Paired-spike interactions and synaptic efficacy of retinal inputs to the thalamus

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.

Spike-based probabilistic inference in analog graphical models using interspike-interval coding

Temporal spike codes play a crucial role in neural information processing. In particular, there is strong experimental evidence that interspike intervals (ISIs) are used for stimulus representation in neural systems. However, very few algorithmic principles exploit the benefits of such temporal codes for probabilistic inference of stimuli or decisions. Here, we describe and rigorously prove the functional properties of a spike-based processor that uses ISI distributions to perform probabilistic inference. The abstract processor architecture serves as a building block for more concrete, neural implementations of the belief-propagation (BP) algorithm in arbitrary graphical models (e.g., Bayesian networks and factor graphs). The distributed nature of graphical models matches well with the architectural and functional constraints imposed by biology. In our model, ISI distributions represent the BP messages exchanged between factor nodes, leading to the interpretation of a single spike as a random sample that follows such a distribution. We verify the abstract processor model by numerical simulation in full graphs, and demonstrate that it can be applied even in the presence of analog variables. As a particular example, we also show results of a concrete, neural implementation of the processor, although in principle our approach is more flexible and allows different neurobiological interpretations. Furthermore, electrophysiological data from area LIP during behavioral experiments are assessed in light of ISI coding, leading to concrete testable, quantitative predictions and a more accurate description of these data compared to hitherto existing models.

Thalamic microcircuits: presynaptic dendrites form two feedforward inhibitory pathways in thalamus

In the visual thalamus, retinal afferents activate both local interneurons and excitatory thalamocortical relay neurons, leading to robust feedforward inhibition that regulates the transmission of sensory information from retina to neocortex. Peculiarly, this feedforward inhibitory pathway is dominated by presynaptic dendrites. Previous work has shown that the output of dendritic terminals of interneurons, also known as F2 terminals, are regulated by both ionotropic and metabotropic glutamate receptors. However, it is unclear whether both classes of glutamate receptors regulate output from the same or distinct dendritic terminals. Here, we used focal glutamate uncaging and whole cell recordings to reveal two types of F2 responses in rat visual thalamus. The first response, which we are calling a Type-A response, was mediated exclusively by ionotropic glutamate receptors (i.e., AMPA and NMDA). In contrast, the second response, which we are calling a Type-B response, was mediated by a combination of ionotropic and type 5 metabotropic glutamate receptors (i.e., mGluR(5)). In addition, we demonstrate that both F2 responses are evoked in the same postsynaptic neurons, which are morphologically distinct from neurons in which no F2 responses are observed. Since photostimulation was relatively focal and small in magnitude, these results suggest distinct F2 terminals, or small clusters of terminals, could be responsible for generating the two inhibitory responses observed. Because of the nature of ionotropic and metabotropic glutamate receptors, we predict the efficacy by which the retina communicates with the thalamus would be strongly regulated by 1) the activity level of a given retinogeniculate axon, and 2) the specific type of F2 terminals activated.

T-type calcium channels consolidate tonic action potential output of thalamic neurons to neocortex

The thalamic output during different behavioral states is strictly controlled by the firing modes of thalamocortical neurons. During sleep, their hyperpolarized membrane potential allows activation of the T-type calcium channels, promoting rhythmic high-frequency burst firing that reduces sensory information transfer. In contrast, in the waking state thalamic neurons mostly exhibit action potentials at low frequency (i.e., tonic firing), enabling the reliable transfer of incoming sensory inputs to cortex. Because of their nearly complete inactivation at the depolarized potentials that are experienced during the wake state, T-channels are not believed to modulate tonic action potential discharges. Here, we demonstrate using mice brain slices that activation of T-channels in thalamocortical neurons maintained in the depolarized/wake-like state is critical for the reliable expression of tonic firing, securing their excitability over changes in membrane potential that occur in the depolarized state. Our results establish a novel mechanism for the integration of sensory information by thalamocortical neurons and point to an unexpected role for T-channels in the early stage of information processing.

From functional architecture to functional connectomics

"Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex" by Hubel and Wiesel (1962) reported several important discoveries: orientation columns, the distinct structures of simple and complex receptive fields, and binocular integration. But perhaps the paper's greatest influence came from the concept of functional architecture (the complex relationship between in vivo physiology and the spatial arrangement of neurons) and several models of functionally specific connectivity. They thus identified two distinct concepts, topographic specificity and functional specificity, which together with cell-type specificity constitute the major determinants of nonrandom cortical connectivity. Orientation columns are iconic examples of topographic specificity, whereby axons within a column connect with cells of a single orientation preference. Hubel and Wiesel also saw the need for functional specificity at a finer scale in their model of thalamic inputs to simple cells, verified in the 1990s. The difficult but potentially more important question of functional specificity between cortical neurons is only now becoming tractable with new experimental techniques.

Monosynaptic functional connectivity in cerebral cortex during wakefulness and under graded levels of anesthesia

The balance between excitation and inhibition is considered to be of significant importance for neural computation and cognitive function. Excitatory and inhibitory functional connectivity in intact cortical neuronal networks in wakefulness and graded levels of anesthesia has not been systematically investigated. We compared monosynaptic excitatory and inhibitory spike transmission probabilities using pairwise cross-correlogram (CCG) analysis. Spikes were measured at 64 sites in the visual cortex of rats with chronically implanted microelectrode arrays during wakefulness and three levels of anesthesia produced by desflurane. Anesthesia decreased the number of active units, the number of functional connections, and the strength of excitatory connections. Connection probability (number of connections per number of active unit pairs) was unaffected until the deepest anesthesia level, at which a significant increase in the excitatory to inhibitory ratio of connection probabilities was observed. The results suggest that the excitatory–inhibitory balance is altered at an anesthetic depth associated with unconsciousness.

