Correlation between spatial frequency and orientation selectivity in V1 cortex: implications of a network model

Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity

Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network.

Synaptic plasticity is crucial for the development of efficient input representation in the different sensory cortices, such as the primary visual cortex. Efficient visual representation is determined by two factors: representational efficiency, i.e. how many different input features can be represented, and metabolic efficiency, i.e. how many spikes are required to represent a specific feature. Previous research has pointed out the importance of plasticity at excitatory synapses to achieve high representational efficiency and feedback inhibition as a gain control mechanism for controlling metabolic efficiency. However, it is only partially understood how the influence of inhibitory plasticity on excitatory plasticity can lead to an efficient representation. Using a spiking neural network, we show that plasticity at feed-forward and feedback inhibitory synapses is necessary for the emergence of well-distributed neuronal selectivity to improve representational efficiency. Further, the emergent balance between excitatory and inhibitory currents improves the metabolic efficiency, and leads to contrast-invariant tuning as an inherent network property. Extending previous work, our simulation results highlight the importance of plasticity at inhibitory synapses.

Effects of Experience on Spatial Frequency Tuning in the Visual System: Behavioral, Visuocortical, and Alpha-band Responses

Using electrophysiology and a classic fear conditioning paradigm, this work examined adaptive visuocortical changes in spatial frequency tuning in a sample of 50 undergraduate students. High-density EEG was recorded while participants viewed 400 total trials of individually presented Gabor patches of 10 different spatial frequencies. Patches were flickered to produce sweep steady-state visual evoked potentials (ssVEPs) at a temporal frequency of 13.33 Hz, with stimulus contrast ramping up from 0% to 41% Michelson over the course of each 2800-msec trial. During the final 200 trials, a selected range of Gabor stimuli (either the lowest or highest spatial frequencies, manipulated between participants) were paired with an aversive 90-dB white noise auditory stimulus. Changes in spatial frequency tuning from before to after conditioning for paired and unpaired gratings were evaluated at the behavioral and electrophysiological level. Specifically, ssVEP amplitude changes were evaluated for lateral inhibition and generalization trends, whereas change in alpha band (8-12 Hz) activity was tested for a generalization trend across spatial frequencies, using permutation-controlled F contrasts. Overall time courses of the sweep ssVEP amplitude envelope and alpha-band power were orthogonal, and ssVEPs proved insensitive to spatial frequency conditioning. Alpha reduction (blocking) was most pronounced when viewing fear-conditioned spatial frequencies, with blocking decreasing along the gradient of spatial frequencies preceding conditioned frequencies, indicating generalization across spatial frequencies. Results suggest that alpha power reduction-conceptually linked to engagement of attention and alertness/arousal mechanisms-to fear-conditioned stimuli operates independently of low-level spatial frequency processing (indexed by ssVEPs) in primary visual cortex.

A Neuronal Network Model of the Primate Visual System: Color Mechanisms in the Retina, LGN and V1

Color plays a key role in human vision but the neural machinery that underlies the transformation from stimulus to perception is not well understood. Here, we implemented a two-dimensional network model of the first stages in the primate parvocellular pathway (retina, lateral geniculate nucleus and layer 4C[Formula: see text] in V1) consisting of conductance-based point neurons. Model parameters were tuned based on physiological and anatomical data from the primate foveal and parafoveal vision, the most relevant visual field areas for color vision. We exhaustively benchmarked the model against well-established chromatic and achromatic visual stimuli, showing spatial and temporal responses of the model to disk- and ring-shaped light flashes, spatially uniform squares and sine-wave gratings of varying spatial frequency. The spatiotemporal patterns of parvocellular cells and cortical cells are consistent with their classification into chromatically single-opponent and double-opponent groups, and nonopponent cells selective for luminance stimuli. The model was implemented in the widely used neural simulation tool NEST and released as open source software. The aim of our modeling is to provide a biologically realistic framework within which a broad range of neuronal interactions can be examined at several different levels, with a focus on understanding how color information is processed.

