A simple model of retina-LGN transmission

State-dependent modulation of thalamocortical oscillations by gamma light flicker with different frequencies, intensities, and duty cycles

Rhythmic light flickers have emerged as useful tools to modulate cognition and rescue pathological oscillations related to neurological disorders by entrainment. However, a mechanistic understanding of the entrainment for different brain oscillatory states and light flicker parameters is lacking. To address this issue, we proposed a biophysical neural network model for thalamocortical oscillations (TCOs) and explored the stimulation effects depending on the thalamocortical oscillatory states and stimulation parameters (frequency, intensity, and duty cycle) using the proposed model and electrophysiology experiments. The proposed model generated alpha, beta, and gamma oscillatory states (with main oscillation frequences at 9, 25, and 35 Hz, respectively), which were successfully transmitted from the thalamus to the cortex. By applying light flicker stimulation, we found that the entrainment was state-dependent and it was more prone to induce entrainment if the flicker perturbation frequency was closer to the endogenous oscillatory frequency. In addition, endogenous oscillation would be accelerated, whereas low-frequency oscillatory power would be suppressed by gamma (30-50 Hz) flickers. Notably, the effects of intensity and duty cycle on entrainment were complex; a high intensity of light flicker did not mean high entrainment possibility, and duty cycles below 50% could induce entrainment easier than those above 50%. Further, we observed entrainment discontinuity during gamma flicker stimulations with different frequencies, attributable to the non-linear characteristics of the network oscillations. These results provide support for the experimental design and clinical applications of the modulation of TCOs by gamma (30-50 Hz) light flicker.

Morphology, physiology and synaptic connectivity of local interneurons in the mouse somatosensory thalamus

Abstract

The thalamic reticular nucleus (TRN) neurons, projecting across the external medullary lamina, have long been considered to be the only significant source of inhibition of the somatosensory ventral posterior (VP) nuclei of the thalamus. Here we report for the first time effective local inhibition and disinhibition in the VP. Inhibitory interneurons were found in GAD67–GFP‐expressing mice and studied using in vitro multiple patch clamp. Inhibitory interneurons have expansive bipolar or tripolar morphologies, reach across most of the VP nucleus and display low threshold bursting behaviour. They form triadic and non‐triadic synaptic connections onto thalamocortical relay neurons and other interneurons, mediating feedforward inhibition and disinhibition. Synaptic inputs arrive before those expected from the TRN neurons, suggesting that local inhibition plays an early and significant role in the functioning of the somatosensory thalamus.

Key points

The physiology and structure of local interneurons in the mouse somatosensory thalamus is described for the first time.

Inhibitory interneurons have extensive dendritic arborization providing significant local dendro‐dendritic inhibition in the somatosensory thalamus.

Triadic and non‐triadic synaptic connectivity onto thalamic relay neurons and other interneurons provides both local feedforward inhibition and disinhibition.

Interneurons of the somatosensory thalamus provide inhibition before the thalamic reticular nucleus, suggesting they play an important role in sensory perception.

A deep convolutional visual encoding model of neuronal responses in the LGN

The Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

Rodent somatosensory thalamocortical circuitry: Neurons, synapses, and connectivity

As our understanding of the thalamocortical system deepens, the questions we face become more complex. Their investigation requires the adoption of novel experimental approaches complemented with increasingly sophisticated computational modeling. In this review, we take stock of current data and knowledge about the circuitry of the somatosensory thalamocortical loop in rodents, discussing common principles across modalities and species whenever appropriate. We review the different levels of organization, including the cells, synapses, neuroanatomy, and network connectivity. We provide a complete overview of this system that should be accessible for newcomers to this field while nevertheless being comprehensive enough to serve as a reference for seasoned neuroscientists and computational modelers studying the thalamocortical system. We further highlight key gaps in data and knowledge that constitute pressing targets for future experimental work. Filling these gaps would provide invaluable information for systematically unveiling how this system supports behavioral and cognitive processes.