Microsaccadic efficacy and contribution to foveal and peripheral vision

Our eyes move constantly, even when we try to fixate our gaze. Fixational eye movements prevent and restore visual loss during fixation, yet the relative impact of each type of fixational eye movement remains controversial. For over five decades, the debate has focused on microsaccades, the fastest and largest fixational eye movements. Some recent studies have concluded that microsaccades counteract visual fading during fixation. Other studies have disputed this idea, contending that microsaccades play no significant role in vision. The disagreement stems from the lack of methods to determine the precise effects of microsaccades on vision versus those of other eye movements, as well as a lack of evidence that microsaccades are relevant to foveal vision. Here we developed a novel generalized method to determine the precise quantified contribution and efficacy of human microsaccades to restoring visibility compared with other eye movements. Our results indicate that microsaccades are the greatest eye movement contributor to the restoration of both foveal and peripheral vision during fixation. Our method to calculate the efficacy and contribution of microsaccades to perception can determine the strength of connection between any two physiological and/or perceptual events, providing a novel and powerful estimate of causal influence; thus, we anticipate wide-ranging applications in neuroscience and beyond.

Inter-neuronal correlation distinguishes mechanisms of direction selectivity in cortical circuit models

Direction selectivity is a fundamental physiological property that arises from primary visual cortex (V1) circuitry, yet basic questions of how direction-selective (DS) receptive fields are constructed remain unanswered. We built a set of simple, plausible neuronal circuits that produce DS cells via different mechanisms and tested these circuits to determine how they can be distinguished experimentally. Our models consisted of populations of spiking units representing physiological cell classes ranging from LGN cells to V1 complex DS cells. They differed in network architecture and DS mechanism, including linear summation of non-DS simple-cell inputs or nonlinear pairwise combinations of non-DS inputs. The circuits also varied in the location of the DS time delay and whether the DS interaction was facilitatory or suppressive. We tested the models with visual stimuli often used experimentally, including sinusoidal gratings and flashed bars, and computed shuffle-corrected cross-correlograms (CCGs) of spike trains from pairs of units that would be accessible to extracellular recording. We found that CCGs revealed fundamental features of the DS models, including the location of signal delays in the DS circuit and the sign (facilitatory or suppressive) of DS interactions. We also found that correlation was strongly stimulus-dependent, changing with direction and temporal frequency in a manner that generalized across model architectures. Our models make specific predictions for designing, optimizing, and interpreting electrophysiology experiments aimed at resolving DS circuitry and provide new insights into mechanisms that could underlie stimulus-dependent correlation. The models are available and easy to explore at www.imodel.org.

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.

Computing with neural synchrony

Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties.

How does the brain compute? Traditional theories of neural computation describe the operating function of neurons in terms of average firing rates, with the timing of spikes bearing little information. However, numerous studies have shown that spike timing can convey information and that neurons are highly sensitive to synchrony in their inputs. Here I propose a simple spike-based computational framework, based on the idea that stimulus-induced synchrony can be used to extract sensory invariants (for example, the location of a sound source), which is a difficult task for classical neural networks. It relies on the simple remark that a series of repeated coincidences is in itself an invariant. Many aspects of perception rely on extracting invariant features, such as the spatial location of a time-varying sound, the identity of an odor with fluctuating intensity, the pitch of a musical note. I demonstrate that simple synchrony-based neuron models can extract these useful features, by using spiking models in several sensory modalities.

A novel, jitter-based method for detecting and measuring spike synchrony and quantifying temporal firing precision

BACKGROUND

Precise spike synchrony, at the millisecond or even sub-millisecond time scale, has been reported in different brain areas, but its neurobiological meaning and its underlying mechanisms remain unknown or controversial. Studying these questions is complicated by the lack of a validated, well-normalized and robust index for quantifying synchrony. Previously used measures of synchrony are often improperly normalized and thereby are not comparable between different experimental conditions, are sensitive to variations in firing rate or to the firing rate differential between the two neurons, and/or rely on untenable assumptions of firing rate stationarity and Poisson statistics. I describe here a novel measure, the Jitter-Based Synchrony Index (JBSI), that overcomes these issues.

RESULTS AND DISCUSSION

The JBSI method is based on the introduction of virtual spike jitter. While previous implementations of the jitter method used it only to detect synchrony, the JBSI method also quantifies synchrony. Previous implementations of the jitter method used computationally intensive Monte Carlo simulations to generate surrogate spike trains, whereas the JBSI is computed analytically. The JBSI method does not assume any specific firing model, and does not require that the spike trains be locked to a repeating external stimulus. The JBSI can assume values from 1 (maximal possible synchrony) to -1 (minimal possible synchrony) and is therefore properly normalized. Using simulated Poisson spike trains with introduced controlled spike coincidences, I demonstrate that the JBSI is a linear measure of the spike coincidence rate, is independent of the mean firing frequency or the firing frequency differential between the two neurons, and is not sensitive to co-modulations in the firing rates of the two neurons. In contrast, several commonly used synchrony indices fail under one or more of these scenarios. I also demonstrate how the JBSI can be used to estimate the spike timing precision in the system.

CONCLUSIONS

The JBSI is a conceptually simple and computationally efficient method that can be used to compute the statistical significance of firing synchrony, to quantify synchrony as a well-normalized index, and to estimate the degree of temporal precision in the system.

