The use of m-sequences in the analysis of visual neurons: linear receptive field properties

Information-theoretic analyses of neural data to minimize the effect of researchers' assumptions in predictive coding studies

Studies investigating neural information processing often implicitly ask both, which processing strategy out of several alternatives is used and how this strategy is implemented in neural dynamics. A prime example are studies on predictive coding. These often ask whether confirmed predictions about inputs or prediction errors between internal predictions and inputs are passed on in a hierarchical neural system-while at the same time looking for the neural correlates of coding for errors and predictions. If we do not know exactly what a neural system predicts at any given moment, this results in a circular analysis-as has been criticized correctly. To circumvent such circular analysis, we propose to express information processing strategies (such as predictive coding) by local information-theoretic quantities, such that they can be estimated directly from neural data. We demonstrate our approach by investigating two opposing accounts of predictive coding-like processing strategies, where we quantify the building blocks of predictive coding, namely predictability of inputs and transfer of information, by local active information storage and local transfer entropy. We define testable hypotheses on the relationship of both quantities, allowing us to identify which of the assumed strategies was used. We demonstrate our approach on spiking data collected from the retinogeniculate synapse of the cat (N = 16). Applying our local information dynamics framework, we are able to show that the synapse codes for predictable rather than surprising input. To support our findings, we estimate quantities applied in the partial information decomposition framework, which allow to differentiate whether the transferred information is primarily bottom-up sensory input or information transferred conditionally on the current state of the synapse. Supporting our local information-theoretic results, we find that the synapse preferentially transfers bottom-up information.

DREDge: robust motion correction for high-density extracellular recordings across species

High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.

Cortical temporal integration can account for limits of temporal perception: investigations in the binaural system

The auditory system has exquisite temporal coding in the periphery which is transformed into a rate-based code in central auditory structures, like auditory cortex. However, the cortex is still able to synchronize, albeit at lower modulation rates, to acoustic fluctuations. The perceptual significance of this cortical synchronization is unknown. We estimated physiological synchronization limits of cortex (in humans with electroencephalography) and brainstem neurons (in chinchillas) to dynamic binaural cues using a novel system-identification technique, along with parallel perceptual measurements. We find that cortex can synchronize to dynamic binaural cues up to approximately 10 Hz, which aligns well with our measured limits of perceiving dynamic spatial information and utilizing dynamic binaural cues for spatial unmasking, i.e. measures of binaural sluggishness. We also find that the tracking limit for frequency modulation (FM) is similar to the limit for spatial tracking, demonstrating that this sluggish tracking is a more general perceptual limit that can be accounted for by cortical temporal integration limits.

Developmental alignment of feedforward inputs and recurrent network activity drives increased response selectivity and reliability in primary visual cortex following the onset of visual experience

Selective and reliable cortical sensory representations depend on synaptic interactions between feedforward inputs, conveying information from lower levels of the sensory pathway, and recurrent networks that reciprocally connect neurons functioning at the same hierarchical level. Here we explore the development of feedforward/recurrent interactions in primary visual cortex of the ferret that is responsible for the representation of orientation, focusing on the feedforward inputs from cortical layer 4 and its relation to the modular recurrent network in layer 2/3 before and after the onset of visual experience. Using simultaneous laminar electrophysiology and calcium imaging we found that in experienced animals, individual layer 4 and layer 2/3 neurons exhibit strongly correlated responses with the modular recurrent network structure in layer 2/3. Prior to experience, layer 2/3 neurons exhibit comparable modular correlation structure, but this correlation structure is missing for individual layer 4 neurons. Further analysis of the receptive field properties of layer 4 neurons in naïve animals revealed that they exhibit very poor orientation tuning compared to layer 2/3 neurons at this age, and this is accompanied by the lack of spatial segregation of ON and OFF subfields, the definitive property of layer 4 simple cells in experienced animals. Analysis of the response dynamics of layer 2/3 neurons with whole-cell patch recordings confirms that individual layer 2/3 neurons in naïve animals receive poorly-selective feedforward input that does not align with the orientation preference of the layer 2/3 responses. Further analysis reveals that the misaligned feedforward input is the underlying cause of reduced selectivity and increased response variability that is evident in the layer 2/3 responses of naïve animals. Altogether, our experiments indicate that the onset of visual experience is accompanied by a critical refinement in the responses of layer 4 neurons and the alignment of feedforward and recurrent networks that increases the selectivity and reliability of the representation of orientation in V1.