STDP Plasticity in TRN Within Hierarchical Spike Timing Model of Visual Information Processing

We investigated age related synaptic plasticity in thalamic reticular nucleus (TRN) as a part of visual information processing system in the brain. Simulation experiments were performed using a hierarchical spike timing neural network model in NEST simulator. The model consists of multiple layers starting with retinal photoreceptors through thalamic relay, primary visual cortex layers up to the lateral intraparietal cortex (LIP) responsible for decision making and preparation of motor response. All synaptic inter- and intra-layer connections of our model are structured according to the literature information. The present work extends the model with spike timing dependent plastic (STDP) synapses within TRN as well as from visual cortex to LIP area. Synaptic strength changes were forced by teaching signal typical for three different age groups (young, middle and elderly) determined experimentally from eye movement data collected by eye tracking device from human subjects preforming a simplified simulated visual navigation task.

Information processing in the LGN: a comparison of neural codes and cell types

To understand how anatomy and physiology allow an organism to perform its function, it is important to know how information that is transmitted by spikes in the brain is received and encoded. A natural question is whether the spike rate alone encodes the information about a stimulus (rate code), or additional information is contained in the temporal pattern of the spikes (temporal code). Here we address this question using data from the cat Lateral Geniculate Nucleus (LGN), which is the visual portion of the thalamus, through which visual information from the retina is communicated to the visual cortex. We analyzed the responses of LGN neurons to spatially homogeneous spots of various sizes with temporally random luminance modulation. We compared the Firing Rate with the Shannon Information Transmission Rate , which quantifies the information contained in the temporal relationships between spikes. We found that the behavior of these two rates can differ quantitatively. This suggests that the energy used for spiking does not translate directly into the information to be transmitted. We also compared Firing Rates with Information Rates for X-ON and X-OFF cells. We found that, for X-ON cells the Firing Rate and Information Rate often behave in a completely different way, while for X-OFF cells these rates are much more highly correlated. Our results suggest that for X-ON cells a more efficient "temporal code" is employed, while for X-OFF cells a straightforward "rate code" is used, which is more reliable and is correlated with energy consumption.

Spike Timing Neural Model of Motion Perception and Decision Making

The paper presents a hierarchical spike timing neural network model developed in NEST simulator aimed to reproduce human decision making in simplified simulated visual navigation tasks. It includes multiple layers starting from retina photoreceptors and retinal ganglion cells (RGC) via thalamic relay including lateral geniculate nucleus (LGN), thalamic reticular nucleus (TRN), and interneurons (IN) mediating connections to the higher brain areas-visual cortex (V1), middle temporal (MT), and medial superior temporal (MTS) areas, involved in dorsal pathway processing of spatial and dynamic visual information. The last layer-lateral intraparietal cortex (LIP)-is responsible for decision making and organization of the subsequent motor response (saccade generation). We simulated two possible decision options having LIP layer with two sub-regions with mutual inhibitory connections whose increased firing rate corresponds to the perceptual decision about motor response-left or right saccade. Each stage of the model was tested by appropriately chosen stimuli corresponding to its selectivity to specific stimulus characteristics (orientation for V1, direction for MT, and expansion/contraction movement templates for MST, respectively). The overall model performance was tested with stimuli simulating optic flow patterns of forward self-motion on a linear trajectory to the left or to the right from straight ahead with a gaze in the direction of heading.

A Neuronal Network Model of the Primate Visual System: Color Mechanisms in the Retina, LGN and V1

Color plays a key role in human vision but the neural machinery that underlies the transformation from stimulus to perception is not well understood. Here, we implemented a two-dimensional network model of the first stages in the primate parvocellular pathway (retina, lateral geniculate nucleus and layer 4C[Formula: see text] in V1) consisting of conductance-based point neurons. Model parameters were tuned based on physiological and anatomical data from the primate foveal and parafoveal vision, the most relevant visual field areas for color vision. We exhaustively benchmarked the model against well-established chromatic and achromatic visual stimuli, showing spatial and temporal responses of the model to disk- and ring-shaped light flashes, spatially uniform squares and sine-wave gratings of varying spatial frequency. The spatiotemporal patterns of parvocellular cells and cortical cells are consistent with their classification into chromatically single-opponent and double-opponent groups, and nonopponent cells selective for luminance stimuli. The model was implemented in the widely used neural simulation tool NEST and released as open source software. The aim of our modeling is to provide a biologically realistic framework within which a broad range of neuronal interactions can be examined at several different levels, with a focus on understanding how color information is processed.