Local dendrodendritic inhibition regulates fast synaptic transmission in visual thalamus

Inhibition from thalamic interneurons plays a critical role in modulating information transfer between thalamus and neocortex. Interestingly, these neurons yield inhibition via two distinct outputs: presynaptic dendrites that innervate thalamocortical relay neurons and axonal outputs. Since the dendrites of thalamic interneurons are the primary targets of incoming synaptic information, it has been hypothesized that local synaptic input could produce highly focused dendritic output. To gain additional insight into the computational power of these presynaptic dendrites, we have combined two-photon laser scanning microscopy, glutamate uncaging, and whole-cell electrophysiological recordings to locally activate dendritic terminals and study their inhibitory contribution to rat thalamocortical relay neurons. Our findings demonstrate that local dendritic release from thalamic interneurons is controlled locally by AMPA/NMDA receptor-mediated recruitment of L-type calcium channels. Moreover, by mapping these connections with single dendrite resolution we not only found that presynaptic dendrites preferentially target proximal regions, but such actions differ significantly across branches. Furthermore, local stimulation of interneuron dendrites did not result in global excitation, supporting the notion that these interneurons can operate as multiplexors, containing numerous independently operating input-output devices.

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.

Sensitivity of noisy neurons to coincident inputs

How do neurons compute? Two main theories compete: neurons could temporally integrate noisy inputs (rate-based theories) or they could detect coincident input spikes (spike timing-based theories). Correlations at fine timescales have been observed in many areas of the nervous system, but they might have a minor impact. To address this issue, we used a probabilistic approach to quantify the impact of coincidences on neuronal response in the presence of fluctuating synaptic activity. We found that when excitation and inhibition are balanced, as in the sensory cortex in vivo, synchrony in a very small proportion of inputs results in dramatic increases in output firing rate. Our theory was experimentally validated with in vitro recordings of cortical neurons of mice. We conclude that not only are noisy neurons well equipped to detect coincidences, but they are so sensitive to fine correlations that a rate-based description of neural computation is unlikely to be accurate in general.

Spikes with short inter-spike intervals in frog retinal ganglion cells are more correlated with their adjacent neurons' activities

Correlated firings among neurons have been extensively investigated; however, previous studies on retinal ganglion cell (RGC) population activities were mainly based on analyzing the correlated activities between the entire spike trains. In the present study, the correlation properties were explored based on burst-like activities and solitary spikes separately. The results indicate that: (1) burst-like activities were more correlated with other neurons' activities; (2) burst-like spikes correlated with their neighboring neurons represented a smaller receptive field than that of correlated solitary spikes. These results suggest that correlated burst-like spikes should be more efficient in signal transmission, and could encode more detailed spatial information.

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.

Rate Dynamics of Leaky Integrate-and-Fire Neurons with Strong Synapses

Firing-rate models provide a practical tool for studying the dynamics of trial- or population-averaged neuronal signals. A wealth of theoretical and experimental studies has been dedicated to the derivation or extraction of such models by investigating the firing-rate response characteristics of ensembles of neurons. The majority of these studies assumes that neurons receive input spikes at a high rate through weak synapses (diffusion approximation). For many biological neural systems, however, this assumption cannot be justified. So far, it is unclear how time-varying presynaptic firing rates are transmitted by a population of neurons if the diffusion assumption is dropped. Here, we numerically investigate the stationary and non-stationary firing-rate response properties of leaky integrate-and-fire neurons receiving input spikes through excitatory synapses with alpha-function shaped postsynaptic currents for strong synaptic weights. Input spike trains are modeled by inhomogeneous Poisson point processes with sinusoidal rate. Average rates, modulation amplitudes, and phases of the period-averaged spike responses are measured for a broad range of stimulus, synapse, and neuron parameters. Across wide parameter regions, the resulting transfer functions can be approximated by a linear first-order low-pass filter. Below a critical synaptic weight, the cutoff frequencies are approximately constant and determined by the synaptic time constants. Only for synapses with unrealistically strong weights are the cutoff frequencies significantly increased. To account for stimuli with larger modulation depths, we combine the measured linear transfer function with the nonlinear response characteristics obtained for stationary inputs. The resulting linear-nonlinear model accurately predicts the population response for a variety of non-sinusoidal stimuli.

Modulation of temporal precision in thalamic population responses to natural visual stimuli

Natural visual stimuli have highly structured spatial and temporal properties which influence the way visual information is encoded in the visual pathway. In response to natural scene stimuli, neurons in the lateral geniculate nucleus (LGN) are temporally precise - on a time scale of 10-25 ms - both within single cells and across cells within a population. This time scale, established by non stimulus-driven elements of neuronal firing, is significantly shorter than that of natural scenes, yet is critical for the neural representation of the spatial and temporal structure of the scene. Here, a generalized linear model (GLM) that combines stimulus-driven elements with spike-history dependence associated with intrinsic cellular dynamics is shown to predict the fine timing precision of LGN responses to natural scene stimuli, the corresponding correlation structure across nearby neurons in the population, and the continuous modulation of spike timing precision and latency across neurons. A single model captured the experimentally observed neural response, across different levels of contrasts and different classes of visual stimuli, through interactions between the stimulus correlation structure and the nonlinearity in spike generation and spike history dependence. Given the sensitivity of the thalamocortical synapse to closely timed spikes and the importance of fine timing precision for the faithful representation of natural scenes, the modulation of thalamic population timing over these time scales is likely important for cortical representations of the dynamic natural visual environment.