Error correction and improved precision of spike timing in converging cortical networks

The brain propagates neuronal signals accurately and rapidly. Nevertheless, whether and how a pool of cortical neurons transmits an undistorted message to a target remains unclear. We apply optogenetic white noise signals to small assemblies of cortical pyramidal cells (PYRs) in freely moving mice. The directly activated PYRs exhibit a spike timing precision of several milliseconds. Instead of losing precision, interneurons driven via synaptic activation exhibit higher precision with respect to the white noise signal. Compared with directly activated PYRs, postsynaptic interneuron spike trains allow better signal reconstruction, demonstrating error correction. Data-driven modeling shows that nonlinear amplification of coincident spikes can generate error correction and improved precision. Over multiple applications of the same signal, postsynaptic interneuron spiking is most reliable at timescales ten times shorter than those of the presynaptic PYR, exhibiting temporal coding. Similar results are observed in hippocampal region CA1. Coincidence detection of convergent inputs enables messages to be precisely propagated between cortical PYRs and interneurons.

•

PYR-to-interneuron spike transmission exhibits error correction and improved precision

•

Interneuron precision is higher when a larger pool of presynaptic PYRs is recruited

•

Error correction and improved precision are consistent with coincidence detection

•

Interneurons activated by synaptic transmission act as temporal coders

The impulse response of optic flow-sensitive descending neurons to roll m-sequences

When animals move through the world, their own movements generate widefield optic flow across their eyes. In insects, such widefield motion is encoded by optic lobe neurons. These lobula plate tangential cells (LPTCs) synapse with optic flow-sensitive descending neurons, which in turn project to areas that control neck, wing and leg movements. As the descending neurons play a role in sensorimotor transformation, it is important to understand their spatio-temporal response properties. Recent work shows that a relatively fast and efficient way to quantify such response properties is to use m-sequences or other white noise techniques. Therefore, here we used m-sequences to quantify the impulse responses of optic flow-sensitive descending neurons in male Eristalis tenax hoverflies. We focused on roll impulse responses as hoverflies perform exquisite head roll stabilizing reflexes, and the descending neurons respond particularly well to roll. We found that the roll impulse responses were fast, peaking after 16.5–18.0 ms. This is similar to the impulse response time to peak (18.3 ms) to widefield horizontal motion recorded in hoverfly LPTCs. We found that the roll impulse response amplitude scaled with the size of the stimulus impulse, and that its shape could be affected by the addition of constant velocity roll or lift. For example, the roll impulse response became faster and stronger with the addition of excitatory stimuli, and vice versa. We also found that the roll impulse response had a long return to baseline, which was significantly and substantially reduced by the addition of either roll or lift.

Summary: The impulse response of hoverfly optic flow-sensitive descending neurons to roll m-sequences reaches its time to peak within 20 ms and slowly returns to baseline over the next 100 ms.

Dissecting cascade computational components in spiking neural networks

Finding out the physical structure of neuronal circuits that governs neuronal responses is an important goal for brain research. With fast advances for large-scale recording techniques, identification of a neuronal circuit with multiple neurons and stages or layers becomes possible and highly demanding. Although methods for mapping the connection structure of circuits have been greatly developed in recent years, they are mostly limited to simple scenarios of a few neurons in a pairwise fashion; and dissecting dynamical circuits, particularly mapping out a complete functional circuit that converges to a single neuron, is still a challenging question. Here, we show that a recent method, termed spike-triggered non-negative matrix factorization (STNMF), can address these issues. By simulating different scenarios of spiking neural networks with various connections between neurons and stages, we demonstrate that STNMF is a persuasive method to dissect functional connections within a circuit. Using spiking activities recorded at neurons of the output layer, STNMF can obtain a complete circuit consisting of all cascade computational components of presynaptic neurons, as well as their spiking activities. For simulated simple and complex cells of the primary visual cortex, STNMF allows us to dissect the pathway of visual computation. Taken together, these results suggest that STNMF could provide a useful approach for investigating neuronal systems leveraging recorded functional neuronal activity.

It is well known that the computation of neuronal circuits is carried out through the staged and cascade structure of different types of neurons. Nevertheless, the information, particularly sensory information, is processed in a network primarily with feedforward connections through different pathways. A peculiar example is the early visual system, where light is transcoded by the retinal cells, routed by the lateral geniculate nucleus, and reached the primary visual cortex. One meticulous interest in recent years is to map out these physical structures of neuronal pathways. However, most methods so far are limited to taking snapshots of a static view of connections between neurons. It remains unclear how to obtain a functional and dynamical neuronal circuit beyond the simple scenarios of a few randomly sampled neurons. Using simulated spiking neural networks of visual pathways with different scenarios of multiple stages, mixed cell types, and natural image stimuli, we demonstrate that a recent computational tool, named spike-triggered non-negative matrix factorization, can resolve these issues. It enables us to recover the entire structural components of neural networks underlying the computation, together with the functional components of each individual neuron. Utilizing it for complex cells of the primary visual cortex allows us to reveal every underpinning of the nonlinear computation. Our results, together with other recent experimental and computational efforts, show that it is possible to systematically dissect neural circuitry with detailed structural and functional components.