Mouse dLGN Receives Functional Input from a Diverse Population of Retinal Ganglion Cells with Limited Convergence

Mouse vision is based on the parallel output of more than 30 functional types of retinal ganglion cells (RGCs). Little is known about how representations of visual information change between retina and dorsolateral geniculate nucleus (dLGN) of the thalamus, the main relay between retina and cortex. Here, we functionally characterized responses of retrogradely labeled dLGN-projecting RGCs and dLGN neurons to the same set of visual stimuli. We found that many of the previously identified functional RGC types innervate dLGN, which maintained a high degree of functional diversity. Using a linear model to assess functional connectivity between RGC types and dLGN neurons, we found that responses of dLGN neurons could be predicted as linear combination of inputs from on average five RGC types, but only two of those had the strongest functional impact. Thus, mouse dLGN receives functional input from a diverse population of RGC types with limited convergence.

Firing-rate models for neurons with a broad repertoire of spiking behaviors

Capturing the response behavior of spiking neuron models with rate-based models facilitates the investigation of neuronal networks using powerful methods for rate-based network dynamics. To this end, we investigate the responses of two widely used neuron model types, the Izhikevich and augmented multi-adapative threshold (AMAT) models, to a range of spiking inputs ranging from step responses to natural spike data. We find (i) that linear-nonlinear firing rate models fitted to test data can be used to describe the firing-rate responses of AMAT and Izhikevich spiking neuron models in many cases; (ii) that firing-rate responses are generally too complex to be captured by first-order low-pass filters but require bandpass filters instead; (iii) that linear-nonlinear models capture the response of AMAT models better than of Izhikevich models; (iv) that the wide range of response types evoked by current-injection experiments collapses to few response types when neurons are driven by stationary or sinusoidally modulated Poisson input; and (v) that AMAT and Izhikevich models show different responses to spike input despite identical responses to current injections. Together, these findings suggest that rate-based models of network dynamics may capture a wider range of neuronal response properties by incorporating second-order bandpass filters fitted to responses of spiking model neurons. These models may contribute to bringing rate-based network modeling closer to the reality of biological neuronal networks.

Contrast gain control and retinogeniculate communication

Visual information processed in the retina is transmitted to primary visual cortex via relay cells in the lateral geniculate nucleus (LGN) of the dorsal thalamus. Although retinal ganglion cells are the primary source of driving input to LGN neurons, not all retinal spikes are transmitted to the cortex. Here, we investigate the relationship between stimulus contrast and retinogeniculate communication and test the hypothesis that both the time course and strength of retinogeniculate interactions are dynamic and dependent on stimulus contrast. By simultaneously recording the spiking activity of synaptically connected retinal ganglion cells and LGN neurons in the cat, we show that the temporal window for retinogeniculate integration and the effectiveness of individual retinal spikes are inversely proportional to stimulus contrast. This finding provides a mechanistic understanding for the phenomenon of augmented contrast gain control in the LGN-a nonlinear receptive field property of LGN neurons whereby response gain during low-contrast stimulation is enhanced relative to response gain during high-contrast stimulation. In addition, these results support the view that network interactions beyond the retina play an essential role in transforming visual signals en route from retina to cortex.