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.

Spike timing and information transmission at retinogeniculate synapses

This study examines the rules governing the transfer of spikes between the retina and the lateral geniculate nucleus (LGN) with the goal of determining whether the most informative retinal spikes preferentially drive LGN responses and what role spike timing plays in the process. By recording from monosynaptically connected pairs of retinal ganglion cells and LGN neurons in vivo in the cat, we show that relayed spikes are more likely than nonrelayed spikes to be evoked by stimuli that match the receptive fields of the recorded cells and that an interspike interval-based mechanism contributes to the process. Relayed spikes are also more reliable in their timing and number where they often achieve the theoretical limit of minimum variance. As a result, relayed spikes carry more visual information per spike. Based on these results, we conclude that retinogeniculate processing increases sparseness in the neural code by selectively relaying the highest fidelity spikes to the visual cortex.

Correlated firing among major ganglion cell types in primate retina

Retinal ganglion cells exhibit substantial correlated firing: a tendency to fire nearly synchronously at rates different from those expected by chance. These correlations suggest that network interactions significantly shape the visual signal transmitted from the eye to the brain. This study describes the degree and structure of correlated firing among the major ganglion cell types in primate retina. Correlated firing among ON and OFF parasol, ON and OFF midget, and small bistratified cells, which together constitute roughly 75% of the input to higher visual areas, was studied using large-scale multi-electrode recordings. Correlated firing in the presence of constant, spatially uniform illumination exhibited characteristic strength, time course and polarity within and across cell types. Pairs of nearby cells with the same light response polarity were positively correlated; cells with the opposite polarity were negatively correlated. The strength of correlated firing declined systematically with distance for each cell type, in proportion to the degree of receptive field overlap. The pattern of correlated firing across cell types was similar at photopic and scotopic light levels, although additional slow correlations were present at scotopic light levels. Similar results were also observed in two other retinal ganglion cell types. Most of these observations are consistent with the hypothesis that shared noise from photoreceptors is the dominant cause of correlated firing. Surprisingly, small bistratified cells, which receive ON input from S cones, fired synchronously with ON parasol and midget cells, which receive ON input primarily from L and M cones. Collectively, these results provide an overview of correlated firing across cell types in the primate retina, and constraints on the underlying mechanisms.

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.

Exploring the function of neural oscillations in early sensory systems

Neuronal oscillations appear throughout the nervous system, in structures as diverse as the cerebral cortex, hippocampus, subcortical nuclei and sense organs. Whether neural rhythms contribute to normal function, are merely epiphenomena, or even interfere with physiological processing are topics of vigorous debate. Sensory pathways are ideal for investigation of oscillatory activity because their inputs can be defined. Thus, we will focus on sensory systems as we ask how neural oscillations arise and how they might encode information about the stimulus. We will highlight recent work in the early visual pathway that shows how oscillations can multiplex different types of signals to increase the amount of information that spike trains encode and transmit. Last, we will describe oscillation-based models of visual processing and explore how they might guide further research.

The response dynamics of rabbit retinal ganglion cells to simulated blur

Retinal ganglion cells (RGCs) are highly sensitive to changes in contrast, which is crucial for the detection of edges in a visual scene. However, in the natural environment, edges do not just vary in contrast, but edges also vary in the degree of blur, which can be caused by distance from the plane of fixation, motion, and shadows. Hence, blur is as much a characteristic of an edge as luminance contrast, yet its effects on the responses of RGCs are largely unexplored.We examined the responses of rabbit RGCs to sharp edges varying by contrast and also to high-contrast edges varying by blur. The width of the blur profile ranged from 0.73 to 13.05 deg of visual angle. For most RGCs, blurring a high-contrast edge produced the same pattern of reduction of response strength and increase in latency as decreasing the contrast of a sharp edge. In support of this, we found a significant correlation between the amount of blur required to reduce the response by 50% and the size of the receptive fields, suggesting that blur may operate by reducing the range of luminance values within the receptive field. These RGCs cannot individually encode for blur, and blur could only be estimated by comparing the responses of populations of neurons with different receptive field sizes. However, some RGCs showed a different pattern of changes in latency and magnitude with changes in contrast and blur; these neurons could encode blur directly.We also tested whether the response of a RGC to a blurred edge was linear, that is, whether the response of a neuron to a sharp edge was equal to the response to a blurred edge plus the response to the missing spatial components that were the difference between a sharp and blurred edge. Brisk-sustained cells were more linear; however, brisk-transient cells exhibited both linear and nonlinear behavior.

Inhibition of adult rat retinal ganglion cells by D1-type dopamine receptor activation

The spike output of neural pathways can be regulated by modulating output neuron excitability and/or their synaptic inputs. Dopaminergic interneurons synapse onto cells that route signals to mammalian retinal ganglion cells, but it is unknown whether dopamine can activate receptors in these ganglion cells and, if it does, how this affects their excitability. Here, we show D(1a) receptor-like immunoreactivity in ganglion cells identified in adult rats by retrogradely transported dextran, and that dopamine, D(1)-type receptor agonists, and cAMP analogs inhibit spiking in ganglion cells dissociated from adult rats. These ligands curtailed repetitive spiking during constant current injections and reduced the number and rate of rise of spikes elicited by fluctuating current injections without significantly altering the timing of the remaining spikes. Consistent with mediation by D(1)-type receptors, SCH-23390 [R-(+)-7-chloro-8-hydroxy-3-methyl-1-phenyl-2,3,4,5-tetrahydro-1H-3-benzazepine] reversed the effects of dopamine on spikes. Contrary to a recent report, spike inhibition by dopamine was not precluded by blocking I(h). Consistent with the reduced rate of spike rise, dopamine reduced voltage-gated Na(+) current (I(Na)) amplitude, and tetrodotoxin, at doses that reduced I(Na) as moderately as dopamine, also inhibited spiking. These results provide the first direct evidence that D(1)-type dopamine receptor activation can alter mammalian retinal ganglion cell excitability and demonstrate that dopamine can modulate spikes in these cells by a mechanism different from the presynaptic and postsynaptic means proposed by previous studies. To our knowledge, our results also provide the first evidence that dopamine receptor activation can reduce excitability without altering the temporal precision of spike firing.

