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

Cholinergic Modulation Promotes Attentional Modulation in Primary Visual Cortex- A Modeling Study

Attention greatly influences sensory neural processing by enhancing firing rates of neurons that represent the attended stimuli and by modulating their tuning properties. The cholinergic system is believed to partly mediate the attention contingent improvement of cortical processing by influencing neuronal excitability, synaptic transmission and neural network characteristics. Here, we used a biophysically based model to investigate the mechanisms by which cholinergic system influences sensory information processing in the primary visual cortex (V1) layer 4C. The physiological properties and architectures of our model were inspired by experimental data and include feed-forward input from dorsal lateral geniculate nucleus that sets up orientation preference in V1 neural responses. When including a cholinergic drive, we found significant sharpening in orientation selectivity, desynchronization of LFP gamma power and spike-field coherence, decreased response variability and correlation reduction mostly by influencing intracortical interactions and by increasing inhibitory drive. Our results indicated that these effects emerged due to changes specific to the behavior of the inhibitory neurons. The behavior of our model closely resembles the effects of attention on neural activities in monkey V1. Our model suggests precise mechanisms through which cholinergic modulation may mediate the effects of attention in the visual cortex.

Functional Logic of Layer 2/3 Inhibitory Connectivity in the Ferret Visual Cortex

Understanding how cortical inhibition shapes circuit function requires identifying the connectivity rules relating the response properties of inhibitory interneurons and their postsynaptic targets. Here we explore the orientation tuning of layer 2/3 inhibitory inputs in the ferret visual cortex using a combination of in vivo axon imaging, functional input mapping, and physiology. Inhibitory boutons exhibit robust orientation-tuned responses with preferences that can differ significantly from the cortical column in which they reside. Inhibitory input fields measured with patterned optogenetic stimulation and intracellular recordings revealed that these inputs originate from a wide range of orientation domains, inconsistent with a model of co-tuned inhibition and excitation. Intracellular synaptic conductance measurements confirm that individual neurons can depart from a co-tuned regime. Our results argue against a simple rule for the arrangement of inhibitory inputs supplied by layer 2/3 circuits and suggest that heterogeneity in presynaptic inhibitory networks contributes to neural response properties.

A model for the origin and development of visual orientation selectivity

Orientation selectivity is a key property of primary visual cortex that contributes, downstream, to object recognition. The origin of orientation selectivity, however, has been debated for decades. It is known that on- and off-centre subcortical pathways converge onto single neurons in primary visual cortex, and that the spatial offset between these pathways gives rise to orientation selectivity. On- and off-centre pathways are intermingled, however, so it is unclear how their inputs to cortex come to be spatially segregated. We here describe a model in which the segregation occurs through Hebbian strengthening and weakening of geniculocortical synapses during the development of the visual system. Our findings include the following. 1. Neighbouring on- and off-inputs to cortex largely cancelled each other at the start of development. At each receptive field location, the Hebbian process increased the strength of one input sign at the expense of the other sign, producing a spatial segregation of on- and off-inputs. 2. The resulting orientation selectivity was precise in that the bandwidths of the orientation tuning functions fell within empirical estimates. 3. The model produced maps of preferred orientation–complete with iso-orientation domains and pinwheels–similar to those found in real cortex. 4. These maps did not originate in cortical processes, but from clustering of off-centre subcortical pathways and the relative location of neighbouring on-centre clusters. We conclude that a model with intermingled on- and off-pathways shaped by Hebbian synaptic plasticity can explain both the origin and development of orientation selectivity.

Many neurons in mammalian primary visual cortex are highly selective for the orientation of visual contours and can therefore contribute to object recognition. Orientation selectivity depends on on- and off-centre retinal neurons that respond, respectively, to light and dark. We describe a signal-processing model that includes both subcortical pathways and cortical neurons. The model predicts the preferred orientation of a cortical neuron from the empirically determined spatial layout of retinal cells. Further, the subcortical-to-cortical connections change in strength during visual development, meaning that cortical neurons in the model have orientation selectivity just as precise as real neurons. Our model can therefore explain the origin of orientation selectivity and the way it develops during visual system maturation.

Balanced synaptic currents underlie low-frequency oscillations in the subiculum

Numerous synaptic and intrinsic membrane mechanisms have been proposed for generating oscillatory activity in the hippocampus. Few studies, however, have directly measured synaptic conductances and membrane properties during oscillations. The time course and relative contribution of excitatory and inhibitory synaptic conductances, as well as the role of intrinsic membrane properties in amplifying synaptic inputs, remains unclear. To address this issue, we used an isolated whole hippocampal preparation that generates autonomous low-frequency oscillations near the theta range. Using 2-photon microscopy and expression of genetically encoded fluorophores, we obtained on-cell and whole-cell patch recordings of pyramidal cells and fast-firing interneurons in the distal subiculum. Pyramidal cell and interneuron spiking shared similar phase-locking to local field potential oscillations. In pyramidal cells, spiking resulted from a concomitant and balanced increase in excitatory and inhibitory synaptic currents. In contrast, interneuron spiking was driven almost exclusively by excitatory synaptic current. Thus, similar to tightly balanced networks underlying hippocampal gamma oscillations and ripples, balanced synaptic inputs in the whole hippocampal preparation drive highly phase-locked spiking at the peak of slower network oscillations.

Dendritic computations captured by an effective point neuron model

Modeling single-neuron dynamics is the first step to quantitatively understand brain computation. Yet, the existing point neuron models fail to capture dendritic effects, which are crucial for neuronal information processing. We derive an effective point neuron model, which incorporates an additional synaptic integration current arising from the nonlinear interaction between synaptic currents across spatial dendrites. Our model captures the somatic voltage response of a neuron with complex dendrites and is capable of performing rich dendritic computations. Besides its computational efficiency in simulations, our model suggests reexamination of previous studies involving the decomposition of excitatory and inhibitory synaptic inputs based on the existing point neuron framework, e.g., the inhibition is often underestimated in experiment.

