Recoding of sensory information across the retinothalamic synapse

Local interneurons in the murine visual thalamus have diverse receptive fields and can provide feature selective inhibition to relay cells

By influencing the type and quality of information that relay cells transmit, local interneurons in thalamus have a powerful impact on cortex. To define the sensory features that these inhibitory neurons encode, we mapped receptive fields of optogenetically identified cells in the murine dorsolateral geniculate nucleus. Although few in number, local interneurons had diverse types of receptive fields, like their counterpart relay cells. This result differs markedly from visual cortex, where inhibitory cells are typically less selective than excitatory cells. To explore how thalamic interneurons might converge on relay cells, we took a computational approach. Using an evolutionary algorithm to search through a library of interneuron models generated from our results, we show that aggregated output from different groups of local interneurons can simulate the inhibitory component of the relay cell's receptive field. Thus, our work provides proof-of-concept that groups of diverse interneurons can supply feature-specific inhibition to relay cells.

Image Sharpness and Contrast Tuning in the Early Visual Pathway

The center-surround organization of the receptive fields (RFs) of retinal ganglion cells highlights the presence of local contrast in visual stimuli. As RF of thalamic relay cells follow the same basic functional organization, it is often assumed that they contribute very little to alter the retinal output. However, in many species, thalamic relay cells largely outnumber their retinal inputs, which diverge to contact simultaneously several units at thalamic level. This gain in cell population as well as retinothalamic convergence opens the door to question how information about contrast is transformed at the thalamic stage. Here, we address this question using a realistic dynamic model of the retinothalamic circuit. Our results show that different components of the thalamic RF might implement filters that are analogous to two types of well-known image processing techniques to preserve the quality of a higher resolution version of the image on its way to the primary visual cortex.

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.

An evolving view of retinogeniculate transmission

The thalamocortical (TC) relay neuron of the dorsoLateral Geniculate Nucleus (dLGN) has borne its imprecise label for many decades in spite of strong evidence that its role in visual processing transcends the implied simplicity of the term "relay". The retinogeniculate synapse is the site of communication between a retinal ganglion cell and a TC neuron of the dLGN. Activation of retinal fibers in the optic tract causes reliable, rapid, and robust postsynaptic potentials that drive postsynaptics spikes in a TC neuron. Cortical and subcortical modulatory systems have been known for decades to regulate retinogeniculate transmission. The dynamic properties that the retinogeniculate synapse itself exhibits during and after developmental refinement further enrich the role of the dLGN in the transmission of the retinal signal. Here we consider the structural and functional substrates for retinogeniculate synaptic transmission and plasticity, and reflect on how the complexity of the retinogeniculate synapse imparts a novel dynamic and influential capacity to subcortical processing of visual information.

Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells

Visual processing depends on specific computations implemented by complex neural circuits. Here, we present a circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast. To localize the sources of such processing, we used recordings at the levels of synaptic input and spiking output in the in vitro mouse retina. We found that an ON-Alpha ganglion cell's excitatory synaptic inputs were described by a divisive interaction between excitation and delayed suppression, which explained nonlinear processing that was already present in ganglion cell inputs. Ganglion cell output was further shaped by spike generation mechanisms. The full model accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaptation of the spike train. These results demonstrate how circuit and cell-intrinsic mechanisms interact for ganglion cell function and, more generally, illustrate the power of circuit-inspired modeling of sensory processing.