Theory of optimal balance predicts and explains the amplitude and decay time of synaptic inhibition

Synaptic inhibition counterbalances excitation, but it is not known what constitutes optimal inhibition. We previously proposed that perfect balance is achieved when the peak of an excitatory postsynaptic potential (EPSP) is exactly at spike threshold, so that the slightest variation in excitation determines whether a spike is generated. Using simulations, we show that the optimal inhibitory postsynaptic conductance (IPSG) increases in amplitude and decay rate as synaptic excitation increases from 1 to 800 Hz. As further proposed by theory, we show that optimal IPSG parameters can be learned through anti-Hebbian rules. Finally, we compare our theoretical optima to published experimental data from 21 types of neurons, in which rates of synaptic excitation and IPSG decay times vary by factors of about 100 (5–600 Hz) and 50 (1–50 ms), respectively. From an infinite range of possible decay times, theory predicted experimental decay times within less than a factor of 2. Across a distinct set of 15 types of neuron recorded in vivo, theory predicted the amplitude of synaptic inhibition within a factor of 1.7. Thus, the theory can explain biophysical quantities from first principles.

Inhibition and excitation are counterbalanced at synapses, but the conditions that constitute optimal balance are not known. Here the authors show through modelling that the properties of synaptic inhibition are fine-tuned to maintain an optimal balance in which peak excitation reaches precisely to spike threshold.

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.

Biophysical Network Modelling of the dLGN Circuit: Different Effects of Triadic and Axonal Inhibition on Visual Responses of Relay Cells

Despite its prominent placement between the retina and primary visual cortex in the early visual pathway, the role of the dorsal lateral geniculate nucleus (dLGN) in molding and regulating the visual signals entering the brain is still poorly understood. A striking feature of the dLGN circuit is that relay cells (RCs) and interneurons (INs) form so-called triadic synapses, where an IN dendritic terminal can be simultaneously postsynaptic to a retinal ganglion cell (GC) input and presynaptic to an RC dendrite, allowing for so-called triadic inhibition. Taking advantage of a recently developed biophysically detailed multicompartmental model for an IN, we here investigate putative effects of these different inhibitory actions of INs, i.e., triadic inhibition and standard axonal inhibition, on the response properties of RCs. We compute and investigate so-called area-response curves, that is, trial-averaged visual spike responses vs. spot size, for circular flashing spots in a network of RCs and INs. The model parameters are grossly tuned to give results in qualitative accordance with previous in vivo data of responses to such stimuli for cat GCs and RCs. We particularly investigate how the model ingredients affect salient response properties such as the receptive-field center size of RCs and INs, maximal responses and center-surround antagonisms. For example, while triadic inhibition not involving firing of IN action potentials was found to provide only a non-linear gain control of the conversion of input spikes to output spikes by RCs, axonal inhibition was in contrast found to substantially affect the receptive-field center size: the larger the inhibition, the more the RC center size shrinks compared to the GC providing the feedforward excitation. Thus, a possible role of the different inhibitory actions from INs to RCs in the dLGN circuit is to provide separate mechanisms for overall gain control (direct triadic inhibition) and regulation of spatial resolution (axonal inhibition) of visual signals sent to cortex.

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.

T-type calcium channels cause bursts of spikes in motor but not sensory thalamic neurons during mimicry of natural patterns of synaptic input

Although neurons within intact nervous systems can be classified as ‘sensory’ or ‘motor,’ it is not known whether there is any general distinction between sensory and motor neurons at the cellular or molecular levels. Here, we extend and test a theory according to which activation of certain subtypes of voltage-gated ion channel (VGC) generate patterns of spikes in neurons of motor systems, whereas VGC are proposed to counteract patterns in sensory neurons. We previously reported experimental evidence for the theory from visual thalamus, where we found that T-type calcium channels (TtCCs) did not cause bursts of spikes but instead served the function of ‘predictive homeostasis’ to maximize the causal and informational link between retinogeniculate excitation and spike output. Here, we have recorded neurons in brain slices from eight sensory and motor regions of rat thalamus while mimicking key features of natural excitatory and inhibitory post-synaptic potentials. As predicted by theory, TtCC did cause bursts of spikes in motor thalamus. TtCC-mediated responses in motor thalamus were activated at more hyperpolarized potentials and caused larger depolarizations with more spikes than in visual and auditory thalamus. Somatosensory thalamus is known to be more closely connected to motor regions relative to auditory and visual thalamus, and likewise the strength of its TtCC responses was intermediate between these regions and motor thalamus. We also observed lower input resistance, as well as limited evidence of stronger hyperpolarization-induced (‘H-type’) depolarization, in nuclei closer to motor output. These findings support our theory of a specific difference between sensory and motor neurons at the cellular level.