Complex dendrites in general present formidable challenges to understanding neuronal information processing. To circumvent the difficulty, a prevalent viewpoint simplifies the neuronal morphology as a point representing the soma, and the excitatory and inhibitory synaptic currents originated from the dendrites are treated as linearly summed at the soma. Despite its extensive applications, the validity of the synaptic current description remains unclear, and the existing point neuron framework fails to characterize the spatiotemporal aspects of dendritic integration supporting specific computations. Using electrophysiological experiments, realistic neuronal simulations, and theoretical analyses, we demonstrate that the traditional assumption of linear summation of synaptic currents is oversimplified and underestimates the inhibition effect. We then derive a form of synaptic integration current within the point neuron framework to capture dendritic effects. In the derived form, the interaction between each pair of synaptic inputs on the dendrites can be reliably parameterized by a single coefficient, suggesting the inherent low-dimensional structure of dendritic integration. We further generalize the form of synaptic integration current to capture the spatiotemporal interactions among multiple synaptic inputs and show that a point neuron model with the synaptic integration current incorporated possesses the computational ability of a spatial neuron with dendrites, including direction selectivity, coincidence detection, logical operation, and a bilinear dendritic integration rule discovered in experiment. Our work amends the modeling of synaptic inputs and improves the computational power of a modeling neuron within the point neuron framework.

Differential Inhibitory Configurations Segregate Frequency Selectivity in the Mouse Inferior Colliculus

Receptive fields and tuning curves of sensory neurons represent the neural substrates that allow animals to efficiently detect and distinguish external stimuli. They are progressively refined to create diverse sensitivity and selectivity for neurons along ascending central pathways. However, the neural circuitry mechanisms have not been directly determined for such fundamental qualities in relation to sensory neurons' functional organizations, because of the technical difficulty of correlating neurons' input and output. Here, we obtained spike outputs and synaptic inputs from the same neurons within characteristically defined neural ensembles, to determine the synaptic mechanisms driving their diverse frequency selectivity in the mouse inferior colliculus. We find that the synaptic strength and timing of excitatory and inhibitory inputs are configured differently and independently within individual neurons' receptive fields, which segregate sensitive and selective neurons and endow neural populations with broad receptive fields and sharp frequency tuning. By computationally modeling spike outputs from integrating synaptic inputs and comparing them with real spike responses of the same neurons, we show that space-clamping errors did not qualitatively affect the estimation of spike responses derived from synaptic currents in in vivo voltage-clamp recordings. These data suggest that heterogeneous inhibitory circuits coexist locally for a parallel but differentiated representation of incoming signals.SIGNIFICANCE STATEMENT Sensitivity and selectivity are functional qualities of sensory systems to facilitate animals' survival. There is little direct evidence for the synaptic basis of neurons' functional variance within neural ensembles. Here we adopted a novel framework to fill such a long-standing gap by uniting population activities with single cells' spike outputs and their synaptic inputs. Furthermore, the effects of space-clamping errors on subcortical synaptic currents were evaluated in vivo, by comparing recorded spike responses and simulated spike outputs from computationally integrating synaptic inputs. Our study illustrated that the synaptic strength and timing of inhibition relative to excitation can be configured differently for neurons within a defined neural ensemble, to segregate their selectivity. It provides new insights into coexisting heterogeneous local circuits.

What Is Excitation/Inhibition and How Is It Regulated? A Case of the Elephant and the Wisemen

The balance between excitation and inhibition in neuronal circuits has drawn more and more attention in recent years, due to its proposed multifaceted functions in the normal neural circuit as well as its potential roles in the etiology of many neurological disorders. Here, we discuss the importance of clearly defining excitation/inhibition by experimental measurements and the implications of some recent studies to our understanding of the regulation of excitation/inhibition at the neuronal level.

Divisive Inhibition Prevails During Simultaneous Optogenetic Activation of All Interneuron Subtypes in Mouse Primary Visual Cortex

The mouse primary visual cortex (V1) has become an important brain area for exploring how neural circuits process information. Optogenetic tools have helped to outline the connectivity of a local V1 circuit comprising excitatory pyramidal neurons and several genetically-defined inhibitory interneuron subtypes that express parvalbumin, somatostatin, or vasoactive intestinal peptide. Optogenetic modulation of individual interneuron subtypes can alter the visual responsiveness of pyramidal neurons with distinct forms of inhibition and disinhibition. However, different interneuron subtypes have potentially opposing actions, and the potency of their effects relative to each other remains unclear. Therefore, in this study we simultaneously optogenetically activated all interneuron subtypes during visual processing to explore whether any single inhibitory effect would predominate. This aggregate interneuron activation consistently inhibited pyramidal neurons in a divisive manner, which was essentially identical to the pattern of inhibition produced by activating parvalbumin-expressing interneurons alone.

Precise excitation-inhibition balance controls gain and timing in the hippocampus

Excitation-inhibition (EI) balance controls excitability, dynamic range, and input gating in many brain circuits. Subsets of synaptic input can be selected or 'gated' by precise modulation of finely tuned EI balance, but assessing the granularity of EI balance requires combinatorial analysis of excitatory and inhibitory inputs. Using patterned optogenetic stimulation of mouse hippocampal CA3 neurons, we show that hundreds of unique CA3 input combinations recruit excitation and inhibition with a nearly identical ratio, demonstrating precise EI balance at the hippocampus. Crucially, the delay between excitation and inhibition decreases as excitatory input increases from a few synapses to tens of synapses. This creates a dynamic millisecond-range window for postsynaptic excitation, controlling membrane depolarization amplitude and timing via subthreshold divisive normalization. We suggest that this combination of precise EI balance and dynamic EI delays forms a general mechanism for millisecond-range input gating and subthreshold gain control in feedforward networks.

Information transmission of mean and variance coding in integrate-and-fire neurons

Neurons process information by translating continuous signals into patterns of discrete spike times. An open question is how much information these spike times contain about signals which modulate either the mean or the variance of the somatic currents in neurons, as is observed experimentally. Here we calculate the exact information contained in discrete spike times about a continuous signal in both encoding strategies. We show that the information content about mean modulating signals is generally substantially larger than about variance modulating signals for biological parameters. Our analysis further reveals that higher information transmission is associated with a larger proportion of nonlinear signal encoding. Our study measures the complete information content of mean and variance coding and provides a method to determine what fraction of the total information is linearly decodable.

Single-neuron perturbations reveal feature-specific competition in V1

The computations performed by local neural populations, such as a cortical layer, are typically inferred from anatomical connectivity and observations of neural activity. Here we describe a method-influence mapping-that uses single-neuron perturbations to directly measure how cortical neurons reshape sensory representations. In layer 2/3 of the primary visual cortex (V1), we use two-photon optogenetics to trigger action potentials in a targeted neuron and calcium imaging to measure the effect on spiking in neighbouring neurons in awake mice viewing visual stimuli. Excitatory neurons on average suppressed other neurons and had a centre-surround influence profile over anatomical space. A neuron's influence on its neighbour depended on their similarity in activity. Notably, neurons suppressed activity in similarly tuned neurons more than in dissimilarly tuned neurons. In addition, photostimulation reduced the population response, specifically to the targeted neuron's preferred stimulus, by around 2%. Therefore, V1 layer 2/3 performed feature competition, in which a like-suppresses-like motif reduces redundancy in population activity and may assist with inference of the features that underlie sensory input. We anticipate that influence mapping can be extended to investigate computations in other neural populations.

