Inhibitory stabilization of the cortical network underlies visual surround suppression

Bottom-Up and Top-Down Input Augment the Variability of Cortical Neurons

Neurons in the cerebral cortex respond inconsistently to a repeated sensory stimulus, yet they underlie our stable sensory experiences. Although the nature of this variability is unknown, its ubiquity has encouraged the general view that each cell produces random spike patterns that noisily represent its response rate. In contrast, here we show that reversibly inactivating distant sources of either bottom-up or top-down input to cortical visual areas in the alert primate reduces both the spike train irregularity and the trial-to-trial variability of single neurons. A simple model in which a fraction of the pre-synaptic input is silenced can reproduce this reduction in variability, provided that there exist temporal correlations primarily within, but not between, excitatory and inhibitory input pools. A large component of the variability of cortical neurons may therefore arise from synchronous input produced by signals arriving from multiple sources.

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.

The impact of inhibitory mechanisms in the inner retina on spatial tuning of RGCs

Spatial tuning properties of retinal ganglion cells (RGCs) are sharpened by lateral inhibition originating at both the outer and inner plexiform layers. Lateral inhibition in the retina contributes to local contrast enhancement and sharpens edges. In this study, we used dynamic clamp recordings to examine the contribution of inner plexiform inhibition, originating from spiking amacrine cells, to the spatial tuning of RGCs. This was achieved by injecting currents generated from physiologically recorded excitatory and inhibitory stimulus-evoked conductances, into different types of primate and mouse RGCs. We determined the effects of injections of size-dependent conductances in which presynaptic inhibition and/or direct inhibition onto RGCs were partly removed by blocking the activity of spiking amacrine cells. We found that inhibition originating from spiking amacrine cells onto bipolar cell terminals and onto RGCs, work together to sharpen the spatial tuning of RGCs. Furthermore, direct inhibition is crucial for preventing spike generation at stimulus offset. These results reveal how inhibitory mechanisms in the inner plexiform layer contribute to determining size tuning and provide specificity to stimulus polarity.

A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure

Visual attention has many effects on neural responses, producing complex changes in firing rates, as well as modifying the structure and size of receptive fields, both in topological and feature space. Several existing models of attention suggest that these effects arise from selective modulation of neural inputs. However, anatomical and physiological observations suggest that attentional modulation targets higher levels of the visual system (such as V4 or MT) rather than input areas (such as V1). Here we propose a simple mechanism that explains how a top-down attentional modulation, falling on higher visual areas, can produce the observed effects of attention on neural responses. Our model requires only the existence of modulatory feedback connections between areas, and short-range lateral inhibition within each area. Feedback connections redistribute the top-down modulation to lower areas, which in turn alters the inputs of other higher-area cells, including those that did not receive the initial modulation. This produces firing rate modulations and receptive field shifts. Simultaneously, short-range lateral inhibition between neighboring cells produce competitive effects that are automatically scaled to receptive field size in any given area. Our model reproduces the observed attentional effects on response rates (response gain, input gain, biased competition automatically scaled to receptive field size) and receptive field structure (shifts and resizing of receptive fields both spatially and in complex feature space), without modifying model parameters. Our model also makes the novel prediction that attentional effects on response curves should shift from response gain to contrast gain as the spatial focus of attention drifts away from the studied cell.

Exerting visual attention results in profound changes in the activity of neurons in visual areas of the brain. Attention increases the firing of some neurons, decreases that of others, moves and resizes the receptive fields of individual neurons, and changes their preferred features according to what is being attended. How are these complex, subtle effects generated? While several models explain various subsets of these effects, a consistent explanation compatible with anatomical and physiological observations remains elusive. Here we show that the apparently complex and multifaceted effects of attention on neural responses can be explained as the automatic consequence of a top-down modulation, falling on higher visual areas (as suggested by anatomical observations), and interacting with short-range inhibition and feedback connections between areas. Our model only assumes the existence of well-known features of brain organization (reciprocal inter-area connections, mutual inhibition between neighboring neurons) to explain a wide range of attentional effects, including apparently finely-tuned effects (complex shifts in feature preferences, automatic scaling of competitive effects to receptive field size, resizing or shifting of receptive fields, etc). Our model also makes novel, testable predictions about the effect of certain attentional manipulations on neural responses.

An excitatory basis for divisive normalization in visual cortex

Neurons in visual cortex are connected not only locally, but also through networks of distal connectivity. These distal networks recruit both excitatory and inhibitory synapses and result in divisive normalization. Normalization is traditionally thought to result from increases in synaptic inhibition. By combining optogenetic stimulation and intracellular recordings in mouse visual cortex, we found that, on the contrary, normalization is a result of a decrease in synaptic excitation.

Canonical computations of cerebral cortex

The idea that there is a fundamental cortical circuit that performs canonical computations remains compelling though far from proven. Here we review evidence for two canonical operations within sensory cortical areas: a feedforward computation of selectivity; and a recurrent computation of gain in which, given sufficiently strong external input, perhaps from multiple sources, intracortical input largely, but not completely, cancels this external input. This operation leads to many characteristic cortical nonlinearities in integrating multiple stimuli. The cortical computation must combine such local processing with hierarchical processing across areas. We point to important changes in moving from sensory cortex to motor and frontal cortex and the possibility of substantial differences between cortex in rodents vs. species with columnar organization of selectivity.

Balanced feedforward inhibition and dominant recurrent inhibition in olfactory cortex

Throughout the brain, the recruitment of feedforward and recurrent inhibition shapes neural responses. However, disentangling the relative contributions of these often-overlapping cortical circuits is challenging. The piriform cortex provides an ideal system to address this issue because the interneurons responsible for feedforward and recurrent inhibition are anatomically segregated in layer (L) 1 and L2/3 respectively. Here we use a combination of optical and electrical activation of interneurons to profile the inhibitory input received by three classes of principal excitatory neuron in the anterior piriform cortex. In all classes, we find that L1 interneurons provide weaker inhibition than L2/3 interneurons. Nonetheless, feedforward inhibitory strength covaries with the amount of afferent excitation received by each class of principal neuron. In contrast, intracortical stimulation of L2/3 evokes strong inhibition that dominates recurrent excitation in all classes. Finally, we find that the relative contributions of feedforward and recurrent pathways differ between principal neuron classes. Specifically, L2 neurons receive more reliable afferent drive and less overall inhibition than L3 neurons. Alternatively, L3 neurons receive substantially more intracortical inhibition. These three features--balanced afferent drive, dominant recurrent inhibition, and differential recruitment by afferent vs. intracortical circuits, dependent on cell class--suggest mechanisms for olfactory processing that may extend to other sensory cortices.

