Circuits that build visual cortical receptive fields

Visual processing: Systematic variation in light-dark bias across visual space

Detecting changes in luminance is a fundamental property of the visual system. A new study shows that lights and darks are represented differently across visual space, with strong OFF bias in central vision and balanced ON/OFF in the periphery.

Differences in expression of calcium binding proteins in the perirhinal and retrosplenial cortex of the rat

The main aim was to describe interneuronal population expressing calcium binding proteins calretinin (CR) and parvalbumin (PV) in the perirhinal (PRC) and retrosplenial (RSC) cortex of the rat. These two cortical areas differ strikingly in their connectivity and function, which could be caused also by different structure of the interneuronal populations. Having a precise knowledge of the cellular composition of any cerebral area forms one of the basic input parameters and tenets for computational modelling of neuronal networks and for understanding some pathological conditions, like generating and spreading of epileptic activity. PRC possesses higher absolute and relative densities of CR+ and PV+ neurons than RSC, but the CR : PV ratio is higher in the RSC, which is similar to the neocortex. The bipolar/bitufted neurons are most common type of CR+ population, while the majority of PV+ neurons show multipolar morphology. Current results indicate that main difference between analysed areas is in density of CR+ neurons, which was significantly higher in the PRC. Our results coupled with works of other authors show that there are significant differences in the interneuronal composition and distribution of heretofore seemingly similar transitional cortical areas. These results may contribute to the better understanding of the mechanism of function of this cortical region in normal and diseased states.

Three rules govern thalamocortical connectivity of fast-spike inhibitory interneurons in the visual cortex

Some cortical neurons receive highly selective thalamocortical (TC) input, but others do not. Here, we examine connectivity of single thalamic neurons (lateral geniculate nucleus, LGN) onto putative fast-spike inhibitory interneurons in layer 4 of rabbit visual cortex. We show that three 'rules' regulate this connectivity. These rules concern: (1) the precision of retinotopic alignment, (2) the amplitude of the postsynaptic local field potential elicited near the interneuron by spikes of the LGN neuron, and (3) the interneuron's response latency to strong, synchronous LGN input. We found that virtually all first-order fast-spike interneurons receive input from nearly all LGN axons that synapse nearby, regardless of their visual response properties. This was not the case for neighboring regular-spiking neurons. We conclude that profuse and highly promiscuous TC inputs to layer-4 fast-spike inhibitory interneurons generate response properties that are well-suited to mediate a fast, sensitive, and broadly tuned feed-forward inhibition of visual cortical excitatory neurons.

Functional representation of the symbol digit modalities test in relapsing remitting multiple sclerosis

BACKGROUND

The Symbol Digit Modalities Test (SDMT) is essential in the screening of cognitive impairments in multiple sclerosis (MS). Methodological adaptions of the SDMT on functional MRI exist, but without specific investigation of more cognitive components of information processing speed (IPS). Additionally, there is only limited data on functional differences between MS-patients and healthy controls (HC).

METHODS

20 MS-patients and 20 HC were investigated executing the original version of the SDMT on fMRI. We analyzed (1) neural networks as indicated in the methodological adaptions (i.e. frontal (Brodman area BA6, BA9), parietal (BA7), occipital (BA17) and cerebellar), (2) functional activations of cognitive components of IPS and (3) functional differences between MS and HC during SDMT.

RESULTS

MS patients performed worse during the SDMT. Both groups demonstrated activation on each region of interest. Cognitive component of IPS was driven by superior parietal and posterior cerebellar activation. MS-patients showed decreased cingulate activation during SDMT as compared to HC.

CONCLUSION

The original SDMT task revealed comparable fMRI-activation sites as reported for previous adaptions. Cognitive components of IPS depend on superior parietal and medial posterior cerebellar regions known to process visuo-spatial integration and anticipation. Attention related areas in the cingulate cortex were decreased in MS-patients.

Risk-taking bias in human decision-making is encoded via a right-left brain push-pull system

Biases and fallacies can nudge humans in one direction or another as they make decisions. During gambling, bias is often generated by internal factors, including individual preferences, past experience, or emotions, and can move a person toward or away from risky behavior. The neural mechanisms responsible for generating internal bias are largely unknown, limiting the treatment of patients with neurological diseases that impair decision-making. We applied mathematical modeling techniques and high-resolution intracerebral recordings to uncover how a hidden internal bias builds up from past experiences to influence decisions and where this internal bias is encoded in the brain. Our findings suggest that biology exploits a distributed lateralized push–pull neural system to govern counterintuitive and highly variable decision-making in humans.

A person’s decisions vary even when options stay the same, like when a gambler changes bets despite constant odds of winning. Internal bias (e.g., emotion) contributes to this variability and is shaped by past outcomes, yet its neurobiology during decision-making is not well understood. To map neural circuits encoding bias, we administered a gambling task to 10 participants implanted with intracerebral depth electrodes in cortical and subcortical structures. We predicted the variability in betting behavior within and across patients by individual bias, which is estimated through a dynamical model of choice. Our analysis further revealed that high-frequency activity increased in the right hemisphere when participants were biased toward risky bets, while it increased in the left hemisphere when participants were biased away from risky bets. Our findings provide electrophysiological evidence that risk-taking bias is a lateralized push–pull neural system governing counterintuitive and highly variable decision-making in humans.

Firing-rate based network modeling of the dLGN circuit: Effects of cortical feedback on spatiotemporal response properties of relay cells

Visually evoked signals in the retina pass through the dorsal geniculate nucleus (dLGN) on the way to the visual cortex. This is however not a simple feedforward flow of information: there is a significant feedback from cortical cells back to both relay cells and interneurons in the dLGN. Despite four decades of experimental and theoretical studies, the functional role of this feedback is still debated. Here we use a firing-rate model, the extended difference-of-Gaussians (eDOG) model, to explore cortical feedback effects on visual responses of dLGN relay cells. For this model the responses are found by direct evaluation of two- or three-dimensional integrals allowing for fast and comprehensive studies of putative effects of different candidate organizations of the cortical feedback. Our analysis identifies a special mixed configuration of excitatory and inhibitory cortical feedback which seems to best account for available experimental data. This configuration consists of (i) a slow (long-delay) and spatially widespread inhibitory feedback, combined with (ii) a fast (short-delayed) and spatially narrow excitatory feedback, where (iii) the excitatory/inhibitory ON-ON connections are accompanied respectively by inhibitory/excitatory OFF-ON connections, i.e. following a phase-reversed arrangement. The recent development of optogenetic and pharmacogenetic methods has provided new tools for more precise manipulation and investigation of the thalamocortical circuit, in particular for mice. Such data will expectedly allow the eDOG model to be better constrained by data from specific animal model systems than has been possible until now for cat. We have therefore made the Python tool pyLGN which allows for easy adaptation of the eDOG model to new situations.