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.

Discrete attractor dynamics underlies persistent activity in the frontal cortex

Short-term memories link events separated in time, such as past sensation and future actions. Short-term memories are correlated with slow neural dynamics, including selective persistent activity, which can be maintained over seconds. In a delayed response task that requires short-term memory, neurons in the mouse anterior lateral motor cortex (ALM) show persistent activity that instructs future actions. To determine the principles that underlie this persistent activity, here we combined intracellular and extracellular electrophysiology with optogenetic perturbations and network modelling. We show that during the delay epoch, the activity of ALM neurons moved towards discrete end points that correspond to specific movement directions. These end points were robust to transient shifts in ALM activity caused by optogenetic perturbations. Perturbations occasionally switched the population dynamics to the other end point, followed by incorrect actions. Our results show that discrete attractor dynamics underlie short-term memory related to motor planning.

Chronic myocardial infarction changed the excitatory-inhibitory synaptic balance in the medial prefrontal cortex of rat

The medial prefrontal cortex is a key area for the regulation of pain and emotion. However, the functional involvement of the medial prefrontal cortex for visceral nociception, at the neuronal or synaptic level, is obscure yet. In the present study, the properties of excitatory and inhibitory synaptic transmission within the layer II/III of rat medial prefrontal cortex after chronic myocardial infarction were studied. It is found that the excitation–inhibition ratio of the medial prefrontal cortex was greatly changed, with enhanced excitation and decreased inhibition inputs to the pyramidal cells of the medial prefrontal cortex, which largely due to decreased spike firing in gamma-aminobutyric acid-ergic neurons. Behaviorally, inhibition of gamma-aminobutyric acid-ergic synaptic transmission alleviated the visceral pain and anxiety. It is thus for the first time showing that the excitation–inhibition ratio is increased in the medial prefrontal cortex after chronic myocardial infarction, which may come from the reduced intrinsic activity of gamma-aminobutyric acid-ergic neurons and is important for regulating the angina pectoris and anxiety induced by chronic myocardial infarction.

Precise Synaptic Balance in the Zebrafish Homolog of Olfactory Cortex

Neuronal computations critically depend on the connectivity rules that govern the convergence of excitatory and inhibitory synaptic signals onto individual neurons. To examine the functional synaptic organization of a distributed memory network, we performed voltage clamp recordings in telencephalic area Dp of adult zebrafish, the homolog of olfactory cortex. In neurons of posterior Dp, odor stimulation evoked large, recurrent excitatory and inhibitory inputs that established a transient state of high conductance and synaptic balance. Excitation and inhibition in individual neurons were co-tuned to different odors and correlated on slow and fast timescales. This precise synaptic balance implies specific connectivity among Dp neurons, despite the absence of an obvious topography. Precise synaptic balance stabilizes activity patterns in different directions of coding space and in time while preserving high bandwidth. The coordinated connectivity of excitatory and inhibitory subnetworks in Dp therefore supports fast recurrent memory operations.

Spatio-temporal characteristics of population responses evoked by microstimulation in the barrel cortex

Intra-cortical microstimulation (ICMS) is a widely used technique to artificially stimulate cortical tissue. This method revealed functional maps and provided causal links between neuronal activity and cognitive, sensory or motor functions. The effects of ICMS on neural activity depend on stimulation parameters. Past studies investigated the effects of stimulation frequency mainly at the behavioral or motor level. Therefore the direct effect of frequency stimulation on the evoked spatio-temporal patterns of cortical activity is largely unknown. To study this question we used voltage-sensitive dye imaging to measure the population response in the barrel cortex of anesthetized rats evoked by high frequency stimulation (HFS), a lower frequency stimulation (LFS) of the same duration or a single pulse stimulation. We found that single pulse and short trains of ICMS induced cortical activity extending over few mm. HFS evoked a lower population response during the sustained response and showed a smaller activation across time and space compared with LFS. Finally the evoked population response started near the electrode site and spread horizontally at a propagation velocity in accordance with horizontal connections. In summary, HFS was less effective in cortical activation compared to LFS although HFS had 5 fold more energy than LFS.

In vivo measurement of afferent activity with axon-specific calcium imaging

In vivo calcium imaging from axons provides direct interrogation of afferent neural activity, informing neural representations that a local circuit receives. Unlike in somata and dendrites, axonal recording of neural activity--both electrically and optically--has been difficult to achieve, thus preventing comprehensive understanding of neuronal circuit function. Here, we developed an active transportation strategy to enrich GCaMP6, a genetically encoded calcium indicator (GECI), uniformly in axons with sufficient brightness, signal-to-noise ratio, and photostability to allow robust, structure-specific imaging of pre-synaptic activity in awake mice. Axon-targeted GCaMP6 (axon-GCaMP6) enables frame-to-frame correlation for motion correction in axons and permits subcellular-resolution recording of axonal activity in previously inaccessible deep brain areas. We used axon-GCaMP6 to record layer-specific local afferents without contamination from somata and intermingled dendrites in the cortex. We expect axon-GCaMP6 will facilitate new applications in investigating afferent signals relayed by genetically defined neuronal populations within and across specific brain regions.

Irrelevance by inhibition: Learning, computation, and implications for schizophrenia

Symptoms of schizophrenia may arise from a failure of cortical circuits to filter-out irrelevant inputs. Schizophrenia has also been linked to disruptions in cortical inhibitory interneurons, consistent with the possibility that in the normally functioning brain, these cells are in some part responsible for determining which sensory inputs are relevant versus irrelevant. Here, we develop a neural network model that demonstrates how the cortex may learn to ignore irrelevant inputs through plasticity processes affecting inhibition. The model is based on the proposal that the amount of excitatory output from a cortical circuit encodes the expected magnitude of reward or punishment ("relevance"), which can be trained using a temporal difference learning mechanism acting on feedforward inputs to inhibitory interneurons. In the model, irrelevant and blocked stimuli drive lower levels of excitatory activity compared with novel and relevant stimuli, and this difference in activity levels is lost following disruptions to inhibitory units. When excitatory units are connected to a competitive-learning output layer with a threshold, the relevance code can be shown to "gate" both learning and behavioral responses to irrelevant stimuli. Accordingly, the combined network is capable of recapitulating published experimental data linking inhibition in frontal cortex with fear learning and expression. Finally, the model demonstrates how relevance learning can take place in parallel with other types of learning, through plasticity rules involving inhibitory and excitatory components, respectively. Altogether, this work offers a theory of how the cortex learns to selectively inhibit inputs, providing insight into how relevance-assignment problems may emerge in schizophrenia.

