammalian neocortex is composed of a variety of cell types organized in a highly interconnected circuit. GABAergic neurons account for only about 20% of cortical neurons. However, they show widespread connectivity and a high degree of diversity in morphology, location, electrophysiological properties and gene expression. In addition, distinct populations of inhibitory neurons have different sensory response properties, capacities for plasticity and sensitivities to changes in sensory experience. In this review we summarize experimental evidence regarding the properties of GABAergic neurons in primary sensory cortex. We will discuss how distinct GABAergic neurons and different forms of GABAergic inhibitory plasticity may contribute to shaping sensory cortical circuit activity and function.

Modulatory effects of activation of metabotropic glutamate receptors on GABAergic circuits in the mouse cortex

Metabotropic glutamate receptors (mGluRs) have a ubiquitous distribution in the central nervous system and often serve to regulate the release of neurotransmitters. We have previously shown that activation of both presynaptic and postsynaptic mGluRs can affect the gain of glutamatergic inputs in both thalamus and cortex. In the present study, we sought to determine the effect of mGluR activation on GABAergic inputs in cortex. Using whole cell recordings in a mouse slice preparation of either primary visual or auditory cortex (V1 or A1), we tested the effects on mGluRs by applying various agonists to the slice. Two pathways were tested in each area: the GABAergic inputs in layers 2/3 activated from layer 4 and the GABAergic inputs in layer 4 activated from adjacent layer 4. In both of these pathways, we found that activation of mGluRs significantly reduced the amplitude of the evoked inhibitory postsynaptic currents. Because the effects were not blocked by the addition of GDPβS to the recording electrode, and because mGluR agonists did not affect responses to photostimulation of GABA in a low-Ca(2+) and high-Mg(2+) bathing solution, we concluded this reduction was due to activation of presynaptic mGluRs. Furthermore, using specific mGluR agonists, we found that group II mGluRs, but not group I mGluRs, were involved in these modulatory effects. Because similar results were found in both pathways in V1 and A1, a possible cortical pattern for these effects is suggested.

Gamma frequency feedback inhibition accounts for key aspects of orientation selectivity in V1

There is now strong evidence that gamma frequency oscillations occur during the engagement of cortical regions. These oscillations involve gamma frequency feedback inhibition. Thus, understanding the properties of this form of inhibition is critical to understanding how excitation and inhibition interact to determine which cells fire and, more generally, how cortex performs computations. In previous work, we argued that gamma frequency inhibition performs a type of winner-take-all computation that obeys simple rules: 1) cells fire if their excitation is within E% of the cell with maximum excitation; 2) E%max is determined by the delay of feedback inhibition and the membrane time constant. This framework was previously applied to the best-studied cortical computation, orientation selectivity of cells in V1. Measurements show that orientation tuning is insensitive to illumination contrast. We showed that this finding can be simply explained by the E%max model. Recently, a new property of orientation selectivity has been discovered: orientation tuning varies with the phase of the gamma oscillation. Here, we show that this too can be simply explained by the E%max model. These successes suggest that simple rules underlie the selection of which cells fire in cortical networks.

Functional coupling from simple to complex cells in the visually driven cortical circuit

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

Sensory-evoked synaptic integration in cerebellar and cerebral cortical neurons

Neurons integrate synaptic inputs across time and space, a process that determines the transformation of input signals into action potential output. This article explores how synaptic integration contributes to the richness of sensory signalling in the cerebellar and cerebral cortices. Whether a neuron receives a few or a few thousand discrete inputs, most evoked synaptic activity generates only subthreshold membrane potential fluctuations. Sensory tuning of synaptic inputs is typically broad, but short-term dynamics and the interplay between excitation and inhibition restrict action potential firing to narrow windows of opportunity. We highlight the challenges and limitations of the use of somatic recordings in the study of synaptic integration and the importance of active dendritic mechanisms in sensory processing.