On route from the retina to primary visual cortex, visually evoked signals have to pass through the dorsal lateral geniculate nucleus (dLGN). However, this is not an exclusive feedforward flow of information as feedback exists from neurons in the cortex back to both relay cells and interneurons in the dLGN. The functional role of this feedback remains mostly unresolved. Here, we use a firing-rate model, the extended difference-of-Gaussians (eDOG) model, to explore cortical feedback effects on visual responses of dLGN relay cells. Our analysis indicates that a particular mix of excitatory and inhibitory cortical feedback agrees best with available experimental observations. In this configuration ON-center relay cells receive both excitatory and (indirect) inhibitory feedback from ON-center cortical cells (ON-ON feedback) where the excitatory feedback is fast and spatially narrow while the inhibitory feedback is slow and spatially widespread. In addition to the ON-ON feedback, the connections are accompanied by OFF-ON connections following a so-called phase-reversed (push-pull) arrangement. To facilitate further applications of the model, we have made the Python tool pyLGN which allows for easy modification and evaluation of the a priori quite general eDOG model to new situations.

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

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

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

Synaptic Contributions to Receptive Field Structure and Response Properties in the Rodent Lateral Geniculate Nucleus of the Thalamus

Comparative physiological and anatomical studies have greatly advanced our understanding of sensory systems. Many lines of evidence show that the murine lateral geniculate nucleus (LGN) has unique attributes, compared with other species such as cat and monkey. For example, in rodent, thalamic receptive field structure is markedly diverse, and many cells are sensitive to stimulus orientation and direction. To explore shared and different strategies of synaptic integration across species, we made whole-cell recordings in vivo from the murine LGN during the presentation of visual stimuli, analyzed the results with different computational approaches, and compared our findings with those from cat. As for carnivores, murine cells with classical center-surround receptive fields had a "push-pull" structure of excitation and inhibition within a given On or Off subregion. These cells compose the largest single population in the murine LGN (∼40%), indicating that push-pull is key in the form vision pathway across species. For two cell types with overlapping On and Off responses, which recalled either W3 or suppressed-by-contrast ganglion cells in murine retina, inhibition took a different form and was most pronounced for spatially extensive stimuli. Other On-Off cells were selective for stimulus orientation and direction. In these cases, retinal inputs were tuned and, for oriented cells, the second-order subunit of the receptive field predicted the preferred angle. By contrast, suppression was not tuned and appeared to sharpen stimulus selectivity. Together, our results provide new perspectives on the role of excitation and inhibition in retinothalamic processing.

SIGNIFICANCE STATEMENT

We explored the murine lateral geniculate nucleus from a comparative physiological perspective. In cat, most retinal cells have center-surround receptive fields and push-pull excitation and inhibition, including neurons with the smallest (highest acuity) receptive fields. The same is true for thalamic relay cells. In mouse retina, the most numerous cell type has the smallest receptive fields but lacks push-pull. The most common receptive field in rodent thalamus, however, is center-surround with push-pull. Thus, receptive field structure supersedes size per se for form vision. Further, for many orientation-selective cells, the second-order component of the receptive field aligned with stimulus preference, whereas suppression was untuned. Thus, inhibition may improve spatial resolution and sharpen other forms of selectivity in rodent lateral geniculate nucleus.

Inferring Master Painters' Esthetic Biases from the Statistics of Portraits

The Processing Fluency Theory posits that the ease of sensory information processing in the brain facilitates esthetic pleasure. Accordingly, the theory would predict that master painters should display biases toward visual properties such as symmetry, balance, and moderate complexity. Have these biases been occurring and if so, have painters been optimizing these properties (fluency variables)? Here, we address these questions with statistics of portrait paintings from the Early Renaissance period. To do this, we first developed different computational measures for each of the aforementioned fluency variables. Then, we measured their statistics in 153 portraits from 26 master painters, in 27 photographs of people in three controlled poses, and in 38 quickly snapped photographs of individual persons. A statistical comparison between Early Renaissance portraits and quickly snapped photographs revealed that painters showed a bias toward balance, symmetry, and moderate complexity. However, a comparison between portraits and controlled-pose photographs showed that painters did not optimize each of these properties. Instead, different painters presented biases toward different, narrow ranges of fluency variables. Further analysis suggested that the painters' individuality stemmed in part from having to resolve the tension between complexity vs. symmetry and balance. We additionally found that constraints on the use of different painting materials by distinct painters modulated these fluency variables systematically. In conclusion, the Processing Fluency Theory of Esthetic Pleasure would need expansion if we were to apply it to the history of visual art since it cannot explain the lack of optimization of each fluency variables. To expand the theory, we propose the existence of a Neuroesthetic Space, which encompasses the possible values that each of the fluency variables can reach in any given art period. We discuss the neural mechanisms of this Space and propose that it has a distributed representation in the human brain. We further propose that different artists reside in different, small sub-regions of the Space. This Neuroesthetic-Space hypothesis raises the question of how painters and their paintings evolve across art periods.

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

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

How inhibitory circuits in the thalamus serve vision

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

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

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

SIGNIFICANCE STATEMENT

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

Modeling Inhibitory Interneurons in Efficient Sensory Coding Models

There is still much unknown regarding the computational role of inhibitory cells in the sensory cortex. While modeling studies could potentially shed light on the critical role played by inhibition in cortical computation, there is a gap between the simplicity of many models of sensory coding and the biological complexity of the inhibitory subpopulation. In particular, many models do not respect that inhibition must be implemented in a separate subpopulation, with those inhibitory interneurons having a diversity of tuning properties and characteristic E/I cell ratios. In this study we demonstrate a computational framework for implementing inhibition in dynamical systems models that better respects these biophysical observations about inhibitory interneurons. The main approach leverages recent work related to decomposing matrices into low-rank and sparse components via convex optimization, and explicitly exploits the fact that models and input statistics often have low-dimensional structure that can be exploited for efficient implementations. While this approach is applicable to a wide range of sensory coding models (including a family of models based on Bayesian inference in a linear generative model), for concreteness we demonstrate the approach on a network implementing sparse coding. We show that the resulting implementation stays faithful to the original coding goals while using inhibitory interneurons that are much more biophysically plausible.