T-type calcium channels promote predictive homeostasis of input-output relations in thalamocortical neurons of lateral geniculate nucleus

A general theory views the function of all neurons as prediction, and one component of this theory is that of “predictive homeostasis” or “prediction error.” It is well established that sensory systems adapt so that neuronal output maintains sensitivity to sensory input, in accord with information theory. Predictive homeostasis applies the same principle at the cellular level, where the challenge is to maintain membrane excitability at the optimal homeostatic level so that spike generation is maximally sensitive to small gradations in synaptic drive. Negative feedback is a hallmark of homeostatic mechanisms, as exemplified by depolarization-activated potassium channels. In contrast, T-type calcium channels exhibit positive feedback that appears at odds with the theory. In thalamocortical neurons of lateral geniculate nucleus (LGN), T-type channels are capable of causing bursts of spikes with an all-or-none character in response to excitation from a hyperpolarized potential. This “burst mode” would partially uncouple visual input from spike output and reduce the information spikes convey about gradations in visual input. However, past observations of T-type-driven bursts may have resulted from unnaturally high membrane excitability. Here we have mimicked within rat brain slices the patterns of synaptic conductance that occur naturally during vision. In support of the theory of predictive homeostasis, we found that T-type channels restored excitability toward its homeostatic level during periods of hyperpolarization. Thus, activation of T-type channels allowed two retinal input spikes to cause one output spike on average, and we observed almost no instances in which output count exceeded input count (a “burst”). T-type calcium channels therefore help to maintain a single optimal mode of transmission rather than creating a second mode. More fundamentally our results support the general theory, which seeks to predict the properties of a neuron's ion channels and synapses given knowledge of natural patterns of synaptic input.

The impact of the lateral geniculate nucleus and corticogeniculate interactions on efficient coding and higher-order visual object processing

Principles of efficient coding suggest that the peripheral units of any sensory processing system are designed for efficient coding. The function of the lateral geniculate nucleus (LGN) as an early stage in the visual system is not well understood. Some findings indicate that similar to the retina that decorrelates input signals spatially, the LGN tends to perform a temporal decorrelation. There is evidence suggesting that corticogeniculate connections may account for this decorrelation in the LGN. In this study, we propose a computational model based on biological evidence reported by Wang et al. (2006), who demonstrated that the influence pattern of V1 feedback is phase-reversed. The output of our model shows how corticogeniculate connections decorrelate LGN responses and make an efficient representation. We evaluated our model using criteria that have previously been tested on LGN neurons through cell recording experiments, including sparseness, entropy, power spectra, and information transfer. We also considered the role of the LGN in higher-order visual object processing, comparing the categorization performance of human subjects with a cortical object recognition model in the presence and absence of our LGN input-stage model. Our results show that the new model that considers the role of the LGN, more closely follows the categorization performance of human subjects.

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.