The divisive normalization model of V1 neurons: a comprehensive comparison of physiological data and model predictions

The physiological responses of simple and complex cells in the primary visual cortex (V1) have been studied extensively and modeled at different levels. At the functional level, the divisive normalization model (DNM; Heeger DJ. Vis Neurosci 9: 181-197, 1992) has accounted for a wide range of single-cell recordings in terms of a combination of linear filtering, nonlinear rectification, and divisive normalization. We propose standardizing the formulation of the DNM and implementing it in software that takes static grayscale images as inputs and produces firing rate responses as outputs. We also review a comprehensive suite of 30 empirical phenomena and report a series of simulation experiments that qualitatively replicate dozens of key experiments with a standard parameter set consistent with physiological measurements. This systematic approach identifies novel falsifiable predictions of the DNM. We show how the model simultaneously satisfies the conflicting desiderata of flexibility and falsifiability. Our key idea is that, while adjustable parameters are needed to accommodate the diversity across neurons, they must be fixed for a given individual neuron. This requirement introduces falsifiable constraints when this single neuron is probed with multiple stimuli. We also present mathematical analyses and simulation experiments that explicate some of these constraints.

Orientation Selectivity from Very Sparse LGN Inputs in a Comprehensive Model of Macaque V1 Cortex

A new computational model of the primary visual cortex (V1) of the macaque monkey was constructed to reconcile the visual functions of V1 with anatomical data on its LGN input, the extreme sparseness of which presented serious challenges to theoretically sound explanations of cortical function. We demonstrate that, even with such sparse input, it is possible to produce robust orientation selectivity, as well as continuity in the orientation map. We went beyond that to find plausible dynamic regimes of our new model that emulate simultaneously experimental data for a wide range of V1 phenomena, beginning with orientation selectivity but also including diversity in neuronal responses, bimodal distributions of the modulation ratio (the simple/complex classification), and dynamic signatures, such as gamma-band oscillations. Intracortical interactions play a major role in all aspects of the visual functions of the model.

SIGNIFICANCE STATEMENT

We present the first realistic model that has captured the sparseness of magnocellular LGN inputs to the macaque primary visual cortex and successfully derived orientation selectivity from them. Three implications are (1) even in input layers to the visual cortex, the system is less feedforward and more dominated by intracortical signals than previously thought, (2) interactions among cortical neurons in local populations produce dynamics not explained by single neurons, and (3) such dynamics are important for function. Our model also shows that a comprehensive picture is necessary to explain function, because different visual properties are related. This study points to the need for paradigm shifts in neuroscience modeling: greater emphasis on population dynamics and, where possible, a move toward data-driven, comprehensive models.

Cortical Control of Spatial Resolution by VIP+ Interneurons

Neuronal tuning, defined by the degree of selectivity to a specific stimulus, is a hallmark of cortical computation. Understanding the role of GABAergic interneurons in shaping cortical tuning is now possible with the ability to manipulate interneuron classes selectively. Here, we show that interneurons expressing vasoactive intestinal polypeptide (VIP⁺) regulate the spatial frequency (SF) tuning of pyramidal neurons in mouse visual cortex. Using two-photon calcium imaging and optogenetic manipulations of VIP⁺ cell activity, we found that activating VIP⁺ cells elicited a stronger network response to stimuli of higher SFs, whereas suppressing VIP⁺ cells resulted in a network response shift toward lower SFs. These results establish that cortical inhibition modulates the spatial resolution of visual processing and add further evidence demonstrating that feature selectivity depends, not only on the feedforward excitatory projections into the cortex, but also on dynamic intracortical modulations by specific forms of inhibition.