Cortical function is a result of coordinated interactions between excitatory and inhibitory neural populations. In previous theoretical models of sensory systems, inhibitory neurons are often ignored or modeled too simplistically to contribute to understanding their role in cortical computation. In biophysical reality, inhibition is implemented with interneurons that have different characteristics from the population of excitatory cells. In this study, we propose a computational approach for including inhibition in theoretical models of neural coding in a way that respects several of these important characteristics, such as the relative number of inhibitory cells and the diversity of their response properties. The main idea is that the significant structure of the sensory world is reflected in very structured models of sensory coding, which can then be exploited in the implementation of the model using modern computational techniques. We demonstrate this approach on one specific model of sensory coding (called “sparse coding”) that has been successful at modeling other aspects of sensory cortex.

A coding transformation for temporally structured sounds within auditory cortical neurons

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

Synaptic mechanisms underlying interaural level difference selectivity in rat auditory cortex

The interaural level difference (ILD) is a sound localization cue that is extensively processed in the auditory brain stem and midbrain and is also represented in the auditory cortex. Here, we asked whether neurons in the auditory cortex passively inherit their ILD tuning from subcortical sources or whether their spiking preferences were actively shaped by local inhibition. If inherited, the ILD selectivity of spiking output should match that of excitatory synaptic input. If shaped by local inhibition, by contrast, excitation should be more broadly tuned than spiking output with inhibition suppressing spiking for nonpreferred stimuli. To distinguish between these two processing strategies, we compared spiking responses with excitation and inhibition in the same neurons across a range of ILDs and average binaural sound levels. We found that cells preferring contralateral ILDs (often called EI cells) followed the inheritance strategy. In contrast, cells that were unresponsive to monaural sounds but responded predominantly to near-zero ILDs (PB cells) instead showed evidence of the local processing strategy. These PB cells received excitatory inputs that were similar to those received by the EI cells. However, contralateral monaural sounds and ILDs >0 dB elicited strong inhibition, quenching the spiking output. These results suggest that in the rat auditory cortex, EI cells do not utilize inhibition to shape ILD sensitivity, whereas PB cells do. We conclude that an auditory cortical circuit computes sensitivity for near-zero ILDs.

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

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

The function of metabotropic glutamate receptors in thalamus and cortex

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

A causal perspective on the analysis of signal and noise correlations and their role in population coding

The role of correlations between neuronal responses is crucial to understanding the neural code. A framework used to study this role comprises a breakdown of the mutual information between stimuli and responses into terms that aim to account for different coding modalities and the distinction between different notions of independence. Here we complete the list of types of independence and distinguish activity independence (related to total correlations), conditional independence (related to noise correlations), signal independence (related to signal correlations), coding independence (related to information transmission), and information independence (related to redundancy). For each type, we identify the probabilistic criterion that defines it, indicate the information-theoretic measure used as statistic to test for it, and provide a graphical criterion to recognize the causal configurations of stimuli and responses that lead to its existence. Using this causal analysis, we first provide sufficiency conditions relating these types. Second, we differentiate the use of the measures as statistics to test for the existence of independence from their use for quantification. We indicate that signal and noise correlation cannot be quantified separately. Third, we explicitly define alternative system configurations used to construct the measures, in which noise correlations or noise and signal correlations are eliminated. Accordingly, we examine which measures are meaningful only as a comparison across configurations and which ones provide a characterization of the actually observed responses without resorting to other configurations. Fourth, we compare the commonly used nonparametric approach to eliminate noise correlations with a functional (model-based) approach, showing that the former approach does not remove those effects of noise correlations captured by the tuning properties of the individual neurons, and implies nonlocal causal structure manipulations. These results improve the interpretation of the measures on the framework and help in understanding how to apply it to analyze the role of correlations.

Strengthening of Direction Selectivity by Broadly Tuned and Spatiotemporally Slightly Offset Inhibition in Mouse Visual Cortex

Direction selectivity (DS) of neuronal responses is fundamental for motion detection. How the integration of synaptic excitation and inhibition contributes to DS however remains not well-understood. Here, in vivo whole-cell voltage-clamp recordings in mouse primary visual cortex (V1) revealed that layer 4 simple cells received direction-tuned excitatory inputs but barely tuned inhibitory inputs under drifting-bar stimulation. Excitation and inhibition exhibited differential temporal offsets under movements of opposite directions: excitation peaked earlier than inhibition at the preferred direction, and vice versa at the null direction. This could be attributed to a small spatial mismatch between overlapping excitatory and inhibitory receptive fields: the distribution of excitatory input strengths was skewed and the skewness was strongly correlated with the DS of excitatory input, whereas that of inhibitory input strengths was spatially symmetric. Neural modeling revealed that the relatively stronger inhibition under null directional movements, as well as the specific spatial-temporal offsets between excitation and inhibition, allowed inhibition to enhance the DS of output responses by suppressing the null response more effectively than the preferred response. Our data demonstrate that while tuned excitatory input provides the basis for DS in mouse V1, the largely untuned and spatiotemporally offset inhibition contributes importantly to sharpening of DS.

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

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

A dendritic mechanism for decoding traveling waves: principles and applications to motor cortex

Traveling waves of neuronal oscillations have been observed in many cortical regions, including the motor and sensory cortex. Such waves are often modulated in a task-dependent fashion although their precise functional role remains a matter of debate. Here we conjecture that the cortex can utilize the direction and wavelength of traveling waves to encode information. We present a novel neural mechanism by which such information may be decoded by the spatial arrangement of receptors within the dendritic receptor field. In particular, we show how the density distributions of excitatory and inhibitory receptors can combine to act as a spatial filter of wave patterns. The proposed dendritic mechanism ensures that the neuron selectively responds to specific wave patterns, thus constituting a neural basis of pattern decoding. We validate this proposal in the descending motor system, where we model the large receptor fields of the pyramidal tract neurons — the principle outputs of the motor cortex — decoding motor commands encoded in the direction of traveling wave patterns in motor cortex. We use an existing model of field oscillations in motor cortex to investigate how the topology of the pyramidal cell receptor field acts to tune the cells responses to specific oscillatory wave patterns, even when those patterns are highly degraded. The model replicates key findings of the descending motor system during simple motor tasks, including variable interspike intervals and weak corticospinal coherence. By additionally showing how the nature of the wave patterns can be controlled by modulating the topology of local intra-cortical connections, we hence propose a novel integrated neuronal model of encoding and decoding motor commands.