Differential tuning of excitation and inhibition shapes direction selectivity in ferret visual cortex

To encode specific sensory inputs, cortical neurons must generate selective responses for distinct stimulus features. In principle, a variety of factors contribute to a cortical neuron’s response selectivity: the tuning and strength of excitatory^1–3 and inhibitory synaptic inputs^4–6, dendritic nonlinearities^7–9, and spike threshold^10,11. Here we employ a combination of techniques including in vivo whole-cell recording, synaptic and cellular resolution in vivo two photon calcium imaging, and GABAergic-selective optogenetic manipulation to dissect the factors contributing to direction selective responses of layer 2/3 neurons in ferret visual cortex (V1). Two-photon calcium imaging of dendritic spines^12,13 revealed that each neuron receives a mixture of excitatory synaptic inputs selective for the somatic preferred or null direction of motion. The relative number of preferred- and null-tuned excitatory inputs predicted a neuron’s somatic direction preference, but failed to account for the degree of direction selectivity. In contrast, in vivo whole-cell patch clamp recordings revealed a striking degree of direction selectivity in subthreshold responses that was significantly correlated with spiking direction selectivity. Subthreshold direction selectivity was predicted by the magnitude and variance of the response to the null direction of motion, and several lines of evidence including conductance measurements demonstrate that differential tuning of excitation and inhibition suppresses responses to the null direction of motion. Consistent with this idea, optogenetic inactivation of GABAergic neurons in layer 2/3 reduced direction selectivity by enhancing responses to the null direction. Furthermore, using a new technique to optogenetically map connections of inhibitory neurons in layer 2/3 in vivo, we find that layer 2/3 inhibitory neurons make long-range, intercolumnar projections to excitatory neurons that prefer the opposite direction of motion. We conclude that intracortical inhibition exerts a major influence on the degree of direction selectivity in layer 2/3 of ferret V1 by suppressing responses to the null direction of motion.

On the Physiological Modulation and Potential Mechanisms Underlying Parieto-Occipital Alpha Oscillations

The parieto-occipital alpha (8–13 Hz) rhythm is by far the strongest spectral fingerprint in the human brain. Almost 90 years later, its physiological origin is still far from clear. In this Research Topic I review human pharmacological studies using electroencephalography (EEG) and magnetoencephalography (MEG) that investigated the physiological mechanisms behind posterior alpha. Based on results from classical and recent experimental studies, I find a wide spectrum of drugs that modulate parieto-occipital alpha power. Alpha frequency is rarely affected, but this might be due to the range of drug dosages employed. Animal and human pharmacological findings suggest that both GABA enhancers and NMDA blockers systematically decrease posterior alpha power. Surprisingly, most of the theoretical frameworks do not seem to embrace these empirical findings and the debate on the functional role of alpha oscillations has been polarized between the inhibition vs. active poles hypotheses. Here, I speculate that the functional role of alpha might depend on physiological excitation as much as on physiological inhibition. This is supported by animal and human pharmacological work showing that GABAergic, glutamatergic, cholinergic, and serotonergic receptors in the thalamus and the cortex play a key role in the regulation of alpha power and frequency. This myriad of physiological modulations fit with the view that the alpha rhythm is a complex rhythm with multiple sources supported by both thalamo-cortical and cortico-cortical loops. Finally, I briefly discuss how future research combining experimental measurements derived from theoretical predictions based of biophysically realistic computational models will be crucial to the reconciliation of these disparate findings.

Synaptic E-I Balance Underlies Efficient Neural Coding

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding.

Mechanisms of Orientation Selectivity in the Primary Visual Cortex

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

Translaminar circuits formed by the pyramidal cells in the superficial layers of cat visual cortex

Pyramidal cells in the superficial layers of the neocortex provide a major excitatory projection to layer 5, which contains the pyramidal cells that project to subcortical motor-related targets. Both structurally and functionally rather little is known about this interlaminar pathway, especially in higher mammals. Here, we made sparse ultrastructural reconstructions of the projection to layer 5 of three pyramidal neurons from layer 3 in cat V1 whose morphology, physiology, and synaptic connections with layers 2 and 3 were known. The dominant targets of the 74 identified synapses in layer 5 were the dendritic spines of pyramidal cells. The fractions of target spiny dendrites were 59, 61, and 84% for the three cells, with the remaining targets being dendrites of smooth neurons. These fractions were similar to the distribution of targets of unlabeled asymmetric synapses in the surrounding neuropil. Serial section reconstructions revealed that the target dendrites were heterogenous in morphology, indicating that different cell types are innervated. This new evidence indicates that the descending projection from the superficial layer pyramidal cells does not simply drive the output pyramidal cells that project to cortical and subcortical targets, but participates in the complex circuitry of the deep cortical layers.

Fluctuations in instantaneous frequency predict alpha amplitude during visual perception

Rhythmic neural activity in the alpha band (8-13 Hz) is thought to have an important role in the selective processing of visual information. Typically, modulations in alpha amplitude and instantaneous frequency are thought to reflect independent mechanisms impacting dissociable aspects of visual information processing. However, in complex systems with interacting oscillators such as the brain, amplitude and frequency are mathematically dependent. Here, we record electroencephalography in human subjects and show that both alpha amplitude and instantaneous frequency predict behavioral performance in the same visual discrimination task. Consistent with a model of coupled oscillators, we show that fluctuations in instantaneous frequency predict alpha amplitude on a single trial basis, empirically demonstrating that these metrics are not independent. This interdependence suggests that changes in amplitude and instantaneous frequency reflect a common change in the excitatory and inhibitory neural activity that regulates alpha oscillations and visual information processing.

