Oscillations and long-lasting correlations in a model of the lateral geniculate nucleus and visual cortex

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.

Extended difference-of-Gaussians model incorporating cortical feedback for relay cells in the lateral geniculate nucleus of cat

A striking feature of the organization of the early visual pathway is the significant feedback from primary visual cortex to cells in the dorsal lateral geniculate nucleus (LGN). Despite numerous experimental and modeling studies, the functional role for this feedback remains elusive. We present a new firing-rate-based model for LGN relay cells in cat, explicitly accounting for thalamocortical loop effects. The established DOG model, here assumed to account for the spatial aspects of the feedforward processing of visual stimuli, is extended to incorporate the influence of thalamocortical loops including a full set of orientation-selective cortical cell populations. Assuming a phase-reversed push-pull arrangement of ON and OFF cortical feedback as seen experimentally, this extended DOG (eDOG) model exhibits linear firing properties despite non-linear firing characteristics of the corticothalamic cells. The spatiotemporal receptive field of the eDOG model has a simple algebraic structure in Fourier space, while the real-space receptive field, as well as responses to visual stimuli, are found by evaluation of an integral. As an example application we use the eDOG model to study effects of cortical feedback on responses to flashing circular spots and patch-grating stimuli and find that the eDOG model can qualitatively account for experimental findings.

A minimal mechanistic model for temporal signal processing in the lateral geniculate nucleus

The receptive fields of cells in the lateral geniculate nucleus (LGN) are shaped by their diverse set of impinging inputs: feedforward synaptic inputs stemming from retina, and feedback inputs stemming from the visual cortex and the thalamic reticular nucleus. To probe the possible roles of these feedforward and feedback inputs in shaping the temporal receptive-field structure of LGN relay cells, we here present and investigate a minimal mechanistic firing-rate model tailored to elucidate their disparate features. The model for LGN relay ON cells includes feedforward excitation and inhibition (via interneurons) from retinal ON cells and excitatory and inhibitory (via thalamic reticular nucleus cells and interneurons) feedback from cortical ON and OFF cells. From a general firing-rate model formulated in terms of Volterra integral equations, we derive a single delay differential equation with absolute delay governing the dynamics of the system. A freely available and easy-to-use GUI-based MATLAB version of this minimal mechanistic LGN circuit model is provided. We particularly investigate the LGN relay-cell impulse response and find through thorough explorations of the model's parameter space that both purely feedforward models and feedback models with feedforward excitation only, can account quantitatively for previously reported experimental results. We find, however, that the purely feedforward model predicts two impulse response measures, the time to first peak and the biphasic index (measuring the relative weight of the rebound phase) to be anticorrelated. In contrast, the models with feedback predict different correlations between these two measures. This suggests an experimental test assessing the relative importance of feedforward and feedback connections in shaping the impulse response of LGN relay cells.

A multi-compartment model for interneurons in the dorsal lateral geniculate nucleus

GABAergic interneurons (INs) in the dorsal lateral geniculate nucleus (dLGN) shape the information flow from retina to cortex, presumably by controlling the number of visually evoked spikes in geniculate thalamocortical (TC) neurons, and refining their receptive field. The INs exhibit a rich variety of firing patterns: Depolarizing current injections to the soma may induce tonic firing, periodic bursting or an initial burst followed by tonic spiking, sometimes with prominent spike-time adaptation. When released from hyperpolarization, some INs elicit rebound bursts, while others return more passively to the resting potential. A full mechanistic understanding that explains the function of the dLGN on the basis of neuronal morphology, physiology and circuitry is currently lacking. One way to approach such an understanding is by developing a detailed mathematical model of the involved cells and their interactions. Limitations of the previous models for the INs of the dLGN region prevent an accurate representation of the conceptual framework needed to understand the computational properties of this region. We here present a detailed compartmental model of INs using, for the first time, a morphological reconstruction and a set of active dendritic conductances constrained by experimental somatic recordings from INs under several different current-clamp conditions. The model makes a number of experimentally testable predictions about the role of specific mechanisms for the firing properties observed in these neurons. In addition to accounting for the significant features of all experimental traces, it quantitatively reproduces the experimental recordings of the action-potential- firing frequency as a function of injected current. We show how and why relative differences in conductance values, rather than differences in ion channel composition, could account for the distinct differences between the responses observed in two different neurons, suggesting that INs may be individually tuned to optimize network operation under different input conditions.

The role of cortical feedback in the generation of the temporal receptive field responses of lateral geniculate nucleus neurons: a computational modelling study

The influence of cortical feedback on thalamic visual responses has been a source of much discussion in recent years. In this study we examine the possible role of cortical feedback in shaping the spatiotemporal receptive field (STRF) responses of thalamocortical (TC) cells in the lateral geniculate nucleus (LGN) of the thalamus. A population-based computational model of the thalamocortical network is used to generate a representation of the STRF response of LGN TC cells within the corticothalamic feedback circuit. The cortical feedback is shown to have little influence on the spatial response properties of the STRF organization. However, the model suggests that cortical feedback may play a key role in modifying the experimentally observed biphasic temporal response property of the STRF, that is, the reversal over time of the polarity of ON and OFF responses of the centre and surround of the receptive field, in particular accounting for the experimentally observed mismatch between retinal cells and TC cells in respect of the magnitude of the second (rebound) phase of the temporal response. The model results also show that this mismatch may result from an anti-phase corticothalamic feedback mechanism.