Physiological studies in humans and monkeys have revealed spatially organized waves of neuronal activity that propagate across the cortex during sensory or behavioral tasks. However the functional role of such waves remains elusive. In the present study, we use numerical simulation to investigate whether wave patterns may serve as a basis for neural coding in cortex. Specifically, we propose a theoretical dendritic mechanism which permits neurons to respond selectively to the morphological properties of waves. In this proposal, the arrangement of excitatory and inhibitory receptors within the dendritic receptor field constitutes a spatial filter of the incoming wave patterns. The proposed mechanism allows the neuron to discriminate waves based on wavelength and orientation, thereby providing a basis for neural decoding. We explore this concept in the context of the descending motor system where the pyramidal tract neurons of motor cortex monosynaptically innervate motor neurons in the spinal cord. Pyramidal tract neurons have broad dendritic fields which make them ideal candidates for spatial filters of waves in motor cortex. Our model demonstrates how wave patterns in motor cortex can be transformed into a descending motor drive which replicates some fundamental oscillatory properties of human motor physiology.

Midbrain local circuits shape sound intensity codes

Hierarchical processing of sensory information requires interaction at multiple levels along the peripheral to central pathway. Recent evidence suggests that interaction between driving and modulating components can shape both top down and bottom up processing of sensory information. Here we show that a component inherited from extrinsic sources combines with local components to code sound intensity. By applying high concentrations of divalent cations to neurons in the nucleus of the inferior colliculus in the auditory midbrain, we show that as sound intensity increases, the source of synaptic efficacy changes from inherited inputs to local circuits. In neurons with a wide dynamic range response to intensity, inherited inputs increase firing rates at low sound intensities but saturate at mid-to-high intensities. Local circuits activate at high sound intensities and widen dynamic range by continuously increasing their output gain with intensity. Inherited inputs are necessary and sufficient to evoke tuned responses, however local circuits change peak output. Push–pull driving inhibition and excitation create net excitatory drive to intensity-variant neurons and tune neurons to intensity. Our results reveal that dynamic range and tuning re-emerge in the auditory midbrain through local circuits that are themselves variable or tuned.

Layer 4 in primary visual cortex of the awake rabbit: contrasting properties of simple cells and putative feedforward inhibitory interneurons

Extracellular recordings were obtained from two cell classes in layer 4 of the awake rabbit primary visual cortex (V1): putative inhibitory interneurons [suspected inhibitory interneurons (SINs)] and putative excitatory cells with simple receptive fields. SINs were identified solely by their characteristic response to electrical stimulation of the lateral geniculate nucleus (LGN, 3+ spikes at >600 Hz), and simple cells were identified solely by receptive field structure, requiring spatially separate ON and/or OFF subfields. Notably, no cells met both criteria, and we studied 62 simple cells and 33 SINs. Fourteen cells met neither criterion. These layer 4 populations were markedly distinct. Thus, SINs were far less linear (F1/F0 < 1), more broadly tuned to stimulus orientation, direction, spatial and temporal frequency, more sensitive to contrast, had much higher spontaneous and stimulus-driven activity, and always had spatially overlapping ON/OFF receptive subfields. SINs responded to drifting gratings with increased firing rates (F0) for all orientations and directions. However, some SINs showed a weaker modulated (F1) response sharply tuned to orientation and/or direction. SINs responded at shorter latencies than simple cells to stationary stimuli, and the responses of both populations could be sustained or transient. Transient simple cells were more sensitive to contrast than sustained simple cells and their visual responses were more frequently suppressed by high contrasts. Finally, cross-correlation between LGN and SIN spike trains confirmed a fast and precisely timed monosynaptic connectivity, supporting the notion that SINs are well suited to provide a fast feedforward inhibition onto targeted cortical populations.

Visual nonclassical receptive field effects emerge from sparse coding in a dynamical system

Extensive electrophysiology studies have shown that many V1 simple cells have nonlinear response properties to stimuli within their classical receptive field (CRF) and receive contextual influence from stimuli outside the CRF modulating the cell's response. Models seeking to explain these non-classical receptive field (nCRF) effects in terms of circuit mechanisms, input-output descriptions, or individual visual tasks provide limited insight into the functional significance of these response properties, because they do not connect the full range of nCRF effects to optimal sensory coding strategies. The (population) sparse coding hypothesis conjectures an optimal sensory coding approach where a neural population uses as few active units as possible to represent a stimulus. We demonstrate that a wide variety of nCRF effects are emergent properties of a single sparse coding model implemented in a neurally plausible network structure (requiring no parameter tuning to produce different effects). Specifically, we replicate a wide variety of nCRF electrophysiology experiments (e.g., end-stopping, surround suppression, contrast invariance of orientation tuning, cross-orientation suppression, etc.) on a dynamical system implementing sparse coding, showing that this model produces individual units that reproduce the canonical nCRF effects. Furthermore, when the population diversity of an nCRF effect has also been reported in the literature, we show that this model produces many of the same population characteristics. These results show that the sparse coding hypothesis, when coupled with a biophysically plausible implementation, can provide a unified high-level functional interpretation to many response properties that have generally been viewed through distinct mechanistic or phenomenological models.

Simple cells in the primary visual cortex (V1) demonstrate many response properties that are either nonlinear or involve response modulations (i.e., stimuli that do not cause a response in isolation alter the cell's response to other stimuli). These non-classical receptive field (nCRF) effects are generally modeled individually and their collective role in biological vision is not well understood. Previous work has shown that classical receptive field (CRF) properties of V1 cells (i.e., the spatial structure of the visual field responsive to stimuli) could be explained by the sparse coding hypothesis, which is an optimal coding model that conjectures a neural population should use the fewest number of cells simultaneously to represent each stimulus. In this paper, we have performed extensive simulated physiology experiments to show that many nCRF response properties are simply emergent effects of a dynamical system implementing this same sparse coding model. These results suggest that rather than representing disparate information processing operations themselves, these nCRF effects could be consequences of an optimal sensory coding strategy that attempts to represent each stimulus most efficiently. This interpretation provides a potentially unifying high-level functional interpretation to many response properties that have generally been viewed through distinct models.