Inhibition in Simple Cell Receptive Fields Is Broad and OFF-Subregion Biased

Inhibition in thalamorecipient layer 4 simple cells of primary visual cortex is believed to play important roles in establishing visual response properties and integrating visual inputs across their receptive fields (RFs). Simple cell RFs are characterized by nonoverlapping, spatially restricted subregions in which visual stimuli can either increase or decrease the firing rate of the cell, depending on contrast. Inhibition is believed to be triggered exclusively from visual stimulation of individual RF subregions. However, this view is at odds with the known anatomy of layer 4 interneurons in visual cortex and differs from recent findings in mouse visual cortex. Here we show with in vivo intracellular recordings in cats that while excitation is restricted to RF subregions, inhibition spans the width of simple cell RFs. Consequently, excitatory stimuli within a subregion concomitantly drive excitation and inhibition. Furthermore, we found that the distribution of inhibition across the RF is stronger toward OFF subregions. This inhibitory OFF-subregion bias has a functional consequence on spatial integration of inputs across the RF. A model based on the known anatomy of layer 4 demonstrates that the known proportion and connectivity of inhibitory neurons in layer 4 of primary visual cortex is sufficient to explain broad inhibition with an OFF-subregion bias while generating a variety of phase relations, including antiphase, between excitation and inhibition in response to drifting gratings.SIGNIFICANCE STATEMENT The wiring of excitatory and inhibitory neurons in cortical circuits is key to determining the response properties in sensory cortex. In the visual cortex, the first cells that receive visual input are simple cells in layer 4. The underlying circuitry responsible for the response properties of simple cells is not yet known. In this study, we challenge a long-held view concerning the pattern of inhibitory input and provide results that agree with current known anatomy. We show here that inhibition is evoked broadly across the receptive fields of simple cells, and we identify a surprising bias in inhibition within the receptive field. Our findings represent a step toward a unified view of inhibition across different species and sensory systems.

Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity

Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we determine the robustness of single-neuron stimulus selective responses, as well as the robustness of attractor states of networks of neurons performing memory tasks. We find that robustness to output noise requires synaptic connections to be in a balanced regime in which excitation and inhibition are strong and largely cancel each other. We evaluate the conditions required for this regime to exist and determine the properties of networks operating within it. A plausible synaptic plasticity rule for learning that balances weight configurations is presented. Our theory predicts an optimal ratio of the number of excitatory and inhibitory synapses for maximizing the encoding capacity of balanced networks for given statistics of afferent activations. Previous work has shown that balanced networks amplify spatiotemporal variability and account for observed asynchronous irregular states. Here we present a distinct type of balanced network that amplifies small changes in the impinging signals and emerges automatically from learning to perform neuronal and network functions robustly.

Stochastic resonance improves vision in the severely impaired

We verified whether a stochastic resonance paradigm (SR), with random interference (“noise”) added in optimal amounts, improves the detection of sub-threshold visual information by subjects with retinal disorder and impaired vision as it does in the normally sighted. Six levels of dynamic, zero-mean Gaussian noise were added to each pixel of images (13 contrast levels) in which alphabet characters were displayed against a uniform gray background. Images were presented with contrast below the subjective threshold to 14 visually impaired subjects (age: 22–53 yrs.). The fraction of recognized letters varied between 0 and 0.3 at baseline and increased in all subjects when noise was added in optimal amounts; peak recognition ranged between 0.2 and 0.8 at noise sigmas between 6 and 30 grey scale values (GSV) and decreased in all subjects at noise levels with sigma above 30 GSV. The results replicate in the visually impaired the facilitation of visual information processing with images presented in SR paradigms that has been documented in sighted subjects. The effect was obtained with low-level image manipulation and application appears readily possible: it would enhance the efficiency of today vision-improving aids and help in the development of the visual prostheses hopefully available in the future.

Once upon a (slow) time in the land of recurrent neuronal networks…

The brain must both react quickly to new inputs as well as store a memory of past activity. This requires biology that operates over a vast range of time scales. Fast time scales are determined by the kinetics of synaptic conductances and ionic channels; however, the mechanics of slow time scales are more complicated. In this opinion article we review two distinct network-based mechanisms that impart slow time scales in recurrently coupled neuronal networks. The first is in strongly coupled networks where the time scale of the internally generated fluctuations diverges at the transition between stable and chaotic firing rate activity. The second is in networks with finitely many members where noise-induced transitions between metastable states appear as a slow time scale in the ongoing network firing activity. We discuss these mechanisms with an emphasis on their similarities and differences.

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task

Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making.

This study explores how intelligent behavior emerges from the basic principles known at the cellular level of biological neuronal network dynamics. Compared to the approaches used in the artificial intelligence community, we applied biologically realistic modeling of neuronal dynamics and plasticity. The building blocks of the model are spiking neurons, spike-time dependent plasticity (STDP) and homeostatic rules, known experimentally, which are shown to play a fundamental role in both keeping the network stable and capable of continous learning. Our study predicts that a combination of these principles makes possible a foraging behavior in a previously unknown environment, including pattern classification to distinct between environment shapes which are rewarded and those which are punished and decision making to select the optimal strategy to acquire the maximal number of the rewarded elements. To solve this complex task we used multi-layer neuronal processing that implemented pattern generalization by unsupervised STDP at the earlier processing step, as commonly observed in the animal and human sensory processing, followed by reinforcement learning at the later steps. In the model, the intelligent behavior emerged spontaneously due to the network organization implementing both local unsupervised plasticity and reward feedback resulting from a successful behavior in the environment.

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

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

A Neural Signature of Divisive Normalization at the Level of Multisensory Integration in Primate Cortex

Studies of multisensory integration by single neurons have traditionally emphasized empirical principles that describe nonlinear interactions between inputs from two sensory modalities. We previously proposed that many of these empirical principles could be explained by a divisive normalization mechanism operating in brain regions where multisensory integration occurs. This normalization model makes a critical diagnostic prediction: a non-preferred sensory input from one modality, which activates the neuron on its own, should suppress the response to a preferred input from another modality. We tested this prediction by recording from neurons in macaque area MSTd that integrate visual and vestibular cues regarding self-motion. We show that many MSTd neurons exhibit the diagnostic form of cross-modal suppression, whereas unisensory neurons in area MT do not. The normalization model also fits population responses better than a model based on subtractive inhibition. These findings provide strong support for a divisive normalization mechanism in multisensory integration.