Spatial-temporal patterns of retinal waves underlying activity-dependent refinement of retinofugal projections

During development, retinal axons project coarsely within their visual targets before refining to form organized synaptic connections. Spontaneous retinal activity, in the form of acetylcholine-driven retinal waves, is proposed to be necessary for establishing these projection patterns. In particular, both axonal terminations of retinal ganglion cells (RGCs) and the size of receptive fields of target neurons are larger in mice that lack the beta2 subunit of the nicotinic acetylcholine receptor (beta2KO). Here, using a large-scale, high-density multielectrode array to record activity from hundreds of RGCs simultaneously, we present analysis of early postnatal retinal activity from both wild-type (WT) and beta2KO retinas. We find that beta2KO retinas have correlated patterns of activity, but many aspects of these patterns differ from those of WT retina. Quantitative analysis suggests that wave directionality, coupled with short-range correlated bursting patterns of RGCs, work together to refine retinofugal projections.

Input-specific intrasynaptic arrangements of ionotropic glutamate receptors and their impact on postsynaptic responses

To examine the intrasynaptic arrangement of postsynaptic receptors in relation to the functional role of the synapse, we quantitatively analyzed the two-dimensional distribution of AMPA and NMDA receptors (AMPARs and NMDARs, respectively) using SDS-digested freeze-fracture replica labeling (SDS-FRL) and assessed the implication of distribution differences on the postsynaptic responses by simulation. In the dorsal lateral geniculate nucleus, corticogeniculate (CG) synapses were twice as large as retinogeniculate (RG) synapses but expressed similar numbers of AMPARs. Two-dimensional views of replicas revealed that AMPARs form microclusters in both synapses to a similar extent, resulting in larger AMPAR-lacking areas in the CG synapses. Despite the broad difference in the AMPAR distribution within a synapse, our simulations based on the actual receptor distributions suggested that the AMPAR quantal response at individual RG synapses is only slightly larger in amplitude, less variable, and faster in kinetics than that at CG synapses having a similar number of the receptors. NMDARs at the CG synapses were expressed twice as many as those in the RG synapses. Electrophysiological recordings confirmed a larger contribution of NMDAR relative to AMPAR-mediated responses in CG synapses. We conclude that synapse size and the density and distribution of receptors have minor influences on quantal responses and that the number of receptors acts as a predominant postsynaptic determinant of the synaptic strength mediated by both the AMPARs and NMDARs.

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.

Activity patterns govern synapse-specific AMPA receptor trafficking between deliverable and synaptic pools

In single neurons, glutamatergic synapses receiving distinct afferent inputs may contain AMPA receptors (-Rs) with unique subunit compositions. However, the cellular mechanisms by which differential receptor transport achieves this synaptic diversity remain poorly understood. In lateral geniculate neurons, we show that retinogeniculate and corticogeniculate synapses have distinct AMPA-R subunit compositions. Under basal conditions at both synapses, GluR1-containing AMPA-Rs are transported from an anatomically defined reserve pool to a deliverable pool near the postsynaptic density (PSD), but further incorporate into the PSD or functional synaptic pool only at retinogeniculate synapses. Vision-dependent activity, stimulation mimicking retinal input, or activation of CaMKII or Ras signaling regulated forward GluR1 trafficking from the deliverable pool to the synaptic pool at both synapses, whereas Rap2 signals reverse GluR1 transport at retinogeniculate synapses. These findings suggest that synapse-specific AMPA-R delivery involves constitutive and activity-regulated transport steps between morphological pools, a mechanism that may extend to the site-specific delivery of other membrane protein complexes.

Retinal oscillations carry visual information to cortex

Thalamic relay cells fire action potentials that transmit information from retina to cortex. The amount of information that spike trains encode is usually estimated from the precision of spike timing with respect to the stimulus. Sensory input, however, is only one factor that influences neural activity. For example, intrinsic dynamics, such as oscillations of networks of neurons, also modulate firing pattern. Here, we asked if retinal oscillations might help to convey information to neurons downstream. Specifically, we made whole-cell recordings from relay cells to reveal retinal inputs (EPSPs) and thalamic outputs (spikes) and then analyzed these events with information theory. Our results show that thalamic spike trains operate as two multiplexed channels. One channel, which occupies a low frequency band (<30 Hz), is encoded by average firing rate with respect to the stimulus and carries information about local changes in the visual field over time. The other operates in the gamma frequency band (40–80 Hz) and is encoded by spike timing relative to retinal oscillations. At times, the second channel conveyed even more information than the first. Because retinal oscillations involve extensive networks of ganglion cells, it is likely that the second channel transmits information about global features of the visual scene.