On the relation between receptive field structure and stimulus selectivity in the tree shrew primary visual cortex

There are notable differences in functional properties of primary visual cortex (V1) neurons among mammalian species, particularly those concerning the occurrence of simple and complex cells and the generation of orientation selectivity. Here, we present quantitative data on receptive field (RF) structure, response modulation, and orientation tuning for single neurons in V1 of the tree shrew, a close relative of primates. We find that spatial RF subfield segregation, a criterion for identifying simple cells, was exceedingly small in the tree shrew V1. In contrast, many neurons exhibited elevated F1/F0 modulation that is often used as a simple cell marker. This apparent discrepancy can be explained by the robust stimulus polarity preference in tree shrew V1, which inflates F1/F0 ratio values. RF structure mapped with sparse-noise-which is spatially restricted and emphasizes thalamo-cortical feed-forward inputs-appeared unrelated to orientation selectivity. However, RF structure mapped using the Hartley subspace stimulus-which covers a large area of the visual field and recruits considerable intracortical processing-did predict orientation preference. Our findings reveal a number of striking similarities in V1 functional organization between tree shrews and primates, emphasizing the important role of intracortical recurrent processing in shaping V1 response properties in these species.

Diverse visual features encoded in mouse lateral geniculate nucleus

The thalamus is crucial in determining the sensory information conveyed to cortex. In the visual system, the thalamic lateral geniculate nucleus (LGN) is generally thought to encode simple center-surround receptive fields, which are combined into more sophisticated features in cortex, such as orientation and direction selectivity. However, recent evidence suggests that a more diverse set of retinal ganglion cells projects to the LGN. We therefore used multisite extracellular recordings to define the repertoire of visual features represented in the LGN of mouse, an emerging model for visual processing. In addition to center-surround cells, we discovered a substantial population with more selective coding properties, including direction and orientation selectivity, as well as neurons that signal absence of contrast in a visual scene. The direction and orientation selective neurons were enriched in regions that match the termination zones of direction selective ganglion cells from the retina, suggesting a source for their tuning. Together, these data demonstrate that the mouse LGN contains a far more elaborate representation of the visual scene than current models posit. These findings should therefore have a significant impact on our understanding of the computations performed in mouse visual cortex.

Analogues of simple and complex cells in rhesus monkey auditory cortex

Receptive fields (RFs) of neurons in primary visual cortex have traditionally been subdivided into two major classes: "simple" and "complex" cells. Simple cells were originally defined by the existence of segregated subregions within their RF that respond to either the on- or offset of a light bar and by spatial summation within each of these regions, whereas complex cells had ON and OFF regions that were coextensive in space [Hubel DH, et al. (1962) J Physiol 160:106-154]. Although other definitions based on the linearity of response modulation have been proposed later [Movshon JA, et al. (1978) J Physiol 283:53-77; Skottun BC, et al. (1991) Vision Res 31(7-8):1079-1086], the segregation of ON and OFF subregions has remained an important criterion for the distinction between simple and complex cells. Here we report that response profiles of neurons in primary auditory cortex of monkeys show a similar distinction: one group of cells has segregated ON and OFF subregions in frequency space; and another group shows ON and OFF responses within largely overlapping response profiles. This observation is intriguing for two reasons: (i) spectrotemporal dissociation in the auditory domain provides a basic neural mechanism for the segregation of sounds, a fundamental prerequisite for auditory figure-ground discrimination; and (ii) the existence of similar types of RF organization in visual and auditory cortex would support the existence of a common canonical processing algorithm within cortical columns.

Neurons in the thalamic reticular nucleus are selective for diverse and complex visual features

All visual signals the cortex receives are influenced by the perigeniculate sector (PGN) of the thalamic reticular nucleus, which receives input from relay cells in the lateral geniculate and provides feedback inhibition in return. Relay cells have been studied in quantitative depth; they behave in a roughly linear fashion and have receptive fields with a stereotyped center-surround structure. We know far less about reticular neurons. Qualitative studies indicate they simply pool ascending input to generate non-selective gain control. Yet the perigeniculate is complicated; local cells are densely interconnected and fire lengthy bursts. Thus, we employed quantitative methods to explore the perigeniculate using relay cells as controls. By adapting methods of spike-triggered averaging and covariance analysis for bursts, we identified both first and second order features that build reticular receptive fields. The shapes of these spatiotemporal subunits varied widely; no stereotyped pattern emerged. Companion experiments showed that the shape of the first but not second order features could be explained by the overlap of On and Off inputs to a given cell. Moreover, we assessed the predictive power of the receptive field and how much information each component subunit conveyed. Linear-non-linear (LN) models including multiple subunits performed better than those made with just one; further each subunit encoded different visual information. Model performance for reticular cells was always lesser than for relay cells, however, indicating that reticular cells process inputs non-linearly. All told, our results suggest that the perigeniculate encodes diverse visual features to selectively modulate activity transmitted downstream.

Long-term modification of cortical synapses improves sensory perception

Synapses and receptive fields of the cerebral cortex are plastic. However, changes to specific inputs must be coordinated within neural networks to ensure that excitability and feature selectivity are appropriately configured for perception of the sensory environment. Long-lasting enhancements and decrements to rat primary auditory cortical excitatory synaptic strength were induced by pairing acoustic stimuli with activation of the nucleus basalis neuromodulatory system. Here we report that these synaptic modifications were approximately balanced across individual receptive fields, conserving mean excitation while reducing overall response variability. Decreased response variability should increase detection and recognition of near-threshold or previously imperceptible stimuli, as we found in behaving animals. Thus, modification of cortical inputs leads to wide-scale synaptic changes, which are related to improved sensory perception and enhanced behavioral performance.