Alterations in Properties of Glutamatergic Transmission in the Temporal Cortex and Hippocampus Following Pilocarpine-Induced Acute Seizures in Wistar Rats

Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy in humans, and is often developed after an initial precipitating brain injury. This form of epilepsy is frequently resistant to pharmacological treatment; therefore, the prevention of TLE is the prospective approach to TLE therapy. The lithium-pilocarpine model in rats replicates some of the main features of TLE in human, including the pathogenic mechanisms of cell damage and epileptogenesis after a primary brain injury. In the present study, we investigated changes in the properties of glutamatergic transmission during the first 3 days after pilocarpine-induced acute seizures in Wistar rats (PILO-rats). Using RT-PCR and electrophysiological techniques, we compared the changes in the temporal cortex (TC) and hippocampus, brain areas differentially affected by seizures. On the first day, we found a transient increase in a ratio of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-methyl d-aspartate (NMDA) receptors in the excitatory synaptic response in pyramidal neurons of the CA1 area of the dorsal hippocampus, but not in the TC. This was accompanied by an increase in the slope of input-output (I/O) curves for fEPSPs recorded in CA1, suggesting an enhanced excitability in AMPARs in this brain area. There was no difference in the AMPA/NMDA ratio in control rats on the third day. We also revealed the alterations in NMDA receptor subunit composition in PILO-rats. The GluN2B/GluN2A mRNA expression ratio increased in the dorsal hippocampus but did not change in the ventral hippocampus or the TC. The kinetics of NMDA-mediated evoked EPSCs in hippocampal neurons was slower in PILO-rats compared with control animals. Ifenprodil, a selective antagonist of GluN2B-containing NMDARs, diminished the area and amplitude of evoked EPSCs in CA1 pyramidal cells more efficiently in PILO-rats compared with control animals. These results demonstrate that PILO-induced seizures lead to more severe alterations in excitatory synaptic transmission in the dorsal hippocampus than in the TC. Seizures affect the relative contribution of AMPA and NMDA receptor conductances in the synaptic response and increase the proportion of GluN2B-containing NMDARs in CA1 pyramidal neurons. These alterations disturb normal circuitry functions in the hippocampus, may cause neuron damage, and may be one of the important pathogenic mechanisms of TLE.

Synaptic Excitation in Spinal Motoneurons Alternates with Synaptic Inhibition and Is Balanced by Outward Rectification during Rhythmic Motor Network Activity

Regular firing in spinal motoneurons of red-eared turtles (Trachemys scripta elegans, either sex) evoked by steady depolarization at rest is replaced by irregular firing during functional network activity. The transition caused by increased input conductance and synaptic fluctuations in membrane potential was suggested to originate from intense concurrent inhibition and excitation. We show that the conductance increase in motoneurons during functional network activity is mainly caused by intrinsic outward rectification near threshold for action potentials by activation of voltage and Ca²⁺ gated K channels. Intrinsic outward rectification facilitates spiking by focusing synaptic depolarization near threshold for action potentials. By direct recording of synaptic currents, we also show that motoneurons are activated by out-of-phase peaks in excitation and inhibition during network activity, whereas continuous low-level concurrent inhibition and excitation may contribute to irregular firing.SIGNIFICANCE STATEMENT Neurons embedded in active neural networks can enter a high-conductance state. High-conductance states were observed in spinal motoneurons during rhythmic motor behavior. Assuming no change in intrinsic conductance, it was suggested that the high-conductance state in motoneurons originated from balanced inhibition and excitation. In this study, we demonstrate that intrinsic outward rectification significantly contributes to the high-conductance state. Outward rectification balances synaptic excitation and maintains membrane potential near spike threshold. In addition, direct synaptic current recordings show out-of-phase excitation and inhibition in motoneurons during rhythmic network activity.

Estimation of Synaptic Conductances in Presence of Nonlinear Effects Caused by Subthreshold Ionic Currents

Subthreshold fluctuations in neuronal membrane potential traces contain nonlinear components, and employing nonlinear models might improve the statistical inference. We propose a new strategy to estimate synaptic conductances, which has been tested using in silico data and applied to in vivo recordings. The model is constructed to capture the nonlinearities caused by subthreshold activated currents, and the estimation procedure can discern between excitatory and inhibitory conductances using only one membrane potential trace. More precisely, we perform second order approximations of biophysical models to capture the subthreshold nonlinearities, resulting in quadratic integrate-and-fire models, and apply approximate maximum likelihood estimation where we only suppose that conductances are stationary in a 50–100 ms time window. The results show an improvement compared to existent procedures for the models tested here.

Retinal Circuitry Balances Contrast Tuning of Excitation and Inhibition to Enable Reliable Computation of Direction Selectivity

Feedforward (FF) inhibition is a common motif in many neural networks. Typically, excitatory inputs drive both principal neurons and interneurons; the interneurons then inhibit the principal neurons, thereby regulating the strength and timing of the FF signal. The interneurons introduce a likely nonlinear processing step that could distort the excitation/inhibition (E/I) ratio in the principal neuron, potentially degrading the reliability of computation in the circuit. In the retina, FF inhibition is an essential feature of the circuitry underlying direction selectivity (DS): glutamatergic bipolar cells (BCs) provide excitatory input to direction-selective ganglion cells (DSGCs) and GABAergic starburst amacrine cells (SACs), and the SACs then provide FF inhibition onto DSGCs. Robust DS computation requires a consistent synaptic E/I ratio in the DSGC in various visual conditions. Here, we show in mouse retina that the E/I ratio is maintained in DSGCs over a wide stimulus contrast range due to compensatory mechanisms in the diverse population of presynaptic BCs. BC inputs to SACs exhibit higher contrast sensitivity, so that the subsequent nonlinear transformation in SACs reduces the contrast sensitivity of FF inhibition to match the sensitivity of direct excitatory inputs onto DSGCs. Measurements of light-evoked responses from individual BC synaptic terminals suggest that the distinct sensitivity of BC inputs reflects different contrast sensitivity between BC subtypes. Numerical simulations suggest that this network arrangement is crucial for reliable DS computation.

SIGNIFICANCE STATEMENT

Properly balanced excitation and inhibition are essential for many neuronal computations across brain regions. Feedforward inhibition circuitry, in which a common excitatory source drives both the principal cell and an interneuron, is a typical mechanism by which neural networks maintain this balance. Feedforward circuits may become imbalanced at low stimulation levels, however, if the excitatory drive is too weak to overcome the activation threshold in the interneuron. Here we reveal how excitation and inhibition remain balanced in direction selective ganglion cells in the mouse retina over a wide visual stimulus range.

Extraction of Synaptic Input Properties in Vivo

Knowledge of synaptic input is crucial for understanding synaptic integration and ultimately neural function. However, in vivo, the rates at which synaptic inputs arrive are high, so that it is typically impossible to detect single events. We show here that it is nevertheless possible to extract the properties of the events and, in particular, to extract the event rate, the synaptic time constants, and the properties of the event size distribution from in vivo voltage-clamp recordings. Applied to cerebellar interneurons, our method reveals that the synaptic input rate increases from 600 Hz during rest to 1000 Hz during locomotion, while the amplitude and shape of the synaptic events are unaffected by this state change. This method thus complements existing methods to measure neural function in vivo.