Functional consequences of neuronal divergence within the retinogeniculate pathway

The neuronal connections from the retina to the dorsal lateral geniculate nucleus (dLGN) are characterized by a high specificity. Each retinal ganglion cell diverges to connect to a small group of geniculate cells and each geniculate cell receives input from a small number of retinal ganglion cells. Consistent with the high specificity of the connections, geniculate cells sharing input from the same retinal afferent are thought to have very similar receptive fields. However, the magnitude of the receptive-field mismatches, which has not been systematically measured across the different cell types in dLGN, seems to be in contradiction with the functional anatomy of the Y visual pathway: Y retinal afferents in the cat diverge into two geniculate layers (A and C) that have Y geniculate cells (Y(A) and Y(C)) with different receptive-field sizes, response latencies, nonlinearity of spatial summation, and contrast sensitivity. To better understand the functional consequences of retinogeniculate divergence, we recorded from pairs of geniculate cells that shared input from a common retinal afferent across layers and within the same layer in dLGN. We found that nearly all cell pairs that shared retinal input across layers had Y-type receptive fields of the same sign (i.e., both on-center) that overlapped by >70%, but frequently differed in size and response latency. The receptive-field mismatches were relatively small in value (receptive-field size ratio <5; difference in peak response <5 ms), but were robustly correlated with the strength of the synchronous firing generated by the shared retinal connections (R(2) = 0.75). On average, the percentage of geniculate spikes that could be attributed to shared retinal inputs was about 10% for all cell-pair combinations studied. These results are used to provide new estimates of retinogeniculate divergence for different cell classes.

Measuring sub-millisecond delays in spiking activity with millisecond time-bins

Recent evidence indicates that sub-millisecond delays in neuronal spiking activity may be relevant for neural coding. Estimates of these delays are usually made from cross-correlation histograms (CCH) binned to 1ms. We investigated the degree to which it is possible to measure delays with sub-millisecond precision when one computes CCHs with bin sizes > or =1ms. To this end, we introduced sub-millisecond shifts into spike trains recorded from cat visual cortex. The bin sizes of 1/2 to 2ms were the most optimal for measuring the artificial shifts, even when detecting shifts smaller than 0.5ms. The results suggest that preferably, one should use CCHs with approximately 1ms binning even when investigating differences in delays considerably smaller than 1ms.

Synchronized firing in the retina

Synchronized firing in neural populations has been proposed to constitute an elementary aspect of the neural code, but a complete understanding of its origins and significance has been elusive. Synchronized firing has been extensively documented in retinal ganglion cells, the output neurons of the retina. However, differences in synchronized firing across species and cell types have led to varied conclusions about its mechanisms and role in visual signaling. Recent work on two identified cell populations in the primate retina, the ON-parasol and OFF-parasol cells, permits a more unified understanding. Intracellular recordings reveal that synchronized firing in these cell types arises primarily from common synaptic input to adjacent pairs of cells. Statistical analysis indicates that local pairwise interactions can explain the pattern of synchronized firing in the entire parasol cell population. Computational analysis reveals that the aggregate impact of synchronized firing on the visual signal is substantial. Thus, in the parasol cells, the origin and impact of synchronized firing on the neural code may be understood as locally shared input which influences the visual signals transmitted from eye to brain.

Osmotic modulation of stimulus-evoked responses in the rat supraoptic nucleus

Neural information is conveyed by action potentials along axons to downstream synaptic targets. Synapses permit functionally relevant modulation of the information transmitted by converging inputs. Previous studies have measured the amount of information associated with a given stimulus based either on spike counts or on the relative frequencies of spike sequences represented as binary strings. Here we apply information theory to the phase-interval stimulus histogram (PhISH) to measure the extent of the stimulus-evoked response using the statistical relationship between each interspike interval and its phase within the stimulus cycle. We used the PhISH as a novel approach to investigate how different osmotic states affect the flow of information through the osmoreceptor complex of the hypothalamus. The amount of information conveyed from one (afferent) element of the complex, the anteroventral region of the third ventricle (AV3V), to another (an efferent element), the supraoptic nucleus, was increased by hypertonic stimulation (intravenous mannitol, z = 4.39, P < 0.001) and decreased by hypotonic stimulation (intragastric water, z = -3.37, P < 0.001). Supraoptic responses to AV3V stimulation differed from those that follow stimulation of a hypothalamic element outside the osmoreceptor complex, the suprachiasmatic nucleus (SCN), which also projects to the supraoptic nucleus. Thus osmosensitive gain control mechanisms differentially modulate osmotically dependent and osmotically independent inputs, and enhance the osmoresponsiveness of supraoptic cells within a physiological range. The value of the novel approach is that its use is not limited to the osmoreceptor ensemble but it can be used to investigate the flow of information throughout the central nervous system.

Deconstruction of spatial integrity in visual stimulus detected by modulation of synchronized activity in cat visual cortex

Spatiotemporal relationships among contour segments can influence synchronization of neural responses in the primary visual cortex. We performed a systematic study to dissociate the impact of spatial and temporal factors in the signaling of contour integration via synchrony. In addition, we characterized the temporal evolution of this process to clarify potential underlying mechanisms. With a 10 x 10 microelectrode array, we recorded the simultaneous activity of multiple cells in the cat primary visual cortex while stimulating with drifting sine-wave gratings. We preserved temporal integrity and systematically degraded spatial integrity of the sine-wave gratings by adding spatial noise. Neural synchronization was analyzed in the time and frequency domains by conducting cross-correlation and coherence analyses. The general association between neural spike trains depends strongly on spatial integrity, with coherence in the gamma band (35-70 Hz) showing greater sensitivity to the change of spatial structure than other frequency bands. Analysis of the temporal dynamics of synchronization in both time and frequency domains suggests that spike timing synchronization is triggered nearly instantaneously by coherent structure in the stimuli, whereas frequency-specific oscillatory components develop more slowly, presumably through network interactions. Our results suggest that, whereas temporal integrity is required for the generation of synchrony, spatial integrity is critical in triggering subsequent gamma band synchronization.