The Primary Visual Cortex Is Differentially Modulated by Stimulus-Driven and Top-Down Attention

Selective attention can be focused either volitionally, by top-down signals derived from task demands, or automatically, by bottom-up signals from salient stimuli. Because the brain mechanisms that underlie these two attention processes are poorly understood, we recorded local field potentials (LFPs) from primary visual cortical areas of cats as they performed stimulus-driven and anticipatory discrimination tasks. Consistent with our previous observations, in both tasks, we found enhanced beta activity, which we have postulated may serve as an attention carrier. We characterized the functional organization of task-related beta activity by (i) cortical responses (EPs) evoked by electrical stimulation of the optic chiasm and (ii) intracortical LFP correlations. During the anticipatory task, peripheral stimulation that was preceded by high-amplitude beta oscillations evoked large-amplitude EPs compared with EPs that followed low-amplitude beta. In contrast, during the stimulus-driven task, cortical EPs preceded by high-amplitude beta oscillations were, on average, smaller than those preceded by low-amplitude beta. Analysis of the correlations between the different recording sites revealed that beta activation maps were heterogeneous during the bottom-up task and homogeneous for the top-down task. We conclude that bottom-up attention activates cortical visual areas in a mosaic-like pattern, whereas top-down attentional modulation results in spatially homogeneous excitation.

Inhibitory stabilization and visual coding in cortical circuits with multiple interneuron subtypes

Recent anatomical and functional characterization of cortical inhibitory interneurons has highlighted the diverse computations supported by different subtypes of interneurons. However, most theoretical models of cortex do not feature multiple classes of interneurons and rather assume a single homogeneous population. We study the dynamics of recurrent excitatory-inhibitory model cortical networks with parvalbumin (PV)-, somatostatin (SOM)-, and vasointestinal peptide-expressing (VIP) interneurons, with connectivity properties motivated by experimental recordings from mouse primary visual cortex. Our theory describes conditions under which the activity of such networks is stable and how perturbations of distinct neuronal subtypes recruit changes in activity through recurrent synaptic projections. We apply these conclusions to study the roles of each interneuron subtype in disinhibition, surround suppression, and subtractive or divisive modulation of orientation tuning curves. Our calculations and simulations determine the architectural and stimulus tuning conditions under which cortical activity consistent with experiment is possible. They also lead to novel predictions concerning connectivity and network dynamics that can be tested via optogenetic manipulations. Our work demonstrates that recurrent inhibitory dynamics must be taken into account to fully understand many properties of cortical dynamics observed in experiments.

Inhibitory Actions Unified by Network Integration

Cortical function is regulated by a strikingly diverse array of local-circuit inhibitory neurons. We evaluated how optogenetically activating somatostatin- and parvalbumin-positive interneurons subtractively or divisively suppressed auditory cortical cells' responses to tones. In both awake and anesthetized animals, we found that activating either family of interneurons produced mixtures of divisive and subtractive effects and that simultaneously recorded neurons were often suppressed in qualitatively different ways. A simple network model shows that threshold nonlinearities can interact with network activity to transform subtractive inhibition of neurons into divisive inhibition of networks, or vice versa. Varying threshold and the strength of suppression of a model neuron could determine whether the effect of inhibition appeared divisive, subtractive, or both. We conclude that the characteristics of response inhibition specific to a single interneuron type can be "masked" by the network configuration and cellular properties of the network in which they are embedded.

Reduced contextual effects on visual contrast perception in schizophrenia and bipolar affective disorder

BACKGROUND

The salience of a visual stimulus is often reduced by nearby stimuli, an effect known as surround suppression of perceived contrast, which may help in locating the borders of an object. Weaker surround suppression has been observed in schizophrenia but it is unclear whether this abnormality is present in other mental disorders with similar symptomatology, or is evident in people with genetic liability for schizophrenia.

METHOD

By examining surround suppression among subjects with schizophrenia or bipolar affective disorder, their unaffected biological relatives and healthy controls we sought to determine whether diminished surround suppression was specific to schizophrenia, and if subjects with a genetic risk for either disorder would show similar deficits. Measuring perceived contrast in different surround conditions also allowed us to investigate how this suppression depends on the similarity of target and surrounding stimuli.

RESULTS

Surround suppression was weaker among schizophrenia patients regardless of surround configuration. Subjects with bipolar affective disorder showed an intermediate deficit, with stronger suppression than in schizophrenia but weaker than control subjects. Surround suppression was normal in relatives of both patient groups. Findings support a deficit in broadly tuned (rather than sharply orientation- or direction-selective) suppression mechanisms.

CONCLUSIONS

Weak broadly tuned suppression during visual perception is evident in schizophrenia and bipolar affective disorder, consistent with impaired gain control related to the clinical expression of these conditions.

Periodic Forcing of Inhibition-Stabilized Networks: Nonlinear Resonances and Phase-Amplitude Coupling

Inhibition-stabilized networks (ISNs) are neural architectures with strong positive feedback among pyramidal neurons balanced by strong negative feedback from inhibitory interneurons, a circuit element found in the hippocampus and the primary visual cortex. In their working regime, ISNs produce damped oscillations in the [Formula: see text]-range in response to inputs to the inhibitory population. In order to understand the properties of interconnected ISNs, we investigated periodic forcing of ISNs. We show that ISNs can be excited over a range of frequencies and derive properties of the resonance peaks. In particular, we studied the phase-locked solutions, the torus solutions, and the resonance peaks. Periodically forced ISNs respond with (possibly multistable) phase-locked activity, whereas networks with sustained intrinsic oscillations respond more dynamically to periodic inputs with tori. Hence, the dynamics are surprisingly rich, and phase effects alone do not adequately describe the network response. This strengthens the importance of phase-amplitude coupling as opposed to phase-phase coupling in providing multiple frequencies for multiplexing and routing information.

Flexible gating of contextual influences in natural vision

Identical sensory inputs can be perceived as strikingly different when embedded in distinct contexts. Neural responses to simple stimuli are also modulated by context, but the contribution of this modulation to the processing of natural sensory input is unclear. We measured surround suppression, a quintessential contextual influence, in macaque primary visual cortex with natural images. We found suppression strength varied substantially for different images. This variability was not well explained by existing descriptions of surround suppression, but it was predicted by Bayesian inference about statistical dependencies in images. In this framework, surround suppression was flexible: it was recruited when the image was inferred to contain redundancies, and substantially reduced in strength otherwise. Our results thus reveal a surprising gating of a basic, widespread cortical computation, by inference about the statistics of natural input.

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.