SIGNIFICANCE STATEMENT

We demonstrate that interneurons expressing vasoactive intestinal polypeptide (VIP⁺) play a causal role in regulating the spatial frequency (SF) tuning of neurons in mouse visual cortex. We show that optogenetic activation of VIP⁺ cells results in a shift in network preference toward higher SFs, whereas suppressing them shifts the network toward lower SFs. Several studies have shown that VIP⁺ cells are sensitive to neuromodulation and increase their firing during locomotion, whisking, and pupil dilation and are involved in spatially specific top-down modulation, reminiscent of the effects of top-down attention, and also that attention enhances spatial resolution. Our findings provide a bridge between these studies by establishing the inhibitory circuitry that regulates these fundamental modulations of SF in the cortex.

Orientation Tuning Depends on Spatial Frequency in Mouse Visual Cortex

The response properties of neurons to sensory stimuli have been used to identify their receptive fields and to functionally map sensory systems. In primary visual cortex, most neurons are selective to a particular orientation and spatial frequency of the visual stimulus. Using two-photon calcium imaging of neuronal populations from the primary visual cortex of mice, we have characterized the response properties of neurons to various orientations and spatial frequencies. Surprisingly, we found that the orientation selectivity of neurons actually depends on the spatial frequency of the stimulus. This dependence can be easily explained if one assumed spatially asymmetric Gabor-type receptive fields. We propose that receptive fields of neurons in layer 2/3 of visual cortex are indeed spatially asymmetric, and that this asymmetry could be used effectively by the visual system to encode natural scenes.

Cue Reliability Represented in the Shape of Tuning Curves in the Owl's Sound Localization System

Optimal use of sensory information requires that the brain estimates the reliability of sensory cues, but the neural correlate of cue reliability relevant for behavior is not well defined. Here, we addressed this issue by examining how the reliability of spatial cue influences neuronal responses and behavior in the owl's auditory system. We show that the firing rate and spatial selectivity changed with cue reliability due to the mechanisms generating the tuning to the sound localization cue. We found that the correlated variability among neurons strongly depended on the shape of the tuning curves. Finally, we demonstrated that the change in the neurons' selectivity was necessary and sufficient for a network of stochastic neurons to predict behavior when sensory cues were corrupted with noise. This study demonstrates that the shape of tuning curves can stand alone as a coding dimension of environmental statistics.

SIGNIFICANCE STATEMENT

In natural environments, sensory cues are often corrupted by noise and are therefore unreliable. To make the best decisions, the brain must estimate the degree to which a cue can be trusted. The behaviorally relevant neural correlates of cue reliability are debated. In this study, we used the barn owl's sound localization system to address this question. We demonstrated that the mechanisms that account for spatial selectivity also explained how neural responses changed with degraded signals. This allowed for the neurons' selectivity to capture cue reliability, influencing the population readout commanding the owl's sound-orienting behavior.

Selective IT neurons are selective along many dimensions

Our visual abilities are unsurpassed because of a sophisticated code for objects located in the inferior temporal (IT) cortex. This code has remained a mystery because IT neurons show extremely diverse shape selectivity with no apparent organizing principle. Here, we show that there is an intrinsic component to selectivity in IT neurons. We tested IT neurons on distinct shapes and their parametric variations and asked whether neurons selective along one dimension were also selective along others. Selective neurons responded to fewer shapes and were narrowly tuned to local variations of these shapes, both along arbitrary morph lines and along variations in size, position, or orientation. For a subset of neurons, selective neurons were selective for both shape and texture. Finally, selective neurons were also more invariant in that they preserved their shape preferences across changes in size, position, and orientation. These observations indicate that there is an intrinsic constraint on the sharpness of tuning for the features coded by each IT neuron, making it always sharply tuned or always broadly tuned along all dimensions. We speculate that this may be an organizing principle throughout visual cortex.