Cortically-controlled population stochastic facilitation as a plausible substrate for guiding sensory transfer across the thalamic gateway

The thalamus is the primary gateway that relays sensory information to the cerebral cortex. While a single recipient cortical cell receives the convergence of many principal relay cells of the thalamus, each thalamic cell in turn integrates a dense and distributed synaptic feedback from the cortex. During sensory processing, the influence of this functional loop remains largely ignored. Using dynamic-clamp techniques in thalamic slices in vitro, we combined theoretical and experimental approaches to implement a realistic hybrid retino-thalamo-cortical pathway mixing biological cells and simulated circuits. The synaptic bombardment of cortical origin was mimicked through the injection of a stochastic mixture of excitatory and inhibitory conductances, resulting in a gradable correlation level of afferent activity shared by thalamic cells. The study of the impact of the simulated cortical input on the global retinocortical signal transfer efficiency revealed a novel control mechanism resulting from the collective resonance of all thalamic relay neurons. We show here that the transfer efficiency of sensory input transmission depends on three key features: i) the number of thalamocortical cells involved in the many-to-one convergence from thalamus to cortex, ii) the statistics of the corticothalamic synaptic bombardment and iii) the level of correlation imposed between converging thalamic relay cells. In particular, our results demonstrate counterintuitively that the retinocortical signal transfer efficiency increases when the level of correlation across thalamic cells decreases. This suggests that the transfer efficiency of relay cells could be selectively amplified when they become simultaneously desynchronized by the cortical feedback. When applied to the intact brain, this network regulation mechanism could direct an attentional focus to specific thalamic subassemblies and select the appropriate input lines to the cortex according to the descending influence of cortically-defined “priors”.

Most of the sensory information in the early visual system is relayed from the retina to the primary visual cortex through principal relay cells in the thalamus. While relay cells receive ∼7–16% of their synapses from retina, they integrate the synaptic barrage of a dense cortical feedback, which accounts for more than 60% of their total input. This feedback is thought to carry some form of “prior” resulting from the computation performed in cortical areas, which influences the response of relay cells, presumably by regulating the transfer of sensory information to cortical areas. Nevertheless, its statistical nature (input synchronization, excitation/inhibition ratio, etc.) and the cellular mechanisms gating thalamic transfer are largely ignored. Here we implemented hybrid circuits (biological and modeled cells) reproducing the main features of the thalamic gate and explored the functional impact of various statistics of the cortical input. We found that the regulation of sensory information is critically determined by the statistical coherence of the cortical synaptic bombardment associated with a stochastic facilitation process. We propose that this tuning mechanism could operate in the intact brain to selectively filter the sensory information reaching cortical areas according to attended features predesignated by the cortical feedback.

Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity

One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1's function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes.

Interspike interval based filtering of directional selective retinal ganglion cells spike trains

The information regarding visual stimulus is encoded in spike trains at the output of retina by retinal ganglion cells (RGCs). Among these, the directional selective cells (DSRGC) are signaling the direction of stimulus motion. DSRGCs' spike trains show accentuated periods of short interspike intervals (ISIs) framed by periods of isolated spikes. Here we use two types of visual stimulus, white noise and drifting bars, and show that short ISI spikes of DSRGCs spike trains are more often correlated to their preferred stimulus feature (that is, the direction of stimulus motion) and carry more information than longer ISI spikes. Firstly, our results show that correlation between stimulus and recorded neuronal response is best at short ISI spiking activity and decrease as ISI becomes larger. We then used grating bars stimulus and found that as ISI becomes shorter the directional selectivity is better and information rates are higher. Interestingly, for the less encountered type of DSRGC, known as ON-DSRGC, short ISI distribution and information rates revealed consistent differences when compared with the other directional selective cell type, the ON-OFF DSRGC. However, these findings suggest that ISI-based temporal filtering integrates a mechanism for visual information processing at the output of retina toward higher stages within early visual system.

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.