Suppressive mechanisms in visual motion processing: From perception to intelligence

Perception operates on an immense amount of incoming information that greatly exceeds the brain's processing capacity. Because of this fundamental limitation, the ability to suppress irrelevant information is a key determinant of perceptual efficiency. Here, I will review a series of studies investigating suppressive mechanisms in visual motion processing, namely perceptual suppression of large, background-like motions. These spatial suppression mechanisms are adaptive, operating only when sensory inputs are sufficiently robust to guarantee visibility. Converging correlational and causal evidence links these behavioral results with inhibitory center-surround mechanisms, namely those in cortical area MT. Spatial suppression is abnormally weak in several special populations, including the elderly and individuals with schizophrenia-a deficit that is evidenced by better-than-normal direction discriminations of large moving stimuli. Theoretical work shows that this abnormal weakening of spatial suppression should result in motion segregation deficits, but direct behavioral support of this hypothesis is lacking. Finally, I will argue that the ability to suppress information is a fundamental neural process that applies not only to perception but also to cognition in general. Supporting this argument, I will discuss recent research that shows individual differences in spatial suppression of motion signals strongly predict individual variations in IQ scores.

Homeostatic role of heterosynaptic plasticity: models and experiments

Homosynaptic Hebbian-type plasticity provides a cellular mechanism of learning and refinement of connectivity during development in a variety of biological systems. In this review we argue that a complimentary form of plasticity-heterosynaptic plasticity-represents a necessary cellular component for homeostatic regulation of synaptic weights and neuronal activity. The required properties of a homeostatic mechanism which acutely constrains the runaway dynamics imposed by Hebbian associative plasticity have been well-articulated by theoretical and modeling studies. Such mechanism(s) should robustly support the stability of operation of neuronal networks and synaptic competition, include changes at non-active synapses, and operate on a similar time scale to Hebbian-type plasticity. The experimentally observed properties of heterosynaptic plasticity have introduced it as a strong candidate to fulfill this homeostatic role. Subsequent modeling studies which incorporate heterosynaptic plasticity into model neurons with Hebbian synapses (utilizing an STDP learning rule) have confirmed its ability to robustly provide stability and competition. In contrast, properties of homeostatic synaptic scaling, which is triggered by extreme and long lasting (hours and days) changes of neuronal activity, do not fit two crucial requirements for a hypothetical homeostatic mechanism needed to provide stability of operation in the face of on-going synaptic changes driven by Hebbian-type learning rules. Both the trigger and the time scale of homeostatic synaptic scaling are fundamentally different from those of the Hebbian-type plasticity. We conclude that heterosynaptic plasticity, which is triggered by the same episodes of strong postsynaptic activity and operates on the same time scale as Hebbian-type associative plasticity, is ideally suited to serve a homeostatic role during on-going synaptic plasticity.

Input-gain control produces feature-specific surround suppression

In primary visual cortex (V1), neuronal responses are sensitive to context. For example, responses to stimuli presented within the receptive field (RF) center are often suppressed by stimuli within the RF surround, and this suppression tends to be strongest when the center and surround stimuli match. We sought to identify the mechanism that gives rise to these properties of surround modulation. To do so, we exploited the stability of implanted multielectrode arrays to record from neurons in V1 of alert monkeys with multiple stimulus sets that more exhaustively probed center-surround interactions. We first replicated previous results concerning center-surround similarity using gratings representing all combinations of center and surround orientation. With this stimulus set, the surround simply scaled population responses to the center, such that the overall population tuning curve had the same shape and peak response. However, when the center contained two superimposed gratings (i.e., a visual "plaid"), one component of which always matched the surround orientation, suppression selectively affected the portion of the response driven by the matching center component, thereby producing shifts in the peak of the population orientation tuning curve. In effect, the surround caused neurons to respond predominantly to the component grating of the center plaid that was unmatched to the surround grating, as if by reducing the effective strength of whichever stimulus attributes were matched to the surround. These results provide key physiological support for theoretical models that propose feature-specific, input-gain control as the mechanism underlying surround suppression.

Adult cortical plasticity studied with chronically implanted electrode arrays

The functional architecture of adult cerebral cortex retains a capacity for experience-dependent change. This is seen after focal binocular lesions as rapid changes in receptive field (RF) of the lesion projection zone (LPZ) in the primary visual cortex (V1). To study the dynamics of the circuitry underlying these changes longitudinally, we implanted microelectrode arrays in macaque (Macaca mulatta) V1, eliminating the possibility of sampling bias, which was a concern in previous studies. With this method, we observed a rapid initial recovery in the LPZ and, during the following weeks, 63-89% of the sites in the LPZ showed recovery of visual responses with significant position tuning. The RFs shifted ∼3° away from the scotoma. In the absence of a lesion, visual stimulation surrounding an artificial scotoma did not elicit visual responses, suggesting that the postlesion RF shifts resulted from cortical reorganization. Interestingly, although both spikes and LFPs gave consistent prelesion position tuning, only spikes reflected the postlesion remapping.

The stabilized supralinear network: a unifying circuit motif underlying multi-input integration in sensory cortex

Neurons in sensory cortex integrate multiple influences to parse objects and support perception. Across multiple cortical areas, integration is characterized by two neuronal response properties: (1) surround suppression--modulatory contextual stimuli suppress responses to driving stimuli; and (2) "normalization"--responses to multiple driving stimuli add sublinearly. These depend on input strength: for weak driving stimuli, contextual influences facilitate or more weakly suppress and summation becomes linear or supralinear. Understanding the circuit operations underlying integration is critical to understanding cortical function and disease. We present a simple, general theory. A wealth of integrative properties, including the above, emerge robustly from four cortical circuit properties: (1) supralinear neuronal input/output functions; (2) sufficiently strong recurrent excitation; (3) feedback inhibition; and (4) simple spatial properties of intracortical connections. Integrative properties emerge dynamically as circuit properties, with excitatory and inhibitory neurons showing similar behaviors. In new recordings in visual cortex, we confirm key model predictions.

Ocular dominance plasticity disrupts binocular inhibition-excitation matching in visual cortex

BACKGROUND

To ensure that neuronal networks function in a stable fashion, neurons receive balanced inhibitory and excitatory inputs. In various brain regions, this balance has been found to change temporarily during plasticity. Whether changes in inhibition have an instructive or permissive role in plasticity remains unclear. Several studies have addressed this question using ocular dominance plasticity in the visual cortex as a model, but so far, it remains controversial whether changes in inhibition drive this form of plasticity by directly affecting eye-specific responses or through increasing the plasticity potential of excitatory connections.