Diverse effects of stimulus history in waking mouse auditory cortex

Responses to auditory stimuli are often strongly influenced by recent stimulus history. For example, in a paradigm called forward suppression, brief sounds can suppress the perception of, and the neural responses to, a subsequent sound, with the magnitude of this suppression depending on both the spectral and temporal distances between the sounds. As a step towards understanding the mechanisms that generate these adaptive representations in awake animals, we quantitatively characterize responses to two-tone sequences in the auditory cortex of waking mice. We find that cortical responses in a forward suppression paradigm are more diverse in waking mice than previously appreciated, that these responses vary between cells with different firing characteristics and waveform shapes, but that the variability in these responses is not substantially related to cortical depth or columnar location. Moreover, responses to the first tone in the sequence are often not linearly related to the suppression of the second tone response, suggesting that spike-frequency adaptation of cortical cells is not a large contributor to forward suppression or its variability. Instead, we use a simple multilayered model to show that cell-to-cell differences in the balance of intracortical inhibition and excitation will naturally produce such a diversity of forward interactions. We propose that diverse inhibitory connectivity allows the cortex to encode spectro-temporally fluctuating stimuli in multiple parallel ways.NEW & NOTEWORTHY Behavioral and neural responses to auditory stimuli are profoundly influenced by recent sounds, yet how this occurs is not known. Here, the authors show in the auditory cortex of awake mice that the quality of history-dependent effects is diverse and related to cell type, response latency, firing rates, and receptive field bandwidth. In a cortical model, differences in excitatory-inhibitory balance can produce this diversity, providing the cortex with multiple ways of representing temporally complex information.

Functional consequences of inhibitory plasticity: homeostasis, the excitation-inhibition balance and beyond

Computational neuroscience has a long-standing tradition of investigating the consequences of excitatory synaptic plasticity. In contrast, the functions of inhibitory plasticity are still largely nebulous, particularly given the bewildering diversity of interneurons in the brain. Here, we review recent computational advances that provide first suggestions for the functional roles of inhibitory plasticity, such as a maintenance of the excitation-inhibition balance, a stabilization of recurrent network dynamics and a decorrelation of sensory responses. The field is still in its infancy, but given the existing body of theory for excitatory plasticity, it is likely to mature quickly and deliver important insights into the self-organization of inhibitory circuits in the brain.

Comparison of mechanisms for contrast-invariance of orientation selectivity in simple cells

The simple cells of the visual cortex respond over a narrow range of stimulus orientations, and this tuning is invariant to the contrast at which the stimulus is presented. The inputs to a single cell derive from a population of thalamic cells that provide a bell-shaped tuning width and offset that increases with stimulus contrast. Synaptic depression, noise and inhibition have been proposed as feedforward mechanisms to explain why these increases do not appear in simple cells. The extent to which these three mechanisms contribute to contrast-invariant orientation tuning is unknown. Consequently, the aim was to test the hypothesis that these mechanisms do not contribute equally. Unlike previous studies, all mechanisms were examined using the same network model based on Banitt et al. (2007). The results showed that thalamocortical synaptic noise was essential and sufficient to widen tuning widths at low contrasts to that of higher contrasts but could not counteract the offset at higher contrasts. Thalamocortical synaptic depression could only be used to counteract a small fraction of the offset otherwise the relationship between contrast and response rate was disrupted. Only broadly tuned simple and complex cell inhibition could counteract the remaining offset for all stimulus contrasts but complex cell inhibition reduced the gain of the response. These results suggest unequal contributions of these feedforward mechanisms with thalamic synaptic noise widening tuning widths for low contrasts, synaptic depression counteracting a small component of the offset and synaptic inhibition completely removing the remaining offset to produce contrast-invariant orientation tuning.

Breaking the Excitation-Inhibition Balance Makes the Cortical Network's Space-Time Dynamics Distinguish Simple Visual Scenes

Brain dynamics are often taken to be temporal dynamics of spiking and membrane potentials in a balanced network. Almost all evidence for a balanced network comes from recordings of cell bodies of few single neurons, neglecting more than 99% of the cortical network. We examined the space-time dynamics of excitation and inhibition simultaneously in dendrites and axons over four visual areas of ferrets exposed to visual scenes with stationary and moving objects. The visual stimuli broke the tight balance between excitation and inhibition such that the network exhibited longer episodes of net excitation subsequently balanced by net inhibition, in contrast to a balanced network. Locally in all four areas the amount of net inhibition matched the amount of net excitation with a delay of 125 ms. The space-time dynamics of excitation-inhibition evolved to reduce the complexity of neuron interactions over the whole network to a flow on a low-(3)-dimensional manifold within 80 ms. In contrast to the pure temporal dynamics, the low dimensional flow evolved to distinguish the simple visual scenes.

An inhibitory pull-push circuit in frontal cortex

Push-pull is a canonical computation of excitatory cortical circuits. Here we identify a pull-push inhibitory circuit in frontal cortex that originates in vasoactive intestinal polypeptide (VIP) expressing interneurons. During arousal, VIP cells rapidly and directly inhibit pyramidal neurons; VIP cells also indirectly excite these pyramidal neurons via parallel disinhibition. Thus, arousal exerts a feed-back pull-push influence on excitatory neurons – an inversion of the canonical push-pull of feed-forward input.

Functional implications of orientation maps in primary visual cortex

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

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

Parallel processing by cortical inhibition enables context-dependent behavior

Physical features of sensory stimuli are fixed, but sensory perception is context-dependent. The precise mechanisms that govern contextual modulation remain unknown. Here, we trained mice to switch between two contexts: passively listening to pure tones vs. performing a recognition task for the same stimuli. Two-photon imaging showed that many excitatory neurons in auditory cortex were suppressed, while some cells became more active during behavior. Whole-cell recordings showed that excitatory inputs were only modestly affected by context, but inhibition was more sensitive, with PV, SOM+, and VIP+ interneurons balancing inhibition/disinhibition within the network. Cholinergic modulation was involved in context-switching, with cholinergic axons increasing activity during behavior and directly depolarizing inhibitory cells. Network modeling captured these findings, but only when modulation coincidently drove all three interneuron subtypes, ruling out either inhibition or disinhibition alone as sole mechanism for active engagement. Parallel processing of cholinergic modulation by cortical interneurons therefore enables context-dependent behavior.