Nonlinear computations shaping temporal processing of precortical vision

Computations performed by the visual pathway are constructed by neural circuits distributed over multiple stages of processing, and thus it is challenging to determine how different stages contribute on the basis of recordings from single areas. In the current article, we address this problem in the lateral geniculate nucleus (LGN), using experiments combined with nonlinear modeling capable of isolating various circuit contributions. We recorded cat LGN neurons presented with temporally modulated spots of various sizes, which drove temporally precise LGN responses. We utilized simultaneously recorded S-potentials, corresponding to the primary retinal ganglion cell (RGC) input to each LGN cell, to distinguish the computations underlying temporal precision in the retina from those in the LGN. Nonlinear models with excitatory and delayed suppressive terms were sufficient to explain temporal precision in the LGN, and we found that models of the S-potentials were nearly identical, although with a lower threshold. To determine whether additional influences shaped the response at the level of the LGN, we extended this model to use the S-potential input in combination with stimulus-driven terms to predict the LGN response. We found that the S-potential input "explained away" the major excitatory and delayed suppressive terms responsible for temporal patterning of LGN spike trains but revealed additional contributions, largely PULL suppression, to the LGN response. Using this novel combination of recordings and modeling, we were thus able to dissect multiple circuit contributions to LGN temporal responses across retina and LGN, and set the foundation for targeted study of each stage.

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.

Energy-Efficient Information Transfer by Visual Pathway Synapses

The architecture of computational devices is shaped by their energy consumption. Energetic constraints are used to design silicon-based computers but are poorly understood for neural computation. In the brain, most energy is used to reverse ion influxes generating excitatory postsynaptic currents (EPSCs) and action potentials. Thus, EPSCs should be small to minimize energy use, but not so small as to impair information transmission. We quantified information flow through the retinothalamic synapse in the visual pathway in brain slices, with cortical and inhibitory input to the postsynaptic cell blocked. Altering EPSC size with dynamic clamp, we found that a larger-than-normal EPSC increased information flow through the synapse. Thus, the evolutionarily selected EPSC size does not maximize retinal information flow to the cortex. By assessing the energy used on postsynaptic ion pumping and action potentials, we show that, instead, the EPSC size optimizes the ratio of retinal information transmitted to energy consumed. These data suggest maximization of information transmission per energy used as a synaptic design principle.

Visual Functions of the Thalamus

The thalamus is the heavily interconnected partner of the neocortex. All areas of the neocortex receive afferent input from and send efferent projections to specific thalamic nuclei. Through these connections, the thalamus serves to provide the cortex with sensory input, and to facilitate interareal cortical communication and motor and cognitive functions. In the visual system, the lateral geniculate nucleus (LGN) of the dorsal thalamus is the gateway through which visual information reaches the cerebral cortex. Visual processing in the LGN includes spatial and temporal influences on visual signals that serve to adjust response gain, transform the temporal structure of retinal activity patterns, and increase the signal-to-noise ratio of the retinal signal while preserving its basic content. This review examines recent advances in our understanding of LGN function and circuit organization and places these findings in a historical context.

The meaning of spikes from the neuron's point of view: predictive homeostasis generates the appearance of randomness

The conventional interpretation of spikes is from the perspective of an external observer with knowledge of a neuron's inputs and outputs who is ignorant of the contents of the "black box" that is the neuron. Here we consider a neuron to be an observer and we interpret spikes from the neuron's perspective. We propose both a descriptive hypothesis based on physics and logic, and a prescriptive hypothesis based on biological optimality. Our descriptive hypothesis is that a neuron's membrane excitability is "known" and the amplitude of a future excitatory postsynaptic conductance (EPSG) is "unknown". Therefore excitability is an expectation of EPSG amplitude and a spike is generated only when EPSG amplitude exceeds its expectation ("prediction error"). Our prescriptive hypothesis is that a diversity of synaptic inputs and voltage-regulated ion channels implement "predictive homeostasis", working to insure that the expectation is accurate. The homeostatic ideal and optimal expectation would be achieved when an EPSP reaches precisely to spike threshold, so that spike output is exquisitely sensitive to small variations in EPSG input. To an external observer who knows neither EPSG amplitude nor membrane excitability, spikes would appear random if the neuron is making accurate predictions. We review experimental evidence that spike probabilities are indeed maintained near an average of 0.5 under natural conditions, and we suggest that the same principles may also explain why synaptic vesicle release appears to be "stochastic". Whereas the present hypothesis accords with principles of efficient coding dating back to Barlow (1961), it contradicts decades of assertions that neural activity is substantially "random" or "noisy". The apparent randomness is by design, and like many other examples of apparent randomness, it corresponds to the ignorance of external macroscopic observers about the detailed inner workings of a microscopic system.