Visual signal processing in the macaque lateral geniculate nucleus

Comparisons of S- or prepotential activity, thought to derive from a retinal ganglion cell afferent, with the activity of relay cells of the lateral geniculate nucleus (LGN) have sometimes implied a loss, or leak, of visual information. The idea of the "leaky" relay cell is reconsidered in the present analysis of prepotential firing and LGN responses of color-opponent cells of the macaque LGN to stimuli varying in size, relative luminance, and spectral distribution. Above a threshold prepotential spike frequency, called the signal transfer threshold (STT), there is a range of more than 2 log units of test field luminance that has a 1:1 relationship between prepotential- and LGN-cell firing rates. Consequently, above this threshold, the LGN cell response can be viewed as an extension of prepotential firing (a "nonleaky relay cell"). The STT level decreased when the size of the stimulus increased beyond the classical receptive field center, indicating that the LGN cell is influenced by factors other than the prepotential input. For opponent ON cells, both the excitatory and the inhibitory response decreased similarly when the test field size increased beyond the center of the receptive field. These findings have consequences for the modeling of LGN cell responses and transmission of visual information, particularly for small fields. For instance, for LGN ON cells, information in the prepotential intensity-response curve for firing rates below the STT is left to be discriminated by OFF cells. Consequently, for a given light adaptation, the STT improves the separation of the response range of retinal ganglion cells into "complementary" ON and OFF pathways.

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.

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.

Recoding of sensory information across the retinothalamic synapse

The neural code that represents the world is transformed at each stage of a sensory pathway. These transformations enable downstream neurons to recode information they receive from earlier stages. Using the retinothalamic synapse as a model system, we developed a theoretical framework to identify stimulus features that are inherited, gained, or lost across stages. Specifically, we observed that thalamic spikes encode novel, emergent, temporal features not conveyed by single retinal spikes. Furthermore, we found that thalamic spikes are not only more informative than retinal ones, as expected, but also more independent. Next, we asked how thalamic spikes gain sensitivity to the emergent features. Explicitly, we found that the emergent features are encoded by retinal spike pairs and then recoded into independent thalamic spikes. Finally, we built a model of synaptic transmission that reproduced our observations. Thus, our results established a link between synaptic mechanisms and the recoding of sensory information.

The episodic nature of spike trains in the early visual pathway

An understanding of the neural code in a given visual area is often confounded by the immense complexity of visual stimuli combined with the number of possible meaningful patterns that comprise the response spike train. In the lateral geniculate nucleus (LGN), visual stimulation generates spike trains comprised of short spiking episodes ("events") separated by relatively long intervals of silence, which establishes a basis for in-depth analysis of the neural code. By studying this event structure in both artificial and natural visual stimulus contexts and at different contrasts, we are able to describe the dependence of event structure on stimulus class and discern which aspects generalize. We find that the event structure on coarse time scales is robust across stimulus and contrast and can be explained by receptive field processing. However, the relationship between the stimulus and fine-time-scale features of events is less straightforward, partially due to a significant amount of trial-to-trial variability. A new measure called "label information" identifies structural elements of events that can contain ≤30% more information in the context of natural movies compared with what is available from the overall event timing. The first interspike interval of an event most robustly conveys additional information about the stimulus and is somewhat more informative than the event spike count and much more informative than the presence of bursts. Nearly every event is preserved across contrast despite changes in their fine-time-scale features, suggesting that--at least on a coarse level--the stimulus selectivity of LGN neurons is contrast invariant. Event-based analysis thus casts previously studied elements of LGN coding such as contrast adaptation and receptive field processing in a new light and leads to broad conclusions about the composition of the LGN neuronal code.

A generalized linear model of the impact of direct and indirect inputs to the lateral geniculate nucleus

Relay neurons in the lateral geniculate nucleus (LGN) receive direct visual input predominantly from a single retinal ganglion cell (RGC), in addition to indirect input from other sources including interneurons, thalamic reticular nucleus (TRN), and the visual cortex. To address the extent of influence of these indirect sources on the response properties of the LGN neurons, we fit a Generalized Linear Model (GLM) to the spike responses of cat LGN neurons driven by spatially homogeneous spots that were rapidly modulated by a pseudorandom luminance sequence. Several spot sizes were used to probe the spatial extent of the indirect visual effects. Our extracellular recordings captured both the LGN spikes and the incoming RGC input (S potentials), allowing us to divide the inputs to the GLM into two categories: the direct RGC input and the indirect input to which we have access through the luminance of the visual stimulus. For spots no larger than the receptive field center, the effect of the indirect input is negligible, while for larger spots its effect can, on average, account for 5% of the variance of the data and for as much as 25% in some cells. The polarity of the indirect visual influence is opposite to that of the linear receptive field of the neurons. We conclude that the indirect source of response modulation of the LGN relay neurons arises from inhibitory sources, compatible with thalamic interneurons or TRN.