Computing local edge probability in natural scenes from a population of oriented simple cells

A key computation in visual cortex is the extraction of object contours, where the first stage of processing is commonly attributed to V1 simple cells. The standard model of a simple cell-an oriented linear filter followed by a divisive normalization-fits a wide variety of physiological data, but is a poor performing local edge detector when applied to natural images. The brain's ability to finely discriminate edges from nonedges therefore likely depends on information encoded by local simple cell populations. To gain insight into the corresponding decoding problem, we used Bayes's rule to calculate edge probability at a given location/orientation in an image based on a surrounding filter population. Beginning with a set of ∼ 100 filters, we culled out a subset that were maximally informative about edges, and minimally correlated to allow factorization of the joint on- and off-edge likelihood functions. Key features of our approach include a new, efficient method for ground-truth edge labeling, an emphasis on achieving filter independence, including a focus on filters in the region orthogonal rather than tangential to an edge, and the use of a customized parametric model to represent the individual filter likelihood functions. The resulting population-based edge detector has zero parameters, calculates edge probability based on a sum of surrounding filter influences, is much more sharply tuned than the underlying linear filters, and effectively captures fine-scale edge structure in natural scenes. Our findings predict nonmonotonic interactions between cells in visual cortex, wherein a cell may for certain stimuli excite and for other stimuli inhibit the same neighboring cell, depending on the two cells' relative offsets in position and orientation, and their relative activation levels.

Sublinear binocular integration preserves orientation selectivity in mouse visual cortex

Inputs from the two eyes are first combined in simple cells in the primary visual cortex. Consequently, visual cortical neurons need to have the flexibility to encode visual features under both monocular and binocular situations. Here we show that binocular orientation selectivity of mouse simple cells is nearly identical to monocular orientation selectivity in both anaesthetized and awake conditions. In vivo whole-cell recordings reveal that the binocular integration of membrane potential responses is sublinear. The sublinear integration keeps binocularly evoked depolarizations below threshold at non-preferred orientations, thus preserving orientation selectivity. Computational simulations based on measured synaptic conductances indicate that inhibition promotes sublinear binocular integration, which are further confirmed by experiments using genetic and pharmacological manipulations. Our findings therefore reveal a cellular mechanism for how visual system can switch effortlessly between monocular and binocular conditions. The same mechanism may apply to other sensory systems that also integrate multiple channels of inputs.

Strabismus disrupts binocular synaptic integration in primary visual cortex

Visual disruption early in development dramatically changes how primary visual cortex neurons integrate binocular inputs. The disruption is paradigmatic for investigating the synaptic basis of long-term changes in cortical function, because the primary visual cortex is the site of binocular convergence. The underlying alterations in circuitry by visual disruption remain poorly understood. Here we compare membrane potential responses, observed via whole-cell recordings in vivo, of primary visual cortex neurons in normal adult cats with those of cats in which strabismus was induced before the developmental critical period. In strabismic cats, we observed a dramatic shift in the ocular dominance distribution of simple cells, the first stage of visual cortical processing, toward responding to one eye instead of both, but not in complex cells, which receive inputs from simple cells. Both simple and complex cells no longer conveyed the binocular information needed for depth perception based on binocular cues. There was concomitant binocular suppression such that responses were weaker with binocular than with monocular stimulation. Our estimates of the excitatory and inhibitory input to single neurons indicate binocular suppression that was not evident in synaptic excitation, but arose de novo because of synaptic inhibition. Further constraints on circuit models of plasticity result from indications that the ratio of excitation to inhibition evoked by monocular stimulation decreased mainly for nonpreferred eye stimulation. Although we documented changes in synaptic input throughout primary visual cortex, a circuit model with plasticity at only thalamocortical synapses is sufficient to account for our observations.

Stereoscopic depth perception using a model based on the primary visual cortex

This work describes an approach inspired by the primary visual cortex using the stimulus response of the receptive field profiles of binocular cells for disparity computation. Using the energy model based on the mechanism of log-Gabor filters for disparity encodings, we propose a suitable model to consistently represent the complex cells by computing the wide bandwidths of the cortical cells. This way, the model ensures the general neurophysiological findings in the visual cortex (V1), emphasizing the physical disparities and providing a simple selection method for the complex cell response. The results suggest that our proposed approach can achieve better results than a hybrid model with phase-shift and position-shift using position disparity alone.