Neuronal representation of visual motion and orientation in the fly medulla

In insects, the first extraction of motion and direction clues from local brightness modulations is thought to take place in the medulla. However, whether and how these computations are represented in the medulla stills remain widely unknown, because electrical recording of the neurons in the medulla is difficult. As an effort to overcome this difficulty, we employed local electroporation in vivo in the medulla of the blowfly (Calliphora vicina) to stain small ensembles of neurons with a calcium-sensitive dye. We studied the responses of these neuronal ensembles to spatial and temporal brightness modulations and found selectivity for grating orientation. In contrast, the responses to the two opposite directions of motion of a grating with the same orientation were similar in magnitude, indicating that strong directional selectivity is either not present in the types of neurons covered by our data set, or that direction-selective signals are too closely spaced to be distinguished by our calcium imaging. The calcium responses also showed a bell-shaped dependency on the temporal frequency of drifting gratings, with an optimum higher than that observed in one of the subsequent processing stages, i.e., the lobula plate. Medulla responses were elicited by on- as well as off-stimuli with some spatial heterogeneity in the sensitivity for “on” and “off”, and in the polarity of the responses. Medulla neurons thus show similarities to some established principles of motion and edge detection in the vertebrate visual system.

Functional and laminar dissociations between muscarinic and nicotinic cholinergic neuromodulation in the tree shrew primary visual cortex

Acetylcholine is an important neuromodulator involved in cognitive function. The impact of cholinergic neuromodulation on computations within the cortical microcircuit is not well understood. Here we investigate the effects of layer-specific cholinergic drug application in the tree shrew primary visual cortex during visual stimulation with drifting grating stimuli of varying contrast and orientation. We describe differences between muscarinic and nicotinic cholinergic effects in terms of both the layer of cortex and the attribute of visual representation. Nicotinic receptor activation enhanced the contrast response in the granular input layer of the cortex, while tending to reduce neural selectivity for orientation across all cortical layers. Muscarinic activation modestly enhanced the contrast response across cortical layers, and tended to improve orientation tuning. This resulted in highest orientation selectivity in the supra- and infragranular layers, where orientation selectivity was already greatest in the absence of pharmacological stimulation. Our results indicate that laminar position plays a crucial part in functional consequences of cholinergic stimulation, consistent with the differential distribution of cholinergic receptors. Nicotinic receptors function to enhance sensory representations arriving in the cortex, whereas muscarinic receptors act to boost the cortical computation of orientation tuning. Our findings suggest close homology between cholinergic mechanisms in tree shrew and primate visual cortices.

Generalized spin models for coupled cortical feature maps obtained by coarse graining correlation based synaptic learning rules

We derive generalized spin models for the development of feedforward cortical architecture from a Hebbian synaptic learning rule in a two layer neural network with nonlinear weight constraints. Our model takes into account the effects of lateral interactions in visual cortex combining local excitation and long range effective inhibition. Our approach allows the principled derivation of developmental rules for low-dimensional feature maps, starting from high-dimensional synaptic learning rules. We incorporate the effects of smooth nonlinear constraints on net synaptic weight projected from units in the thalamic layer (the fan-out) and on the net synaptic weight received by units in the cortical layer (the fan-in). These constraints naturally couple together multiple feature maps such as orientation preference and retinotopic organization. We give a detailed illustration of the method applied to the development of the orientation preference map as a special case, in addition to deriving a model for joint pattern formation in cortical maps of orientation preference, retinotopic location, and receptive field width. We show that the combination of Hebbian learning and center-surround cortical interaction naturally leads to an orientation map development model that is closely related to the XY magnetic lattice model from statistical physics. The results presented here provide justification for phenomenological models studied in Cowan and Friedman (Advances in neural information processing systems 3, 1991), Thomas and Cowan (Phys Rev Lett 92(18):e188101, 2004) and provide a developmental model realizing the synaptic weight constraints previously assumed in Thomas and Cowan (Math Med Biol 23(2):119-138, 2006).

Distinct functions for direct and transthalamic corticocortical connections

Essentially all cortical areas receive thalamic inputs and send outputs to lower motor centers. Cortical areas communicate with each other by means of direct corticocortical and corticothalamocortical pathways, often organized in parallel. We distinguish these functionally, stressing that the transthalamic pathways are class 1 (formerly known as “driver”) pathways capable of transmitting information, whereas the direct pathways vary, being either class 2 (formerly known as “modulator”) or class 1. The transthalamic pathways provide a thalamic gate that can be open or closed (and otherwise more subtly modulated), and these inputs to the thalamus are generally branches of axons with motor functions. Thus the transthalamic corticocortical pathways that can be gated carry information about the cortical processing in one cortical area and also about the motor instructions currently being issued from that area and copied to other cortical areas.

Inhibitory circuits for visual processing in thalamus

Synapses made by local interneurons dominate the intrinsic circuitry of the mammalian visual thalamus and influence all signals traveling from the eye to cortex. Here we draw on physiological and computational analyses of receptive fields in the cat's lateral geniculate nucleus to describe how inhibition helps to enhance selectivity for stimulus features in space and time and to improve the efficiency of the neural code. Further, we explore specialized synaptic attributes of relay cells and interneurons and discuss how these might be adapted to preserve the temporal precision of retinal spike trains and thereby maximize the rate of information transmitted downstream.

Synaptic properties of connections between the primary and secondary auditory cortices in mice

Little is known regarding the synaptic properties of corticocortical connections from one cortical area to another. To expand on this knowledge, we assessed the synaptic properties of excitatory projections from the primary to secondary auditory cortex and vice versa. We identified 2 types of postsynaptic responses. The first class of responses have larger initial excitatory postsynaptic potentials (EPSPs), exhibit paired-pulse depression, are limited to ionotropic glutamate receptor activation, and have larger synaptic terminals; the second has smaller initial EPSPs, paired-pulse facilitation, metabotropic glutamate receptor activation, and smaller synaptic terminals. These responses are similar to the driver and modulator properties previously identified for thalamic and thalamocortical circuitry, suggesting that the same classification may extend to corticocortical inputs and have an implication for the functional organization of corticocortical circuits.