RESULTS

We tested how three major classes of interneurons affect eye-specific responses in normally reared or monocularly deprived mice by optogenetically suppressing their activity. We find that in contrast to somatostatin-expressing or vasoactive intestinal polypeptide-expressing interneurons, parvalbumin (PV)-expressing interneurons strongly inhibit visual responses. In individual neurons of normal mice, inhibition and excitation driven by either eye are balanced, and suppressing PV interneurons does not alter ocular preference. Monocular deprivation disrupts the binocular balance of inhibition and excitation in individual neurons, causing suppression of PV interneurons to change their ocular preference. Importantly, however, these changes do not consistently favor responses to one of the eyes at the population level.

CONCLUSIONS

Monocular deprivation disrupts the binocular balance of inhibition and excitation of individual cells. This disbalance does not affect the overall expression of ocular dominance. Our data therefore support a permissive rather than an instructive role of inhibition in ocular dominance plasticity.

The role of nicotinic acetylcholine receptors in autosomal dominant nocturnal frontal lobe epilepsy

Autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE) is a focal epilepsy with attacks typically arising in the frontal lobe during non-rapid eye movement (NREM) sleep. It is characterized by clusters of complex and stereotyped hypermotor seizures, frequently accompanied by sudden arousals. Cognitive and psychiatric symptoms may be also observed. Approximately 12% of the ADNFLE families carry mutations on genes coding for subunits of the heteromeric neuronal nicotinic receptors (nAChRs). This is consistent with the widespread expression of these receptors, particularly the α4β2^* subtype, in the neocortex and thalamus. However, understanding how mutant nAChRs lead to partial frontal epilepsy is far from being straightforward because of the complexity of the cholinergic regulation in both developing and mature brains. The relation with the sleep-waking cycle must be also explained. We discuss some possible pathogenetic mechanisms in the light of recent advances about the nAChR role in prefrontal regions as well as the studies carried out in murine models of ADNFLE. Functional evidence points to alterations in prefrontal GABA release, and the synaptic unbalance probably arises during the cortical circuit maturation. Although most of the available functional evidence concerns mutations on nAChR subunit genes, other genes have been recently implicated in the disease, such as KCNT1 (coding for a Na⁺-dependent K⁺ channel), DEPD5 (Disheveled, Egl-10 and Pleckstrin Domain-containing protein 5), and CRH (Corticotropin-Releasing Hormone). Overall, the uncertainties about both the etiology and the pathogenesis of ADNFLE point to the current gaps in our knowledge the regulation of neuronal networks in the cerebral cortex.

Surround suppression and temporal processing of visual signals

Extraclassical surround suppression strongly modulates responses of neurons in the retina, lateral geniculate nucleus (LGN), and primary visual cortex. Although a great deal is known about the spatial properties of extraclassical suppression and the role it serves in stimulus size tuning, relatively little is known about how extraclassical suppression shapes visual processing in the temporal domain. We recorded the spiking activity of retinal ganglion cells and LGN neurons in the cat to test the hypothesis that extraclassical suppression influences temporal features of visual responses in the early visual system. Our results demonstrate that extraclassical suppression not only shifts the distribution of interspike intervals in a manner that decreases the efficacy of neuronal communication, it also decreases the reliability of neuronal responses to visual stimuli and it decreases the duration of visual responses, an effect that underlies a rightward shift in the temporal frequency tuning of LGN neurons. Taken together, these results reveal a dynamic relationship between extraclassical suppression and the temporal features of neuronal responses.

Recent advances in the neuropsychopharmacology of serotonergic hallucinogens

Serotonergic hallucinogens, such as (+)-lysergic acid diethylamide, psilocybin, and mescaline, are somewhat enigmatic substances. Although these drugs are derived from multiple chemical families, they all produce remarkably similar effects in animals and humans, and they show cross-tolerance. This article reviews the evidence demonstrating the serotonin 5-HT2A receptor is the primary site of hallucinogen action. The 5-HT2A receptor is responsible for mediating the effects of hallucinogens in human subjects, as well as in animal behavioral paradigms such as drug discrimination, head twitch response, prepulse inhibition of startle, exploratory behavior, and interval timing. Many recent clinical trials have yielded important new findings regarding the psychopharmacology of these substances. Furthermore, the use of modern imaging and electrophysiological techniques is beginning to help unravel how hallucinogens work in the brain. Evidence is also emerging that hallucinogens may possess therapeutic efficacy.

Adenosine effects on inhibitory synaptic transmission and excitation-inhibition balance in the rat neocortex

KEY POINTS

Adenosine might be the most widespread neuromodulator in the brain, but its effects on inhibitory transmission in the neocortex are not understood. Here we report that adenosine suppresses inhibitory transmission to layer 2/3 pyramidal neurons via activation of presynaptic A1 receptors. We present evidence for functional A2A receptors, which have a weak modulatory effect on the A1-mediated suppression, at about 50% of inhibitory synapses at pyramidal neurons. Adenosine suppresses excitatory and inhibitory transmission to a different extent, and can change the excitation-inhibition balance at a set of synapses bidirectionally, but on average the balance was maintained during application of adenosine. These results suggest that changes of adenosine concentration may lead to differential modulation of excitatory-inhibitory balance in pyramidal neurons, and thus redistribution of local spotlights of activity in neocortical circuits, while preserving the balanced state of the whole network.

ABSTRACT

Adenosine might be the most widespread neuromodulator in the brain: as a metabolite of ATP it is present in every neuron and glial cell. However, how adenosine affects operation of neurons and networks in the neocortex is poorly understood, mostly because modulation of inhibitory transmission by adenosine has been so little studied. To clarify adenosine's role at inhibitory synapses, and in excitation-inhibition balance in pyramidal neurons, we recorded pharmacologically isolated inhibitory responses, compound excitatory-inhibitory responses and spontaneous events in layer 2/3 pyramidal neurons in slices from rat visual cortex. We show that adenosine (1-150 μm) suppresses inhibitory transmission to these neurons in a concentration-dependent and reversible manner. The suppression was mediated by presynaptic A1 receptors (A1Rs) because it was blocked by a selective A1 antagonist, DPCPX, and associated with changes of release indices: paired-pulse ratio, inverse coefficient of variation and frequency of miniature events. At some synapses (12 out of 24) we found evidence for A2ARs: their blockade led to a small but significant increase of the magnitude of adenosine-mediated suppression. This effect of A2AR blockade was not observed when A1Rs were blocked, suggesting that A2ARs do not have their own effect on transmission, but can modulate the A1R-mediated suppression. At both excitatory and inhibitory synapses, the magnitude of A1R-mediated suppression and A2AR-A1R interaction expressed high variability, suggesting high heterogeneity of synapses in the sensitivity to adenosine. Adenosine could change the balance between excitation and inhibition at a set of inputs to a neuron bidirectionally, towards excitation or towards inhibition. On average, however, these bidirectional changes cancelled each other, and the overall balance of excitation and inhibition was maintained during application of adenosine. These results suggest that changes of adenosine concentration may lead to differential modulation of excitatory-inhibitory balance in pyramidal neurons, and thus redistribution of local spotlights of activity in neocortical circuits, while preserving the balanced state of the whole network.