Cumulative latency advance underlies fast visual processing in desynchronized brain state

Fast sensory processing is vital for the animal to efficiently respond to the changing environment. This is usually achieved when the animal is vigilant, as reflected by cortical desynchronization. However, the neural substrate for such fast processing remains unclear. Here, we report that neurons in rat primary visual cortex (V1) exhibited shorter response latency in the desynchronized state than in the synchronized state. In vivo whole-cell recording from the same V1 neurons undergoing the two states showed that both the resting and visually evoked conductances were higher in the desynchronized state. Such conductance increases of single V1 neurons shorten the response latency by elevating the membrane potential closer to the firing threshold and reducing the membrane time constant, but the effects only account for a small fraction of the observed latency advance. Simultaneous recordings in lateral geniculate nucleus (LGN) and V1 revealed that LGN neurons also exhibited latency advance, with a degree smaller than that of V1 neurons. Furthermore, latency advance in V1 increased across successive cortical layers. Thus, latency advance accumulates along various stages of the visual pathway, likely due to a global increase of membrane conductance in the desynchronized state. This cumulative effect may lead to a dramatic shortening of response latency for neurons in higher visual cortex and play a critical role in fast processing for vigilant animals.

Preserved excitatory-inhibitory balance of cortical synaptic inputs following deprived eye stimulation after a saturating period of monocular deprivation in rats

Monocular deprivation (MD) during development leads to a dramatic loss of responsiveness through the deprived eye in primary visual cortical neurons, and to degraded spatial vision (amblyopia) in all species tested so far, including rodents. Such loss of responsiveness is accompanied since the beginning by a decreased excitatory drive from the thalamo-cortical inputs. However, in the thalamorecipient layer 4, inhibitory interneurons are initially unaffected by MD and their synapses onto pyramidal cells potentiate. It remains controversial whether ocular dominance plasticity similarly or differentially affects the excitatory and inhibitory synaptic conductances driven by visual stimulation of the deprived eye and impinging onto visual cortical pyramids, after a saturating period of MD. To address this issue, we isolated visually-driven excitatory and inhibitory conductances by in vivo whole-cell recordings from layer 4 regular-spiking neurons in the primary visual cortex (V1) of juvenile rats. We found that a saturating period of MD comparably reduced visually-driven excitatory and inhibitory conductances driven by visual stimulation of the deprived eye. Also, the excitatory and inhibitory conductances underlying the synaptic responses driven by the ipsilateral, left open eye were similarly potentiated compared to controls. Multiunit recordings in layer 4 followed by spike sorting indicated that the suprathreshold loss of responsiveness and the MD-driven ocular preference shifts were similar for narrow spiking, putative inhibitory neurons and broad spiking, putative excitatory neurons. Thus, by the time the plastic response has reached a plateau, inhibitory circuits adjust to preserve the normal balance between excitation and inhibition in the cortical network of the main thalamorecipient layer.

Mechanisms for stable, robust, and adaptive development of orientation maps in the primary visual cortex

Development of orientation maps in ferret and cat primary visual cortex (V1) has been shown to be stable, in that the earliest measurable maps are similar in form to the eventual adult map, robust, in that similar maps develop in both dark rearing and in a variety of normal visual environments, and yet adaptive, in that the final map pattern reflects the statistics of the specific visual environment. How can these three properties be reconciled? Using mechanistic models of the development of neural connectivity in V1, we show for the first time that realistic stable, robust, and adaptive map development can be achieved by including two low-level mechanisms originally motivated from single-neuron results. Specifically, contrast-gain control in the retinal ganglion cells and the lateral geniculate nucleus reduces variation in the presynaptic drive due to differences in input patterns, while homeostatic plasticity of V1 neuron excitability reduces the postsynaptic variability in firing rates. Together these two mechanisms, thought to be applicable across sensory systems in general, lead to biological maps that develop stably and robustly, yet adapt to the visual environment. The modeling results suggest that topographic map stability is a natural outcome of low-level processes of adaptation and normalization. The resulting model is more realistic, simpler, and far more robust, and is thus a good starting point for future studies of cortical map development.