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence

Spike-timing-dependent plasticity (STDP) determines the evolution of the synaptic weights according to their pre- and post-synaptic activity, which in turn changes the neuronal activity on a (much) slower time scale. This paper examines the effect of STDP in a recurrently connected network stimulated by external pools of input spike trains, where both input and recurrent synapses are plastic. Our previously developed theoretical framework is extended to incorporate weight-dependent STDP and dendritic delays. The weight dynamics is determined by an interplay between the neuronal activation mechanisms, the input spike-time correlations, and the learning parameters. For the case of two external input pools, the resulting learning scheme can exhibit a symmetry breaking of the input connections such that two neuronal groups emerge, each specialized to one input pool only. In addition, we show how the recurrent connections within each neuronal group can be strengthened by STDP at the expense of those between the two groups. This neuronal self-organization can be seen as a basic dynamical ingredient for the emergence of neuronal maps induced by activity-dependent plasticity.

Modulation of firing rate by background synaptic noise statistics in rat visual cortical neurons

It has been shown previously that background synaptic noise modulates the response gain of neocortical neurons. However, the role of the statistical properties of the noise in modulating firing rate is not known. Here, the dependence of firing rate on the statistical properties of the excitatory to inhibitory balance (EI) in cortical pyramidal neurons was studied. Excitatory glutamatergic and inhibitory GABAergic synaptic conductances were simulated as two stochastic processes and injected into individual neurons in vitro through use of the dynamic-clamp system. Response gain was significantly modulated as a function of the statistical interactions between excitatory and inhibitory synaptic conductances. Firing rates were compared for noisy synaptic conductance steps by varying either the EI correlation or the relative delay between correlated E and I. When inhibitory synaptic conductances exhibited a short temporal delay (5 ms) relative to correlated excitatory synaptic conductances, the response gain was increased compared with noise with no temporal delay but with an equivalent degree of correlation. The dependence of neuronal firing rate on the EI delay of the noisy background synaptic conductance suggests that individual excitatory pyramidal neurons are sensitive to the EI balance of the synaptic conductance. Therefore the statistical EI interactions encoded within the synaptic subthreshold membrane fluctuations are able to modulate neuronal firing properties.

Drivers and modulators in the central auditory pathways

The classic view of auditory information flow depicts a simple serial route from the periphery through tonotopically-organized nuclei in the brainstem, midbrain and thalamus, ascending eventually to the neocortex. Yet, complicating this picture are numerous parallel ascending and descending pathways, whose roles in auditory processing are poorly defined. To address this ambiguity, we have identified several anatomical and physiological properties that distinguish the auditory glutamatergic pathways into two groups that we have termed “drivers” and “modulators”. Driver pathways are associated with information-bearing pathways, while modulator pathways modify these principal information streams. These properties illuminate the potential roles of some previously ill-defined auditory pathways, and may be extended further to categorize either unknown or mischaracterized pathways throughout the auditory system.

Intervening inhibition underlies simple-cell receptive field structure in visual cortex

Synaptic inputs underlying spike receptive fields (RFs) are key to understanding mechanisms for neuronal processing. Here, whole-cell voltage-clamp recordings from neurons in mouse primary visual cortex revealed the spatial patterns of their excitatory and inhibitory synaptic inputs evoked by On and Off stimuli. Surprisingly, neurons with either segregated or overlapped On/Off spike subfields exhibited substantial overlaps between all the four synaptic subfields. The segregated RF structures are generated by the integration of excitation and inhibition with a stereotypic pattern: the peaks of excitatory On/Off subfields are separated and flank co-localized peaks of inhibitory On/Off subfields. The small mismatch of excitation/inhibition leads to an asymmetric inhibitory shaping of On/Off spatial tunings, resulting in a great enhancement of their distinctiveness. Thus, slightly separated On/Off excitation together with intervening inhibition can create simple-cell RF structure, and the dichotomy of RF structures may arise from a fine-tuning of the spatial arrangement of synaptic inputs.

Selective targeting of the dendrites of corticothalamic cells by thalamic afferents in area 17 of the cat

Pyramidal cells of layer 6 in cat visual cortex are the source of the corticothalamic projection, and their recurrent collaterals provide substantially more excitatory synapses in layer 4 than does the thalamic input. They have predominantly simple receptive fields and can be driven monosynaptically by electrically stimulating thalamic relay cells. Layer 6 cells could thus provide a significant disynaptic amplification of the thalamic input to layer 4, particularly since their synapses facilitate, unlike the thalamic afferents whose synapses depress. However, purely geometric considerations of the relation of their dendritic trees to the thalamic input indicate that they should form a far smaller number of synapses with thalamic afferents than do the simple cells of layer 4. We thus analyzed quantitatively the thalamic input to identified corticothalamic cells by labeling the thalamic afferents and corticothalamic cells in vivo. We made a correlated light and electron microscopic study of 73 "contacts" between thalamic afferents and five corticothalamic cells. The electron microscope revealed that only 24 of the contacts identified at light microscope level were indeed synapses and, contrary to geometric predictions, virtually all were located on spines on the basal dendrites. Our quantitative estimates indicate that the corticothalamic cells form even fewer synapses with the thalamic afferents than predicted by geometric considerations and only 1/10 as many as do the layer 4 simple cells. These data strongly suggest it is the collective computation of cortical neurons, not the monosynaptic thalamic input, that determines the output of the corticothalamic cells.

Visual receptive field structure of cortical inhibitory neurons revealed by two-photon imaging guided recording

Synaptic inhibition plays an important role in shaping receptive field (RF) properties in the visual cortex. However, the underlying mechanisms remain not well understood, partly because of difficulties in systematically studying functional properties of cortical inhibitory neurons in vivo. Here, we established two-photon imaging guided cell-attached recordings from genetically labeled inhibitory neurons and nearby "shadowed" excitatory neurons in the primary visual cortex of adult mice. Our results revealed that in layer 2/3, the majority of excitatory neurons exhibited both On and Off spike subfields, with their spatial arrangement varying from being completely segregated to overlapped. In contrast, most layer 4 excitatory neurons exhibited only one discernable subfield. Interestingly, no RF structure with significantly segregated On and Off subfields was observed for layer 2/3 inhibitory neurons of either the fast-spike or regular-spike type. They predominantly possessed overlapped On and Off subfields with a significantly larger size than the excitatory neurons and exhibited much weaker orientation tuning. These results from the mouse visual cortex suggest that different from the push-pull model proposed for simple cells, layer 2/3 simple-type neurons with segregated spike On and Off subfields likely receive spatially overlapped inhibitory On and Off inputs. We propose that the phase-insensitive inhibition can enhance the spatial distinctiveness of On and Off subfields through a gain control mechanism.