Dynamics of Gamma Bursts in Local Field Potentials

In this letter, we provide a stochastic analysis of, and supporting simulation data for, a stochastic model of the generation of gamma bursts in local field potential (LFP) recordings by interacting populations of excitatory and inhibitory neurons. Our interest is in behavior near a fixed point of the stochastic dynamics of the model. We apply a recent limit theorem of stochastic dynamics to probe into details of this local behavior, obtaining several new results. We show that the stochastic model can be written in terms of a rotation multiplied by a two-dimensional standard Ornstein-Uhlenbeck (OU) process. Viewing the rewritten process in terms of phase and amplitude processes, we are able to proceed further in analysis. We demonstrate that gamma bursts arise in the model as excursions of the modulus of the OU process. The associated pair of stochastic phase and amplitude processes satisfies their own pair of stochastic differential equations, which indicates that large phase slips occur between gamma bursts. This behavior is mirrored in LFP data simulated from the original model. These results suggest that the rewritten model is a valid representation of the behavior near the fixed point for a wide class of models of oscillatory neural processes.

Image familiarization sharpens response dynamics of neurons in inferotemporal cortex

Repeated viewing of an image over days and weeks induces a marked reduction in the strength with which neurons in monkey inferotemporal cortex respond to it. The processing advantage that attaches to this reduction is unknown. One possibility is that truncation of the response to a familiar image leaves neurons in a state of readiness to respond to ensuing images and thereby enhances their ability to track rapidly changing displays. We explored this possibility by assessing neuronal responses to familiar and novel images in rapid serial visual displays. Inferotemporal neurons responded more strongly to familiar than to novel images in such displays. The effect was stronger among putative inhibitory neurons than among putative excitatory neurons. A comparable effect occurred at the level of the scalp potential in humans. We conclude that long-term familiarization sharpens the response dynamics of neurons in both monkey and human extrastriate visual cortex.

Experience-dependent specialization of receptive field surround for selective coding of natural scenes

At eye opening, neurons in primary visual cortex (V1) are selective for stimulus features, but circuits continue to refine in an experience-dependent manner for some weeks thereafter. How these changes contribute to the coding of visual features embedded in complex natural scenes remains unknown. Here we show that normal visual experience after eye opening is required for V1 neurons to develop a sensitivity for the statistical structure of natural stimuli extending beyond the boundaries of their receptive fields (RFs), which leads to improvements in coding efficiency for full-field natural scenes (increased selectivity and information rate). These improvements are mediated by an experience-dependent increase in the effectiveness of natural surround stimuli to hyperpolarize the membrane potential specifically during RF-stimulus epochs triggering action potentials. We suggest that neural circuits underlying surround modulation are shaped by the statistical structure of visual input, which leads to more selective coding of features in natural scenes.

•

V1 firing is more selective to natural than phase-scrambled surround stimuli

•

Improved selectivity is caused by Vm hyperpolarisation prior to RF-spiking events

•

Natural surround sensitivity is experience dependent, absent in immature/deprived V1

•

Vision shapes circuits to improve V1 coding of natural scenes across RF and surround

Computing with a canonical neural circuits model with pool normalization and modulating feedback

Evidence suggests that the brain uses an operational set of canonical computations like normalization, input filtering, and response gain enhancement via reentrant feedback. Here, we propose a three-stage columnar architecture of cascaded model neurons to describe a core circuit combining signal pathways of feedforward and feedback processing and the inhibitory pooling of neurons to normalize the activity. We present an analytical investigation of such a circuit by first reducing its detail through the lumping of initial feedforward response filtering and reentrant modulating signal amplification. The resulting excitatory-inhibitory pair of neurons is analyzed in a 2D phase-space. The inhibitory pool activation is treated as a separate mechanism exhibiting different effects. We analyze subtractive as well as divisive (shunting) interaction to implement center-surround mechanisms that include normalization effects in the characteristics of real neurons. Different variants of a core model architecture are derived and analyzed--in particular, individual excitatory neurons (without pool inhibition), the interaction with an inhibitory subtractive or divisive (i.e., shunting) pool, and the dynamics of recurrent self-excitation combined with divisive inhibition. The stability and existence properties of these model instances are characterized, which serve as guidelines to adjust these properties through proper model parameterization. The significance of the derived results is demonstrated by theoretical predictions of response behaviors in the case of multiple interacting hypercolumns in a single and in multiple feature dimensions. In numerical simulations, we confirm these predictions and provide some explanations for different neural computational properties. Among those, we consider orientation contrast-dependent response behavior, different forms of attentional modulation, contrast element grouping, and the dynamic adaptation of the silent surround in extraclassical receptive field configurations, using only slight variations of the same core reference model.

Orientation-tuned surround suppression in mouse visual cortex

The firing rates of neurons in primary visual cortex (V1) are suppressed by large stimuli, an effect known as surround suppression. In cats and monkeys, the strength of suppression is sensitive to orientation; responses to regions containing uniform orientations are more suppressed than those containing orientation contrast. This effect is thought to be important for scene segmentation, but the underlying neural mechanisms are poorly understood. We asked whether it is possible to study these mechanisms in the visual cortex of mice, because of recent advances in technology for studying the cortical circuitry in mice. It is unknown whether neurons in mouse V1 are sensitive to orientation contrast. We measured the orientation selectivity of surround suppression in the different layers of mouse V1. We found strong surround suppression in layer 4 and the superficial layers, part of which was orientation tuned: iso-oriented surrounds caused more suppression than cross-oriented surrounds. Surround suppression was delayed relative to the visual response and orientation-tuned suppression was delayed further, suggesting two separate suppressive mechanisms. Previous studies proposed that surround suppression depends on the activity of inhibitory somatostatin-positive interneurons in the superficial layers. To test the involvement of the superficial layers we topically applied lidocaine. Silencing of the superficial layers did not prevent orientation-tuned suppression in layer 4. These results show that neurons in mouse V1, which lacks orientation columns, show orientation-dependent surround suppression in layer 4 and the superficial layers and that surround suppression in layer 4 does not require contributions from neurons in the superficial layers.

Multiple components of surround modulation in primary visual cortex: multiple neural circuits with multiple functions?