Statistical wiring of thalamic receptive fields optimizes spatial sampling of the retinal image

It is widely assumed that mosaics of retinal ganglion cells establish the optimal representation of visual space. However, relay cells in the visual thalamus often receive convergent input from several retinal afferents and, in cat, outnumber ganglion cells. To explore how the thalamus transforms the retinal image, we built a model of the retinothalamic circuit using experimental data and simple wiring rules. The model shows how the thalamus might form a resampled map of visual space with the potential to facilitate detection of stimulus position in the presence of sensor noise. Bayesian decoding conducted with the model provides support for this scenario. Despite its benefits, however, resampling introduces image blur, thus impairing edge perception. Whole-cell recordings obtained in vivo suggest that this problem is mitigated by arrangements of excitation and inhibition within the receptive field that effectively boost contrast borders, much like strategies used in digital image processing.

Effects of cortical feedback on the spatial properties of relay cells in the lateral geniculate nucleus

Feedback connections to early-level sensory neurons have been shown to affect many characteristics of their neural response. Because selectivity for stimulus size is a fundamental property of visual neurons, we examined the summation tuning and discretely mapped receptive field (RF) properties of cells in the lateral geniculate nucleus (LGN) both with and without feedback from visual cortex. Using extracellular recording in halothane-anesthetized cats, we used small luminance probes displaced in Cartesian coordinates to measure discrete response area, and optimal sinusoidal gratings of varying diameter to estimate preferred optimal summation size and level of center-surround antagonism. In conditions where most cortical feedback was pharmacologically removed, discretely mapped RF response areas showed an overall significant enlargement for the population compared with control conditions. A switch to increased levels of burst firing, spatially displaced from the RF center, suggested this was mediated by changes in excitatory-inhibitory balance across visual space. With the use of coextensive stimulation, there were overall highly significant increases in the optimal summation size and reduction of surround antagonism with removal of cortical feedback in the LGN. When fitted with a difference-of-Gaussian (DOG) model, changes in the center size, center amplitude, and surround amplitude parameters were most significantly related to the removal of cortical feedback. In summary, corticothalamic innervation of the visual thalamus can modify spatial summation properties in LGN relay cells, an effect most parsimoniously explained by changes in the excitatory-inhibitory balance.

Visual orientation and directional selectivity through thalamic synchrony

Thalamic neurons respond to visual scenes by generating synchronous spike trains on the timescale of 10-20 ms that are very effective at driving cortical targets. Here we demonstrate that this synchronous activity contains unexpectedly rich information about fundamental properties of visual stimuli. We report that the occurrence of synchronous firing of cat thalamic cells with highly overlapping receptive fields is strongly sensitive to the orientation and the direction of motion of the visual stimulus. We show that this stimulus selectivity is robust, remaining relatively unchanged under different contrasts and temporal frequencies (stimulus velocities). A computational analysis based on an integrate-and-fire model of the direct thalamic input to a layer 4 cortical cell reveals a strong correlation between the degree of thalamic synchrony and the nonlinear relationship between cortical membrane potential and the resultant firing rate. Together, these findings suggest a novel population code in the synchronous firing of neurons in the early visual pathway that could serve as the substrate for establishing cortical representations of the visual scene.