The taste of sugars

Sugars evoke a distinctive perceptual quality ("sweetness" in humans) and are generally highly preferred. The neural basis for these phenomena is reviewed for rodents, in which detailed electrophysiological measurements have been made. A receptor has been identified that binds sweeteners and activates G-protein-mediated signaling in taste receptor cells, which leads to changes in neural firing rates in the brain, where perceptions of taste quality, intensity, and palatability are generated. Most cells in gustatory nuclei are broadly tuned, so quality perception presumably arises from patterns of activity across neural populations. However, some manipulations affect only the most sugar-oriented cells, making it useful to consider them as a distinct neural subtype. Quality perception may also arise partly due to temporal patterns of activity to sugars, especially within sugar-oriented cells that give large but delayed responses. Non-specific gustatory neurons that are excited by both sugars and unpalatable stimuli project to ventral forebrain areas, where neural responses provide a closer match with behavioral preferences. This transition likely involves opposing excitatory and inhibitory influences by different subgroups of gustatory cells. Sweeteners are generally preferred over water, but the strength of this preference can vary across time or between individuals, and higher preferences for sugars are often associated with larger taste-evoked responses.

Origin and dynamics of extraclassical suppression in the lateral geniculate nucleus of the macaque monkey

In addition to the classical, center/surround receptive field of neurons in the lateral geniculate nucleus (LGN), there is an extraclassical, nonlinear surround that can strongly suppress LGN responses. This form of suppression likely plays an important role in adjusting the gain of LGN responses to visual stimuli. We performed experiments in alert and anesthetized macaque monkies to quantify extraclassical suppression in the LGN and determine the roles of feedforward and feedback pathways in the generation of LGN suppression. Results show that suppression is significantly stronger among magnocellular neurons than parvocellular neurons and that suppression arises too quickly for involvement from cortical feedback. Furthermore, the amount of suppression supplied by the retina is not significantly different from that in the LGN. These results indicate that extraclassical suppression in the macaque LGN relies on feedforward mechanisms and suggest that suppression in the cortex likely includes a component established in the retina.

Baseline shifts do not predict attentional modulation of target processing during feature-based visual attention

Cues that direct selective attention to a spatial location have been observed to increase baseline neural activity in visual areas that represent a to-be-attended stimulus location. Analogous attention-related baseline shifts have also been observed in response to attention-directing cues for non-spatial stimulus features. It has been proposed that baseline shifts with preparatory attention may serve as the mechanism by which attention modulates the responses to subsequent visual targets that match the attended location or feature. Using functional MRI, we localized color- and motion-sensitive visual areas in individual subjects and investigated the relationship between cue-induced baseline shifts and the subsequent attentional modulation of task-relevant target stimuli. Although attention-directing cues often led to increased background neural activity in feature specific visual areas, these increases were not correlated with either behavior in the task or subsequent attentional modulation of the visual targets. These findings cast doubt on the hypothesis that attention-related shifts in baseline neural activity result in selective sensory processing of visual targets during feature-based selective attention.

Properties of stimulus-dependent synchrony in retinal ganglion cells

Neighboring retinal ganglion cells often spike synchronously, but the possible function and mechanism of this synchrony is unclear. Recently, the strength of the fast correlation between ON-OFF directionally selective cells of the rabbit retina was shown to be stimulus dependent. Here, we extend that study, investigating stimulus-dependent correlation among multiple ganglion-cell classes, using multi-electrode recordings. Our results generalized those for directionally selective cells. All cell pairs exhibiting significant spike synchrony did it for an extended edge but rarely for full-field stimuli. The strength of this synchrony did not depend on the amplitude of the response and correlations could be present even when the cells' receptive fields did not overlap. In addition, correlations tended to be orientation selective in a manner predictable by the relative positions of the receptive fields. Finally, extended edges and full-field stimuli produced significantly greater and smaller correlations than predicted by chance respectively. We propose an amacrine-network model for the enhancement and depression of correlation. Such an apparently purposeful control of correlation adds evidence for retinal synchrony playing a functional role in vision.

Visual stimuli modulate precise synchronous firing within the thalamus

The work of Mircea Steriade demonstrated that the neocortex could synchronize large regions of the thalamus within 10-100 milliseconds (for review see Steriade and Timofeev, 2003, Steriade, 2005). Unlike the synchrony generated by the cortex, the retinal afferents synchronize a restricted group of neighboring thalamic neurons with <1-millisecond precision (Alonso et al., 1996, Yeh et al., 2003). Here, we use a large sample (n= 372) of simultaneous recordings from neighboring neurons in the Lateral Geniculate Nucleus (LGN) to illustrate the high specificity of the synchrony generated by retinal afferents and its dependency on sensory stimulation. First, we demonstrate that cells sharing a retinal afferent show a balanced receptive field diversity: while slight receptive field mismatches are common, the largest mismatches in a specific property (e.g. receptive field size) are restricted to cells that are precisely matched in other properties (e.g. receptive field overlap). Second, we show that these receptive field mismatches are functionally important and can lead to a 5-fold variation in the percentage of synchronous spikes driven by the shared retinal afferent under different stimulus conditions. Based on these and other findings, we speculate that the precise synchronous firing of cells sharing a retinal afferent could serve to amplify local stimuli that may be too brief and small to generate a large number of thalamic spikes.