The responses of neurons in primary visual cortex (V1) to stimulation of their receptive field (RF) are modulated by stimuli in the RF surround. This modulation is suppressive when the stimuli in the RF and surround are of similar orientation, but less suppressive or facilitatory when they are cross-oriented. Similarly, in human vision surround stimuli selectively suppress the perceived contrast of a central stimulus. Although the properties of surround modulation have been thoroughly characterized in many species, cortical areas and sensory modalities, its role in perception remains unknown. Here we argue that surround modulation in V1 consists of multiple components having different spatio-temporal and tuning properties, generated by different neural circuits and serving different visual functions. One component arises from LGN afferents, is fast, untuned for orientation, and spatially restricted to the surround region nearest to the RF (the near-surround); its function is to normalize V1 cell responses to local contrast. Intra-V1 horizontal connections contribute a slower, narrowly orientation-tuned component to near-surround modulation, whose function is to increase the coding efficiency of natural images in manner that leads to the extraction of object boundaries. The third component is generated by topdown feedback connections to V1, is fast, broadly orientation-tuned, and extends into the far-surround; its function is to enhance the salience of behaviorally relevant visual features. Far- and near-surround modulation, thus, act as parallel mechanisms: the former quickly detects and guides saccades/attention to salient visual scene locations, the latter segments object boundaries in the scene.

Sensory optimization by stochastic tuning

Individually, visual neurons are each selective for several aspects of stimulation, such as stimulus location, frequency content, and speed. Collectively, the neurons implement the visual system's preferential sensitivity to some stimuli over others, manifested in behavioral sensitivity functions. We ask how the individual neurons are coordinated to optimize visual sensitivity. We model synaptic plasticity in a generic neural circuit and find that stochastic changes in strengths of synaptic connections entail fluctuations in parameters of neural receptive fields. The fluctuations correlate with uncertainty of sensory measurement in individual neurons: The higher the uncertainty the larger the amplitude of fluctuation. We show that this simple relationship is sufficient for the stochastic fluctuations to steer sensitivities of neurons toward a characteristic distribution, from which follows a sensitivity function observed in human psychophysics and which is predicted by a theory of optimal allocation of receptive fields. The optimal allocation arises in our simulations without supervision or feedback about system performance and independently of coupling between neurons, making the system highly adaptive and sensitive to prevailing stimulation.

Correlations in V1 are reduced by stimulation outside the receptive field

The trial-to-trial response variability of nearby cortical neurons is correlated. These correlations may strongly influence population coding performance. Numerous studies have shown that correlations can be dynamically modified by attention, adaptation, learning, and potent stimulus drive. However, the mechanisms that influence correlation strength remain poorly understood. Here we test whether correlations are influenced by presenting stimuli outside the classical receptive field (RF) of visual neurons, where they recruit a normalization signal termed surround suppression. We recorded simultaneously the activity of dozens of cells using microelectrode arrays implanted in the superficial layers of V1 in anesthetized, paralyzed macaque monkeys. We presented annular stimuli that encircled--but did not impinge upon--the RFs of the recorded cells. We found that these "extra-classical" stimuli reduced correlations in the absence of stimulation of the RF, closely resembling the decorrelating effects of stimulating the RFs directly. Our results suggest that normalization signals may be an important mechanism for modulating correlations.

Optimal control of transient dynamics in balanced networks supports generation of complex movements

Populations of neurons in motor cortex engage in complex transient dynamics of large amplitude during the execution of limb movements. Traditional network models with stochastically assigned synapses cannot reproduce this behavior. Here we introduce a class of cortical architectures with strong and random excitatory recurrence that is stabilized by intricate, fine-tuned inhibition, optimized from a control theory perspective. Such networks transiently amplify specific activity states and can be used to reliably execute multidimensional movement patterns. Similar to the experimental observations, these transients must be preceded by a steady-state initialization phase from which the network relaxes back into the background state by way of complex internal dynamics. In our networks, excitation and inhibition are as tightly balanced as recently reported in experiments across several brain areas, suggesting inhibitory control of complex excitatory recurrence as a generic organizational principle in cortex.

Spatiotemporal characteristics of surround suppression in primary visual cortex and lateral geniculate nucleus of the cat

In the primary visual cortex (V1), a neuronal response to stimulation of the classical receptive field (CRF) is predominantly suppressed by a stimulus presented outside the CRF (extraclassical receptive field, ECRF), a phenomenon referred to as ECRF suppression. To elucidate the neuronal mechanisms and origin of ECRF suppression in V1 of anesthetized cats, we examined the temporal properties of the spatial extent and orientation specificity of ECRF suppression in V1 and the lateral geniculate nucleus (LGN), using stationary-flashed sinusoidal grating. In V1, we found three components of ECRF suppression: 1) local and fast, 2) global and fast, and 3) global and late. The local and fast component, which resulted from within 2° of the boundary of the CRF, started no more than 10 ms after the onset of the CRF response and exhibited low specificity for the orientation of the ECRF stimulus. These spatiotemporal properties corresponded to those of geniculate ECRF suppression, suggesting that the local and fast component of V1 is inherited from the LGN. In contrast, the two global components showed rather large spatial extents ∼5° from the CRF boundary and high specificity for orientation, suggesting that their possible origin is the cortex, not the LGN. Correspondingly, the local component was observed in all neurons of the thalamocortical recipient layer, while the global component was biased toward other layers. Therefore, we conclude that both subcortical and cortical mechanisms with different spatiotemporal properties are involved in ECRF suppression.

Corticocortical feedback increases the spatial extent of normalization

Normalization has been proposed as a canonical computation operating across different brain regions, sensory modalities, and species. It provides a good phenomenological description of non-linear response properties in primary visual cortex (V1), including the contrast response function and surround suppression. Despite its widespread application throughout the visual system, the underlying neural mechanisms remain largely unknown. We recently observed that corticocortical feedback contributes to surround suppression in V1, raising the possibility that feedback acts through normalization. To test this idea, we characterized area summation and contrast response properties in V1 with and without feedback from V2 and V3 in alert macaques and applied a standard normalization model to the data. Area summation properties were well explained by a form of divisive normalization, which computes the ratio between a neuron's driving input and the spatially integrated activity of a "normalization pool." Feedback inactivation reduced surround suppression by shrinking the spatial extent of the normalization pool. This effect was independent of the gain modulation thought to mediate the influence of contrast on area summation, which remained intact during feedback inactivation. Contrast sensitivity within the receptive field center was also unaffected by feedback inactivation, providing further evidence that feedback participates in normalization independent of the circuit mechanisms involved in modulating contrast gain and saturation. These results suggest that corticocortical feedback contributes to surround suppression by increasing the visuotopic extent of normalization and, via this mechanism, feedback can play a critical role in contextual information processing.