Theta-Gamma Coding Meets Communication-through-Coherence: Neuronal Oscillatory Multiplexing Theories Reconciled

Several theories have been advanced to explain how cross-frequency coupling, the interaction of neuronal oscillations at different frequencies, could enable item multiplexing in neural systems. The communication-through-coherence theory proposes that phase-matching of gamma oscillations between areas enables selective processing of a single item at a time, and a later refinement of the theory includes a theta-frequency oscillation that provides a periodic reset of the system. Alternatively, the theta-gamma neural code theory proposes that a sequence of items is processed, one per gamma cycle, and that this sequence is repeated or updated across theta cycles. In short, both theories serve to segregate representations via the temporal domain, but differ on the number of objects concurrently represented. In this study, we set out to test whether each of these theories is actually physiologically plausible, by implementing them within a single model inspired by physiological data. Using a spiking network model of visual processing, we show that each of these theories is physiologically plausible and computationally useful. Both theories were implemented within a single network architecture, with two areas connected in a feedforward manner, and gamma oscillations generated by feedback inhibition within areas. Simply increasing the amplitude of global inhibition in the lower area, equivalent to an increase in the spatial scope of the gamma oscillation, yielded a switch from one mode to the other. Thus, these different processing modes may co-exist in the brain, enabling dynamic switching between exploratory and selective modes of attention.

There is a growing consensus that neuronal oscillations constitute a fundamental computational mechanism in the brain. Beyond this, recent experimental evidence has highlighted interactions between oscillations at high and low frequencies (e.g. gamma oscillations, 40–80 Hz, are modulated by theta oscillations, 4–10 Hz), and two major theories have developed regarding the functional role of this kind of cross-frequency coupling. Here, we present a computational modelling study of these theories with strong implications for biological studies. Firstly, we demonstrate for the first time that each of these theories is physiologically plausible, in that they can be implemented in a spiking network model with parameters guided by experimental data. Secondly, we show that they are each computationally useful, able to overcome a feature-binding ambiguity in a presented stimulus. Finally, we implement both theories within a single network model, and find that only a single parameter change is required to switch between the two processing states. This leads to the exciting new proposal that both theories may be correct, both implemented in the brain, with dynamic switching between modes according to processing and attentional requirements.

Synaptic Conductances during Interictal Discharges in Pyramidal Neurons of Rat Entorhinal Cortex

In epilepsy, the balance of excitation and inhibition underlying the basis of neural network activity shifts, resulting in neuronal network hyperexcitability and recurrent seizure-associated discharges. Mechanisms involved in ictal and interictal events are not fully understood, in particular, because of controversial data regarding the dynamics of excitatory and inhibitory synaptic conductances. In the present study, we estimated AMPAR-, NMDAR-, and GABA_A R-mediated conductances during two distinct types of interictal discharge (IID) in pyramidal neurons of rat entorhinal cortex in cortico-hippocampal slices. Repetitively emerging seizure-like events and IIDs were recorded in high extracellular potassium, 4-aminopyridine, and reduced magnesium-containing solution. An original procedure for estimating synaptic conductance during IIDs was based on the differences among the current-voltage characteristics of the synaptic components. The synaptic conductance dynamics obtained revealed that the first type of IID is determined by activity of GABA_A R channels with depolarized reversal potential. The second type of IID is determined by the interplay between excitation and inhibition, with early AMPAR and prolonged depolarized GABA_A R and NMDAR-mediated components. The study then validated the contribution of these components to IIDs by intracellular pharmacological isolation. These data provide new insights into the mechanisms of seizures generation, development, and cessation.

Somatostatin-Expressing Inhibitory Interneurons in Cortical Circuits

Cortical inhibitory neurons exhibit remarkable diversity in their morphology, connectivity, and synaptic properties. Here, we review the function of somatostatin-expressing (SOM) inhibitory interneurons, focusing largely on sensory cortex. SOM neurons also comprise a number of subpopulations that can be distinguished by their morphology, input and output connectivity, laminar location, firing properties, and expression of molecular markers. Several of these classes of SOM neurons show unique dynamics and characteristics, such as facilitating synapses, specific axonal projections, intralaminar input, and top-down modulation, which suggest possible computational roles. SOM cells can be differentially modulated by behavioral state depending on their class, sensory system, and behavioral paradigm. The functional effects of such modulation have been studied with optogenetic manipulation of SOM cells, which produces effects on learning and memory, task performance, and the integration of cortical activity. Different classes of SOM cells participate in distinct disinhibitory circuits with different inhibitory partners and in different cortical layers. Through these disinhibitory circuits, SOM cells help encode the behavioral relevance of sensory stimuli by regulating the activity of cortical neurons based on subcortical and intracortical modulatory input. Associative learning leads to long-term changes in the strength of connectivity of SOM cells with other neurons, often influencing the strength of inhibitory input they receive. Thus despite their heterogeneity and variability across cortical areas, current evidence shows that SOM neurons perform unique neural computations, forming not only distinct molecular but also functional subclasses of cortical inhibitory interneurons.

The neurogenesis of P1 and N1: A concurrent EEG/LFP study

It is generally recognised that event related potentials (ERPs) of electroencephalogram (EEG) primarily reflect summed post-synaptic activity of the local pyramidal neural population(s). However, it is still not understood how the positive and negative deflections (e.g. P1, N1 etc) observed in ERP recordings are related to the underlying excitatory and inhibitory post-synaptic activity. We investigated the neurogenesis of P1 and N1 in ERPs by pharmacologically manipulating inhibitory post-synaptic activity in the somatosensory cortex of rodent, and concurrently recording EEG and local field potentials (LFPs). We found that the P1 wave in the ERP and LFP of the supragranular layers is determined solely by the excitatory post-synaptic activity of the local pyramidal neural population, as is the initial segment of the N1 wave across cortical depth. The later part of the N1 wave was modulated by inhibitory post-synaptic activity, with its peak and the pulse width increasing as inhibition was reduced. These findings suggest that the temporal delay of inhibition with respect to excitation observed in intracellular recordings is also reflected in extracellular field potentials (FPs), resulting in a temporal window during which only excitatory post-synaptic activity and leak channel activity are recorded in the ERP and evoked LFP time series. Based on these findings, we provide clarification on the interpretation of P1 and N1 in terms of the excitatory and inhibitory post-synaptic activities of the local pyramidal neural population(s).