Temporal precision in the visual pathway through the interplay of excitation and stimulus-driven suppression

Visual neurons can respond with extremely precise temporal patterning to visual stimuli that change on much slower time scales. Here, we investigate how the precise timing of cat thalamic spike trains-which can have timing as precise as 1 ms-is related to the stimulus, in the context of both artificial noise and natural visual stimuli. Using a nonlinear modeling framework applied to extracellular data, we demonstrate that the precise timing of thalamic spike trains can be explained by the interplay between an excitatory input and a delayed suppressive input that resembles inhibition, such that neuronal responses only occur in brief windows where excitation exceeds suppression. The resulting description of thalamic computation resembles earlier models of contrast adaptation, suggesting a more general role for mechanisms of contrast adaptation in visual processing. Thus, we describe a more complex computation underlying thalamic responses to artificial and natural stimuli that has implications for understanding how visual information is represented in the early stages of visual processing.

Cognitive and perceptual functions of the visual thalamus

The thalamus is classically viewed as passively relaying information to the cortex. However, there is growing evidence that the thalamus actively regulates information transmission to the cortex and between cortical areas using a variety of mechanisms, including the modulation of response magnitude, firing mode, and synchrony of neurons according to behavioral demands. We discuss how the visual thalamus contributes to attention, awareness, and visually guided actions, to present a general role for the thalamus in perception and cognition.

Sparse and dense coding of natural stimuli by distinct midbrain neuron subpopulations in weakly electric fish

While peripheral sensory neurons respond to natural stimuli with a broad range of spatiotemporal frequencies, central neurons instead respond sparsely to specific features in general. The nonlinear transformations leading to this emergent selectivity are not well understood. Here we characterized how the neural representation of stimuli changes across successive brain areas, using the electrosensory system of weakly electric fish as a model system. We found that midbrain torus semicircularis (TS) neurons were on average more selective in their responses than hindbrain electrosensory lateral line lobe (ELL) neurons. Further analysis revealed two categories of TS neurons: dense coding TS neurons that were ELL-like and sparse coding TS neurons that displayed selective responses. These neurons in general responded to preferred stimuli with few spikes and were mostly silent for other stimuli. We further investigated whether information about stimulus attributes was contained in the activities of ELL and TS neurons. To do so, we used a spike train metric to quantify how well stimuli could be discriminated based on spiking responses. We found that sparse coding TS neurons performed poorly even when their activities were combined compared with ELL and dense coding TS neurons. In contrast, combining the activities of as few as 12 dense coding TS neurons could lead to optimal discrimination. On the other hand, sparse coding TS neurons were better detectors of whether their preferred stimulus occurred compared with either dense coding TS or ELL neurons. Our results therefore suggest that the TS implements parallel detection and estimation of sensory input.

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.

Information transmission and detection thresholds in the vestibular nuclei: single neurons vs. population encoding

Understanding how sensory neurons transmit information about relevant stimuli remains a major goal in neuroscience. Of particular relevance are the roles of neural variability and spike timing in neural coding. Peripheral vestibular afferents display differential variability that is correlated with the importance of spike timing; regular afferents display little variability and use a timing code to transmit information about sensory input. Irregular afferents, conversely, display greater variability and instead use a rate code. We studied how central neurons within the vestibular nuclei integrate information from both afferent classes by recording from a group of neurons termed vestibular only (VO) that are known to make contributions to vestibulospinal reflexes and project to higher-order centers. We found that, although individual central neurons had sensitivities that were greater than or equal to those of individual afferents, they transmitted less information. In addition, their velocity detection thresholds were significantly greater than those of individual afferents. This is because VO neurons display greater variability, which is detrimental to information transmission and signal detection. Combining activities from multiple VO neurons increased information transmission. However, the information rates were still much lower than those of equivalent afferent populations. Furthermore, combining responses from multiple VO neurons led to lower velocity detection threshold values approaching those measured from behavior (∼ 2.5 vs. 0.5-1°/s). Our results suggest that the detailed time course of vestibular stimuli encoded by afferents is not transmitted by VO neurons. Instead, they suggest that higher vestibular pathways must integrate information from central vestibular neuron populations to give rise to behaviorally observed detection thresholds.