Regulating Cortical Oscillations in an Inhibition-Stabilized Network

Understanding the anatomical and functional architecture of the brain is essential for designing neurally inspired intelligent systems. Theoretical and empirical studies suggest a role for narrowband oscillations in shaping the functional architecture of the brain through their role in coding and communication of information. Such oscillations are ubiquitous signals in the electrical activity recorded from the brain. In the cortex, oscillations detected in the gamma range (30-80 Hz) are modulated by behavioral states and sensory features in complex ways. How is this regulation achieved? Although several underlying principles for the genesis of these oscillations have been proposed, a unifying account for their regulation has remained elusive. In a network of excitatory and inhibitory neurons operating in an inhibition-stabilized regime, we show that strongly superlinear responses of inhibitory neurons facilitate bidirectional regulation of oscillation frequency and power. In such a network, the balance of drives to the excitatory and inhibitory populations determines how the power and frequency of oscillations are modulated. The model accounts for the puzzling increase in their frequency with the salience of visual stimuli, and a decrease with their size. Oscillations in our model grow stronger as the mean firing level is reduced, accounting for the size dependence of visually evoked gamma rhythms, and suggesting a role for oscillations in improving the signal-to-noise ratio (SNR) of signals in the brain. Empirically testing such predictions is still challenging, and implementing the proposed coding and communication strategies in neuromorphic systems could assist in our understanding of the biological system.

Cortical oscillations arise from contextual interactions that regulate sparse coding

Precise spike times carry information and are important for synaptic plasticity. Synchronizing oscillations such as gamma bursts could coordinate spike times, thus regulating information transmission in the cortex. Oscillations are driven by inhibitory neurons and are modulated by sensory stimuli and behavioral states. How their power and frequency are regulated is an open question. Using a model cortical circuit, we propose a regulatory mechanism that depends on the activity balance of monosynaptic and disynaptic pathways to inhibitory neurons: Monosynaptic input causes more powerful oscillations whereas disynaptic input increases the frequency of oscillations. The balance of stimulation to the two pathways modulates the overall distribution of spikes, with stronger disynaptic stimulation (e.g., preferred stimuli inside visual receptive fields) producing high firing rates and weak oscillations; in contrast, stronger monosynaptic stimulation (e.g., suppressive contextual stimulation from outside visual receptive fields) generates low firing rates and strong oscillatory regulation of spike timing, as observed in alert cortex processing complex natural stimuli. By accounting for otherwise paradoxical experimental findings, our results demonstrate how the frequency and power of oscillations, and hence spike times, can be modulated by both sensory input and behavioral context, with powerful oscillations signifying a cortical state under inhibitory control in which spikes are sparse and spike timing is precise.

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.

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.

Dynamical models of cortical circuits

Cortical neurons operate within recurrent neuronal circuits. Dissecting their operation is key to understanding information processing in the cortex and requires transparent and adequate dynamical models of circuit function. Convergent evidence from experimental and theoretical studies indicates that strong feedback inhibition shapes the operating regime of cortical circuits. For circuits operating in inhibition-dominated regimes, mathematical and computational studies over the past several years achieved substantial advances in understanding response modulation and heterogeneity, emergent stimulus selectivity, inter-neuron correlations, and microstate dynamics. The latter indicate a surprisingly strong dependence of the collective circuit dynamics on the features of single neuron action potential generation. New approaches are needed to definitely characterize the cortical operating regime.

Selectivity and sparseness in randomly connected balanced networks

Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the “paradoxical” effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

Spike synchronization in cat primary visual cortex depends on similarity of surround-suppression magnitude

In the primary visual cortex (V1), the spike synchronization seen in neuron pairs with non-overlapping receptive fields can be explained by similarities in their preferred orientation (PO). However, this is not true for pairs with overlapping receptive fields, as they can still exhibit spike synchronization even if their POs are only weakly correlated. Here, we investigated the relationship between spike synchronization and suppressive modulation derived from classical receptive-field surround (surround suppression). We found that layer 4 and layer 2/3 pairs exhibited mainly asymmetric spike synchronization that had non-zero time-lags and was dependent on both the similarity of the PO and the strength of surround suppression. In contrast, layer 2/3 and layer 2/3 pairs showed mainly symmetric spike synchronization that had zero time-lag and was dependent on the similarity of the strength of surround suppression but not on the similarity in POs. From these results, we propose that in cat V1 there exists a functional network that mainly depends on the similarity in surround suppression, and that in layer 2/3 neurons the network maintains surround suppression that is primarily inherited from layer 4 neurons.

Similar adaptation effects in primary visual cortex and area MT of the macaque monkey under matched stimulus conditions

Recent stimulus history, or adaptation, can alter neuronal response properties. Adaptation effects have been characterized in a number of visually responsive structures, from the retina to higher visual cortex. However, it remains unclear whether adaptation effects across stages of the visual system take a similar form in response to a particular sensory event. This is because studies typically probe a single structure or cortical area, using a stimulus ensemble chosen to provide potent drive to the cells of interest. Here we adopt an alternative approach and compare adaptation effects in primary visual cortex (V1) and area MT using identical stimulus ensembles. Previous work has suggested these areas adjust to recent stimulus drive in distinct ways. We show that this is not the case: adaptation effects in V1 and MT can involve weak or strong loss of responsivity and shifts in neuronal preference toward or away from the adapter, depending on stimulus size and adaptation duration. For a particular stimulus size and adaptation duration, however, effects are similar in nature and magnitude in V1 and MT. We also show that adaptation effects in MT of awake animals depend strongly on stimulus size. Our results suggest that the strategies for adjusting to recent stimulus history depend more strongly on adaptation duration and stimulus size than on the cortical area. Moreover, they indicate that different levels of the visual system adapt similarly to recent sensory experience.

A complex-valued firing-rate model that approximates the dynamics of spiking networks

Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons.

Windows to the soul: vision science as a tool for studying biological mechanisms of information processing deficits in schizophrenia

Cognitive and information processing deficits are core features and important sources of disability in schizophrenia. Our understanding of the neural substrates of these deficits remains incomplete, in large part because the complexity of impairments in schizophrenia makes the identification of specific deficits very challenging. Vision science presents unique opportunities in this regard: many years of basic research have led to detailed characterization of relationships between structure and function in the early visual system and have produced sophisticated methods to quantify visual perception and characterize its neural substrates. We present a selective review of research that illustrates the opportunities for discovery provided by visual studies in schizophrenia. We highlight work that has been particularly effective in applying vision science methods to identify specific neural abnormalities underlying information processing deficits in schizophrenia. In addition, we describe studies that have utilized psychophysical experimental designs that mitigate generalized deficit confounds, thereby revealing specific visual impairments in schizophrenia. These studies contribute to accumulating evidence that early visual cortex is a useful experimental system for the study of local cortical circuit abnormalities in schizophrenia. The high degree of similarity across neocortical areas of neuronal subtypes and their patterns of connectivity suggests that insights obtained from the study of early visual cortex may be applicable to other brain regions. We conclude with a discussion of future studies that combine vision science and neuroimaging methods. These studies have the potential to address pressing questions in schizophrenia, including the dissociation of local circuit deficits vs. impairments in feedback modulation by cognitive processes such as spatial attention and working memory, and the relative contributions of glutamatergic and GABAergic deficits.