Silent suppressive surrounds and optimal spatial frequencies of single neurons in cat V1

The receptive fields of the clear majority of neurons in the primary visual cortex (V1) of cats contain silent surround regions beyond the classical receptive field (CRF). When stimulated on their own, the silent surround regions do not generate action potentials (spikes); instead, they modulate (and usually partially suppress) spike responses to stimuli presented in the CRF. In the present study, we subdivided our sample of single V1 neurons recorded from anesthetized cats into two distinct categories: surround-suppressive (SS) cells and surround-non-suppressive (SN) cells. Consistent with previous reports, we found a negative correlation between the size of the CRF and the optimal spatial frequency (SF) of circular patches of achromatic gratings presented in the cells' receptive fields. Furthermore, we found a positive correlation between the strength of the surround suppression and the optimal spatial frequency of the achromatic gratings presented in the cells' receptive fields. The correlation between the strength of surround suppression and the optimal spatial frequency was stronger in neurons with suppressive regions located in the so-called 'end' zones. The functional implications of these relationships are discussed.

Sample skewness as a statistical measurement of neuronal tuning sharpness

We propose using the statistical measurement of the sample skewness of the distribution of mean firing rates of a tuning curve to quantify sharpness of tuning. For some features, like binocular disparity, tuning curves are best described by relatively complex and sometimes diverse functions, making it difficult to quantify sharpness with a single function and parameter. Skewness provides a robust nonparametric measure of tuning curve sharpness that is invariant with respect to the mean and variance of the tuning curve and is straightforward to apply to a wide range of tuning, including simple orientation tuning curves and complex object tuning curves that often cannot even be described parametrically. Because skewness does not depend on a specific model or function of tuning, it is especially appealing to cases of sharpening where recurrent interactions among neurons produce sharper tuning curves that deviate in a complex manner from the feedforward function of tuning. Since tuning curves for all neurons are not typically well described by a single parametric function, this model independence additionally allows skewness to be applied to all recorded neurons, maximizing the statistical power of a set of data. We also compare skewness with other nonparametric measures of tuning curve sharpness and selectivity. Compared to these other nonparametric measures tested, skewness is best used for capturing the sharpness of multimodal tuning curves defined by narrow peaks (maximum) and broad valleys (minima). Finally, we provide a more formal definition of sharpness using a shape-based information gain measure and derive and show that skewness is correlated with this definition.

Stochastic generation of gamma-band activity in primary visual cortex of awake and anesthetized monkeys

Oscillatory neural activity within the gamma band (25-90 Hz) is generally thought to be able to provide a timing signal for harmonizing neural computations across different brain regions. Using time-frequency analyses of the dynamics of gamma-band activity in the local field potentials recorded from monkey primary visual cortex, we found identical temporal characteristics of gamma activity in both awake and anesthetized brain states, including large variability of peak frequency, brief oscillatory epochs (<100 ms on average), and stochastic statistics of the incidence and duration of oscillatory events. These findings indicate that gamma-band activity is temporally unstructured and is inherently a stochastic signal generated by neural networks. This idea was corroborated further by our neural-network simulations. Our results suggest that gamma-band activity is too random to serve as a clock signal for synchronizing neuronal responses in awake as in anesthetized monkeys. Instead, gamma-band activity is more likely to be filtered neuronal network noise. Its mean frequency changes with global state and is reduced under anesthesia.

Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity

One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1's function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes.

Local circuit inhibition in the cerebral cortex as the source of gain control and untuned suppression

Theoretical considerations have led to the concept that the cerebral cortex is operating in a balanced state in which synaptic excitation is approximately balanced by synaptic inhibition from the local cortical circuit. This paper is about the functional consequences of the balanced state in sensory cortex. One consequence is gain control: there is experimental evidence and theoretical support for the idea that local circuit inhibition acts as a local automatic gain control throughout the cortex. Second, inhibition increases cortical feature selectivity: many studies of different sensory cortical areas have reported that suppressive mechanisms contribute to feature selectivity. Synaptic inhibition from the local microcircuit should be untuned (or broadly tuned) for stimulus features because of the microarchitecture of the cortical microcircuit. Untuned inhibition probably is the source of Untuned Suppression that enhances feature selectivity. We studied inhibition's function in our experiments, guided by a neuronal network model, on orientation selectivity in the primary visual cortex, V1, of the Macaque monkey. Our results revealed that Untuned Suppression, generated by local circuit inhibition, is crucial for the generation of highly orientation-selective cells in V1 cortex.