Unraveling the principles of auditory cortical processing: can we learn from the visual system?

Studies of auditory cortex are often driven by the assumption, derived from our better understanding of visual cortex, that basic physical properties of sounds are represented there before being used by higher-level areas for determining sound-source identity and location. However, we only have a limited appreciation of what the cortex adds to the extensive subcortical processing of auditory information, which can account for many perceptual abilities. This is partly because of the approaches that have dominated the study of auditory cortical processing to date, and future progress will unquestionably profit from the adoption of methods that have provided valuable insights into the neural basis of visual perception. At the same time, we propose that there are unique operating principles employed by the auditory cortex that relate largely to the simultaneous and sequential processing of previously derived features and that therefore need to be studied and understood in their own right.

Modulator property of the intrinsic cortical projection from layer 6 to layer 4

Layer 4 of the sensory neocortex receives widespread convergent inputs from thalamic, intracortical, and corticocortical sources. Yet, the relative information bearing roles for most of these pathways remain largely undefined. Here we show that the intracortical projections from layer 6 to layer 4 exhibit a physiological property that is consistent with a modulator role. Using in vitro slice preparations of the auditory and somatosensory cortices, we found that electrical stimulation or photostimulation of layer 6 elicits a prolonged depolarizing response that is attributable to the activation of group 1 metabotropic glutamate receptors. These results complement the known physiological properties of the layer 6 to layer 4 pathway, and further suggest that this pathway is not a principle conduit for information flow, but rather acts as a modulator of cortical activity.

The normalization model of attention

Attention has been found to have a wide variety of effects on the responses of neurons in visual cortex. We describe a model of attention that exhibits each of these different forms of attentional modulation, depending on the stimulus conditions and the spread (or selectivity) of the attention field in the model. The model helps reconcile proposals that have been taken to represent alternative theories of attention. We argue that the variety and complexity of the results reported in the literature emerge from the variety of empirical protocols that were used, such that the results observed in any one experiment depended on the stimulus conditions and the subject's attentional strategy, a notion that we define precisely in terms of the attention field in the model, but that has not typically been completely under experimental control.

Glutamatergic inhibition in sensory neocortex

In the mammalian brain, glutamate and gamma-aminobutyric acid are considered major excitatory and inhibitory neurotransmitters, respectively. However, we have found evidence that glutamate can also act as a postsynaptic inhibitory neurotransmitter in layer 4 of the neocortex. Using whole-cell recordings from layer 4 neurons in slice preparations from the mouse visual, auditory, and somatosensory cortices, we found that metabotropic glutamate receptor (mGluR) agonists (ACPD, APDC, and DCG IV) elicit a robust, long-lasting hyperpolarization that is abolished by the group II mGluR antagonist, MCCG. This response largely involves a K(+) conductance mediated by G-protein activity and GIRK channels. Furthermore, electrical and photostimulation of the intracortical inputs to layer 4 elicits a similar hyperpolarization that is blocked by group II mGluR antagonists. This novel inhibition mediated by group II mGluRs may be an unappreciated mechanism for refining cortical receptive fields in layer 4 and may enable synaptic gain control during periods of high activity.

A sparse generative model of V1 simple cells with intrinsic plasticity

Current models for learning feature detectors work on two timescales: on a fast timescale, the internal neurons' activations adapt to the current stimulus; on a slow timescale, the weights adapt to the statistics of the set of stimuli. Here we explore the adaptation of a neuron's intrinsic excitability, termed intrinsic plasticity, which occurs on a separate timescale. Here, a neuron maintains homeostasis of an exponentially distributed firing rate in a dynamic environment. We exploit this in the context of a generative model to impose sparse coding. With natural image input, localized edge detectors emerge as models of V1 simple cells. An intermediate timescale for the intrinsic plasticity parameters allows modeling aftereffects. In the tilt aftereffect, after a viewer adapts to a grid of a certain orientation, grids of a nearby orientation will be perceived as tilted away from the adapted orientation. Our results show that adapting the neurons' gain-parameter but not the threshold-parameter accounts for this effect. It occurs because neurons coding for the adapting stimulus attenuate their gain, while others increase it. Despite its simplicity and low maintenance, the intrinsic plasticity model accounts for more experimental details than previous models without this mechanism.

Inhibition, spike threshold, and stimulus selectivity in primary visual cortex

Ever since Hubel and Wiesel described orientation selectivity in the visual cortex, the question of how precise selectivity emerges has been marked by considerable debate. There are essentially two views of how selectivity arises. Feed-forward models rely entirely on the organization of thalamocortical inputs. Feedback models rely on lateral inhibition to refine selectivity relative to a weak bias provided by thalamocortical inputs. The debate is driven by two divergent lines of evidence. On the one hand, many response properties appear to require lateral inhibition, including precise orientation and direction selectivity and crossorientation suppression. On the other hand, intracellular recordings have failed to find consistent evidence for lateral inhibition. Here we demonstrate a resolution to this paradox. Feed-forward models incorporating the intrinsic nonlinear properties of cortical neurons and feed-forward circuits (i.e., spike threshold, contrast saturation, and spike-rate rectification) can account for properties that have previously appeared to require lateral inhibition.

Nearly instantaneous brightness induction

Brightness induction is the modulation of the perceived intensity of a region by the luminance of surrounding regions and reveals fundamental properties of neural organization in the visual system. Grating induction affords a unique opportunity to precisely measure the temporal properties of induction using a quadrature motion technique. Contrary to previous reports that induction is a sluggish process with temporal frequency cutoffs of 2-5 Hz (R. L. DeValois, M. A. Webster, K. K. DeValois, & B. Lingelbach, 1986; A. F. Rossi & M. A. Paradiso, 1996), we find that induction is nearly instantaneous. The temporal response of induced brightness differs from that of luminance gratings by a small time lag (<1 ms), or by a small temporal phase lag (<0.016 cycle), and remains relatively constant across wide variations in test field height. These data are not easily explained by an edge-dependent, homogeneous filling-in process (A. F. Rossi & M. A. Paradiso, 1996); however, they are consistent with an explanation of brightness induction based on spatial filtering by cortical simple cells (B. Blakeslee & M. E. McCourt, 1999).