Thalamic filtering of retinal spike trains by postsynaptic summation

At many synapses in the central nervous system, spikes within high-frequency trains have a better chance of driving the postsynaptic neuron than spikes occurring in isolation. We asked what mechanism accounts for this selectivity at the retinogeniculate synapse. The amplitude of synaptic potentials was remarkably constant, ruling out a major role for presynaptic mechanisms such as synaptic facilitation. Instead, geniculate spike trains could be predicted from retinal spike trains on the basis of postsynaptic summation. This simple form of integration explains the response differences between a geniculate neuron and its main retinal driver, and thereby determines the flow of visual information to cortex.

Cortical activity influences geniculocortical spike efficacy in the macaque monkey

Thalamocortical communication is a dynamic process influenced by both presynaptic and postsynaptic mechanisms. In this study, we recorded single-unit responses from cortical neurons that received direct input from the lateral geniculate nucleus (LGN) to address the question of whether prior patterns of cortical activity affect the ability of LGN inputs to drive cortical responses. By examining the ongoing activity that preceded the arrival of electrically evoked spikes from the LGN, we identified a number of activity patterns that were predictive of suprathreshold communication. Namely, cortical neurons were more likely to respond to LGN stimulation when their activity levels increased to 30-40Hz and/or their activity displayed rhythmic patterns (30 ms intervals) with increased power in the gamma frequency band. Cortical neurons were also more likely to respond to LGN stimulation when their activity increased 30-40 ms prior to stimulation, suggesting that the phase of gamma activity also contributes to geniculocortical communication. Based on these results, we conclude that ongoing activity in the cortex is not random, but rather organized in a manner that can influence the dynamics of thalamocortical communication.

Information processing in the primate retina: circuitry and coding

The function of any neural circuit is governed by connectivity of neurons in the circuit and the computations performed by the neurons. Recent research on retinal function has substantially advanced understanding in both areas. First, visual information is transmitted to the brain by at least 17 distinct retinal ganglion cell types defined by characteristic morphology, light response properties, and central projections. These findings provide a much more accurate view of the parallel visual pathways emanating from the retina than do previous models, and they highlight the importance of identifying distinct cell types and their connectivity in other neural circuits. Second, encoding of visual information involves significant temporal structure and interactions in the spike trains of retinal neurons. The functional importance of this structure is revealed by computational analysis of encoding and decoding, an approach that may be applicable to understanding the function of other neural circuits.

A simple model of retina-LGN transmission

To gain a deeper understanding of the transmission of visual signals from retina through the lateral geniculate nucleus (LGN), we have used a simple leaky integrate and-fire model to simulate a relay cell in the LGN. The simplicity of the model was motivated by two questions: (1) Can an LGN model that is driven by a retinal spike train recorded as synaptic ('S') potentials, but does not include a diverse array of ion channels, nor feedback inputs from the cortex, brainstem, and thalamic reticular nucleus, accurately simulate the LGN discharge on a spike-for-spike basis? (2) Are any special synaptic mechanisms, beyond simple summation of currents, necessary to model experimental recordings? We recorded cat relay cell responses to spatially homogeneous small or large spots, with luminance that was rapidly modulated in a pseudo-random fashion. Model parameters for each cell were optimized with a Simplex algorithm using a short segment of the recording. The model was then tested on a much longer, distinct data set consisting of responses to numerous repetitions of the noisy stimulus. For LGN cells that spiked in response to a sufficiently large fraction of retinal inputs, we found that this simplified model accurately predicted the firing times of LGN discharges. This suggests that modulations of the efficacy of the retino-geniculate synapse by pre-synaptic facilitation or depression are not necessary in order to account for the LGN responses generated by our stimuli, and that post-synaptic summation is sufficient.

Retinogeniculate Transmission in Wakefulness

Despite popular belief that the primary function of the thalamus is to “gate” sensory inputs by state, few studies have attempted to directly characterize the efficacy of such gating in the awake, behaving animal. I measured the efficacy of retinogeniculate transmission in the awake cat by taking advantage of the fact that many neurons in the lateral geniculate nucleus (LGN) are dominated by a single retinal input, and that this input produces a distinct event known as the S-potential. Retinal input failed to produce an LGN action potential half of the time. However, success or failure was powerfully tied to the recency of the S-potential. Short intervals tend to be successful and long intervals unsuccessful. For four of 12 neurons, the probability that a given S-potential could cause a spike exceeded 90% if that S-potential was preceded by an S-potential within the previous 10 ms (100 Hz). Whereas this temporal influence on efficacy has been demonstrated extensively in anesthetized animals, wakefulness is different in several ways. Overall efficacy is better in wakefulness than in anesthesia, the durations of facilitating effects are briefer in wakefulness, efficacy of long intervals is superior in wakefulness, and the temporal dependence can be briefly disrupted by altering background illumination. The last two observations may be particularly significant. Increased success at long intervals in wakefulness provides additional evidence that the spike code of the anesthetized animal is not the spike code of the awake animal. Altering retinogeniculate efficacy by altering visual conditions undermines the influence inter-S-potential interval might have in determining efficacy in the real world. Finally, S-potential amplitude, duration, and even slope are dynamic and systematic within wakefulness; providing further support that the S-potential is the extracellular signature of the retinal EPSP.