Network model of top-down influences on local gain and contextual interactions in visual cortex

The visual system uses continuity as a cue for grouping oriented line segments that define object boundaries in complex visual scenes. Many studies support the idea that long-range intrinsic horizontal connections in early visual cortex contribute to this grouping. Top-down influences in primary visual cortex (V1) play an important role in the processes of contour integration and perceptual saliency, with contour-related responses being task dependent. This suggests an interaction between recurrent inputs to V1 and intrinsic connections within V1 that enables V1 neurons to respond differently under different conditions. We created a network model that simulates parametrically the control of local gain by hypothetical top-down modification of local recurrence. These local gain changes, as a consequence of network dynamics in our model, enable modulation of contextual interactions in a task-dependent manner. Our model displays contour-related facilitation of neuronal responses and differential foreground vs. background responses over the neuronal ensemble, accounting for the perceptual pop-out of salient contours. It quantitatively reproduces the results of single-unit recording experiments in V1, highlighting salient contours and replicating the time course of contextual influences. We show by means of phase-plane analysis that the model operates stably even in the presence of large inputs. Our model shows how a simple form of top-down modulation of the effective connectivity of intrinsic cortical connections among biophysically realistic neurons can account for some of the response changes seen in perceptual learning and task switching.

Effects of stimulus spatial frequency, size, and luminance contrast on orientation tuning of neurons in the dorsal lateral geniculate nucleus of cat

It is generally thought that orientation selectivity first appears in the primary visual cortex (V1), whereas neurons in the lateral geniculate nucleus (LGN), an input source for V1, are thought to be insensitive to stimulus orientation. Here we show that increasing both the spatial frequency and size of the grating stimuli beyond their respective optimal values strongly enhance the orientation tuning of LGN neurons. The resulting orientation tuning was clearly contrast-invariant. Furthermore, blocking intrathalamic inhibition by iontophoretically administering γ-aminobutyric acid (GABA)A receptor antagonists, such as bicuculline and GABAzine, slightly but significantly weakened the contrast invariance. Our results suggest that orientation tuning in the LGN is caused by an elliptical classical receptive field and orientation-tuned surround suppression, and that its contrast invariance is ensured by local GABAA inhibition. This contrast-invariant orientation tuning in LGN neurons may contribute to the contrast-invariant orientation tuning seen in V1 neurons.

Inferring trial-to-trial excitatory and inhibitory synaptic inputs from membrane potential using Gaussian mixture Kalman filtering

Time-varying excitatory and inhibitory synaptic inputs govern activity of neurons and process information in the brain. The importance of trial-to-trial fluctuations of synaptic inputs has recently been investigated in neuroscience. Such fluctuations are ignored in the most conventional techniques because they are removed when trials are averaged during linear regression techniques. Here, we propose a novel recursive algorithm based on Gaussian mixture Kalman filtering (GMKF) for estimating time-varying excitatory and inhibitory synaptic inputs from single trials of noisy membrane potential in current clamp recordings. The KF is followed by an expectation maximization (EM) algorithm to infer the statistical parameters (time-varying mean and variance) of the synaptic inputs in a non-parametric manner. As our proposed algorithm is repeated recursively, the inferred parameters of the mixtures are used to initiate the next iteration. Unlike other recent algorithms, our algorithm does not assume an a priori distribution from which the synaptic inputs are generated. Instead, the algorithm recursively estimates such a distribution by fitting a Gaussian mixture model (GMM). The performance of the proposed algorithms is compared to a previously proposed PF-based algorithm (Paninski et al., 2012) with several illustrative examples, assuming that the distribution of synaptic input is unknown. If noise is small, the performance of our algorithms is similar to that of the previous one. However, if noise is large, they can significantly outperform the previous proposal. These promising results suggest that our algorithm is a robust and efficient technique for estimating time varying excitatory and inhibitory synaptic conductances from single trials of membrane potential recordings.

Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations

When recording the membrane potential, V, of a neuron it is desirable to be able to extract the synaptic input. Critically, the synaptic input is stochastic and nonreproducible so one is therefore often restricted to single-trial data. Here, we introduce means of estimating the inhibition and excitation and their confidence limits from single sweep trials. The estimates are based on the mean membrane potential, V̄, and the membrane time constant, τ. The time constant provides the total conductance ( G = capacitance/τ) and is extracted from the autocorrelation of V. The synaptic conductances can then be inferred from V̄ when approximating the neuron as a single compartment. We further employ a stochastic model to establish limits of confidence. The method is verified on models and experimental data, where the synaptic input is manipulated pharmacologically or estimated by an alternative method. The method gives best results if the synaptic input is large compared with other conductances, the intrinsic conductances have little or no time dependence or are comparably small, the ligand-gated kinetics is faster than the membrane time constant, and the majority of synaptic contacts are electrotonically close to soma (recording site). Although our data are in current clamp, the method also works in V-clamp recordings, with some minor adaptations. All custom made procedures are provided in Matlab.

Energy-efficient encoding by shifting spikes in neocortical neurons

The speed of computations in neocortical networks critically depends on the ability of populations of spiking neurons to rapidly detect subtle changes in the input and translate them into firing rate changes. However, high sensitivity to perturbations may lead to explosion of noise and increased energy consumption. Can neuronal networks reconcile the requirements for high sensitivity, operation in a low-noise regime, and constrained energy consumption? Using intracellular recordings in slices from the rat visual cortex, we show that layer 2/3 pyramidal neurons are highly sensitive to minor input perturbations. They can change their population firing rate in response to small artificial excitatory postsynaptic currents (aEPSCs) immersed in fluctuating noise very quickly, within 2-2.5 ms. These quick responses were mediated by the generation of new, additional action potentials (APs), but also by shifting spikes into the response peak. In that latter case, the spike count increase during the peak and the decrease after the peak cancelled each other, thus producing quick responses without increases in total spike count and associated energy costs. The contribution of spikes from one or the other source depended on the aEPSCs timing relative to the waves of depolarization produced by ongoing activity. Neurons responded by shifting spikes to aEPSCs arriving at the beginning of a depolarization wave, but generated additional spikes in response to aEPSCs arriving towards the end of a wave. We conclude that neuronal networks can combine high sensitivity to perturbations and operation in a low-noise regime. Moreover, certain patterns of ongoing activity favor this combination and energy-efficient computations.