Sharpening of directional selectivity from neural output of rabbit retina

The estimation of motion direction from time varying retinal images is a fundamental task of visual systems. Neurons that selectively respond to directional visual motion are found in almost all species. In many of them already in the retina direction selective neurons signal their preferred direction of movement. Scientific evidences suggest that direction selectivity is carried from the retina to higher brain areas. Here we adopt a simple integrate-and-fire neuron model, inspired by recent work of Casti et al. (2008), to investigate how directional selectivity changes in cells postsynaptic to directional selective retinal ganglion cells (DSRGC). Our model analysis shows that directional selectivity in the postsynaptic cells increases over a wide parameter range. The degree of directional selectivity positively correlates with the probability of burst-like firing of presynaptic DSRGCs. Postsynaptic potentials summation and spike threshold act together as a temporal filter upon the input spike train. Prior to the intricacy of neural circuitry between retina and higher brain areas, we suggest that sharpening is a straightforward result of the intrinsic spiking pattern of the DSRGCs combined with the summation of excitatory postsynaptic potentials and the spike threshold in postsynaptic neurons.

Stimulus size dependence of information transfer from retina to thalamus

Relay cells in the mammalian lateral geniculate nucleus (LGN) are driven primarily by single retinal ganglion cells (RGCs). However, an LGN cell responds typically to less than half of the spikes it receives from the RGC that drives it, and without retinal drive the LGN is silent (Kaplan and Shapley, 1984). Recent studies, which used stimuli restricted to the receptive field (RF) center, show that despite the great loss of spikes, more than half of the information carried by the RGC discharge is typically preserved in the LGN discharge (Sincich et al., 2009), suggesting that the retinal spikes that are deleted by the LGN carry less information than those that are transmitted to the cortex. To determine how LGN relay neurons decide which retinal spikes to respond to, we recorded extracellularly from the cat LGN relay cell spikes together with the slow synaptic ('S') potentials that signal the firing of retinal spikes. We investigated the influence of the inhibitory surround of the LGN RF by stimulating the eyes with spots of various sizes, the largest of which covered the center and surround of the LGN relay cell's RF. We found that for stimuli that activated mostly the RF center, each LGN spike delivered more information than the retinal spike, but this difference was reduced as stimulus size increased to cover the RF surround. To evaluate the optimality of the LGN editing of retinal spikes, we created artificial spike trains from the retinal ones by various deletion schemes. We found that single LGN cells transmitted less information than an optimal detector could.

Preserving information in neural transmission

Along most neural pathways, the spike trains transmitted from one neuron to the next are altered. In the process, neurons can either achieve a more efficient stimulus representation, or extract some biologically important stimulus parameter, or succeed at both. We recorded the inputs from single retinal ganglion cells and the outputs from connected lateral geniculate neurons in the macaque to examine how visual signals are relayed from retina to cortex. We found that geniculate neurons re-encoded multiple temporal stimulus features to yield output spikes that carried more information about stimuli than was available in each input spike. The coding transformation of some relay neurons occurred with no decrement in information rate, despite output spike rates that averaged half the input spike rates. This preservation of transmitted information was achieved by the short-term summation of inputs that geniculate neurons require to spike. A reduced model of the retinal and geniculate visual responses, based on two stimulus features and their associated nonlinearities, could account for >85% of the total information available in the spike trains and the preserved information transmission. These results apply to neurons operating on a single time-varying input, suggesting that synaptic temporal integration can alter the temporal receptive field properties to create a more efficient representation of visual signals in the thalamus than the retina.