Corticothalamic feedback enhances stimulus response precision in the visual system

There is a tightly coupled bidirectional interaction between visual cortex and visual thalamus [lateral geniculate nucleus (LGN)]. Using drifting sinusoidal grating stimuli, we compared the response of cells in the LGN with and without feedback from the visual cortex. Raster plots revealed a striking difference in the response pattern of cells with and without feedback. This difference was reflected in the results from computing vector sum plots and the ratio of zero harmonic to the fundamental harmonic of the fast Fourier transform (FFT) for these responses. The variability of responses assessed by using the Fano factor was also different for the two groups, with the cells without feedback showing higher variability. We examined the covariance of these measures between pairs of simultaneously recorded cells with and without feedback, and they were much more strongly positively correlated with feedback. We constructed orientation tuning curves from the central 5 ms in the raw cross-correlograms of the outputs of pairs of LGN cells, and these curves revealed much sharper tuning with feedback. We discuss the significance of these data for cortical function and suggest that the precision in stimulus-linked firing in the LGN appears as an emergent factor from the corticothalamic interaction.

Always returning: feedback and sensory processing in visual cortex and thalamus

Feedback projections are an integral part of the mammalian visual system. Although it is tempting to relegate them to a subsidiary role in visual processing, because their supposed latency and lag might appear to be unfavourable for an involvement in fast processing, this is a dangerous simplification. Certainly for the world in motion, feedback from higher motion areas can influence the transfer of ascending input when, or even before, the input arrives. Here, we consider the circuit formed by layer 6 feedback cells in the visual cortex and how this straddles the retinothalamic and thalamocortical transfer of visual input. We discuss its links to feedback from the cortical motion area MT (V5), and suggest that motion perception involves a dynamic interplay between MT, V1 and the thalamus. This review is part of the TINS special issue on The Neural Substrates of Cognition.

Synchronous Activity in Cat Visual Cortex Encodes Collinear and Cocircular Contours

We explored how contour information in primary visual cortex might be embedded in the simultaneous activity of multiple cells recorded with a 100-electrode array. Synchronous activity in cat visual cortex was more selective and predictable in discriminating between drifting grating and concentric ring stimuli than changes in firing rate. Synchrony was found even between cells with wholly different orientation preferences when their receptive fields were circularly aligned, and membership in synchronous groups was orientation and curvature dependent. The existence of synchrony between cocircular cells reinforces its role as a general mechanism for contour integration and shape detection as predicted by association field concepts. Our data suggest that cortical synchrony results from common and synchronous input from earlier visual areas and that it could serve to shape extrastriate response selectivity.

The influence of the corticothalamic projection on responses in thalamus and cortex

We review results on the in vivo properties of neurons in the dorsal lateral geniculate nucleus (dLGN) that receives its afferent input from the retina and projects to the visual cortex. In addition, the dLGN receives input from the brain stem and from a rather strong corticothalamic back-projection, which originates in layer 6 of the visual cortex. We compare the behaviour of dLGN cells during spontaneous changes of the frequency contents of the electroencephalograph (EEG) (which are mainly related to a changing brain stem influence), with those that are obtained when experimentally silencing the corticothalamic feedback. The spatial and temporal response properties of dLGN cells are compared during these two conditions, and we report that the neurons behave similarly during a synchronized EEG state and during inactive corticothalamic feedback. In both situations, dLGN cells are rather phasic and their remaining tonic activity is temporally dispersed, indicating a hyperpolarizing effect. By means of a novel method, we were able to chronically eliminate a large proportion of the corticothalamic projection neurons from the otherwise intact cortex. In this condition, we found that cortical cells also lose their EEG specific response differences but, in this instance, probably due to a facilitatory (depolarizing) plasticity reaction of the remaining network.

Spike timing, synchronization and information processing on the sensory side of the central nervous system

To what extent is the variability of the neuronal responses compatible with the use of spike timing for sensory information processing by the central nervous system? In reviewing the state of the art of this question, I first analyze the characteristics of this variability with its three elements: synaptic noise, impact of ongoing activity and possible fluctuations in evoked responses. I then review the recent literature on the various sensory modalities: somato-sensory, olfactory, gustatory and visual and auditory processing. I emphasize that the conditions in which precise timing, at the millisecond level, is usually obtained, are conditions that usually require dynamic stimulation or sharp changes in the stimuli. By contrast, situations in which stimulation not belonging to the temporal domain is temporally encoded lead to much coarser temporal coding; although in both cases, neural networks transmit the signals with similarly high precision. Synchronization among neurons is an important tool in information processing in both cases but again seems to act either at millisecond or tens of millisecond levels. Information theory applied to both situations confirms that the average rate of information transmission is much higher in dynamic than in static situations. These facts suggest that channels of precise temporal encoding may exist in the brain but imply populations of neurons working in a yet to be discovered way.

Neural assemblies: technical issues, analysis, and modeling

Neurons often work together to compute and process information, and neural assemblies arise from synaptic interactions and neural circuits. One way to study neural assemblies is to simultaneously record from several or many neurons and study the statistical relations among their spike trains. From this analysis researchers can try to understand the nature of the assemblies, which can also lead to attempts at modeling the underlying mechanisms. In this review we discuss three important parts of this process: (1) technical issues related to simultaneously recording more than one single unit, (2) ways of analyzing the data and (3) recent models offering hypothetical mechanisms of neural assemblies, especially models which incorporate feedback.