A Time-Varying Information Measure for Tracking Dynamics of Neural Codes in a Neural Ensemble

The amount of information that differentially correlated spikes in a neural ensemble carry is not the same; the information of different types of spikes is associated with different features of the stimulus. By calculating a neural ensemble’s information in response to a mixed stimulus comprising slow and fast signals, we show that the entropy of synchronous and asynchronous spikes are different, and their probability distributions are distinctively separable. We further show that these spikes carry a different amount of information. We propose a time-varying entropy (TVE) measure to track the dynamics of a neural code in an ensemble of neurons at each time bin. By applying the TVE to a multiplexed code, we show that synchronous and asynchronous spikes carry information in different time scales. Finally, a decoder based on the Kalman filtering approach is developed to reconstruct the stimulus from the spikes. We demonstrate that slow and fast features of the stimulus can be entirely reconstructed when this decoder is applied to asynchronous and synchronous spikes, respectively. The significance of this work is that the TVE can identify different types of information (for example, corresponding to synchronous and asynchronous spikes) that might simultaneously exist in a neural code.

Synaptic and intrinsic mechanisms underlying development of cortical direction selectivity

Modifications of synaptic inputs and cell-intrinsic properties both contribute to neuronal plasticity and development. To better understand these mechanisms, we undertook an intracellular analysis of the development of direction selectivity in the ferret visual cortex, which occurs rapidly over a few days after eye opening. We found strong evidence of developmental changes in linear spatiotemporal receptive fields of simple cells, implying alterations in circuit inputs. Further, this receptive field plasticity was accompanied by increases in near-spike-threshold excitability and input-output gain that resulted in dramatically increased spiking responses in the experienced state. Increases in subthreshold membrane responses induced by the receptive field plasticity and the increased input-output spiking gain were both necessary to explain the elevated firing rates in experienced ferrets. These results demonstrate that cortical direction selectivity develops through a combination of plasticity in inputs and in cell-intrinsic properties.

Extraretinal Spike Normalization in Retinal Ganglion Cell Axons

Spike conduction velocity characteristically differs between myelinated and unmyelinated axons. Here we test whether spikes of myelinated and unmyelinated paths differ in other respects by measuring rat retinal ganglion cell (RGC) spike duration in the intraretinal, unmyelinated nerve fiber layer and the extraretinal, myelinated optic nerve and optic chiasm. We find that rapid spike firing and illumination broaden spikes in intraretinal axons but not in extraretinal axons. RGC axons thus initiate spikes intraretinally and normalize spike duration extraretinally. Additionally, we analyze spikes that were recorded in a previous study of rhesus macaque retinogeniculate transmission and find that rapid spike firing does not broaden spikes in optic tract. The spike normalization we find reduces the number of spike properties that can change during RGC light responses. However, this is not because identical spikes fire in all axons. Instead, our recordings show that different subtypes of RGC generate axonal spikes of different durations and that the differences resemble spike duration increases that alter neurotransmitter release from other neurons. Moreover, previous studies have shown that RGC spikes of shorter duration can fire at higher maximum frequencies. These properties should facilitate signal transfer by different mechanisms at RGC synapses onto subcortical target neurons.

Spatial frequency selectivity of the human visual cortex estimated with pseudo-random visual evoked cortical potential (VECP)

Single-cell recordings in the primary visual cortex (V1) show neurons with spatial frequency (SF) tuning, which had different responses to chromatic and luminance stimuli. Visually evoked cortical potential (VECP) investigations have reported different spatial profiles. The current study aimed to investigate the spatial selectivity of V1 to simultaneous stimulus of chromatic and luminance contrasts. Compound stimuli temporally driven by m-sequences at 8 SFs were utilized to generate VECP records from thirty subjects (14 trichromats and 16 colorblind subjects). We extracted the second-order kernel, first and second slices (K2.1 and K2.2, respectively). Optimal SF, SF bandwidth, and high SF cut-off were estimated from the best-fitted functions to the VECP amplitude vs SF. For trichromats, K2.1 waveforms had a negative component (N1 K2.1) at 100 ms followed by a positive component (P1 K2.1). K2.2 waveforms also had a negative component (N1 K2.2) at 100 ms followed by a positive deflection (P1 K2.2). SF tuning of N1 K2.1 and N1 K2.2 had a band-pass profile, while the P1 K2.1 was low-pass tuned. P1 K2.1 optimal SF differed significantly from both other negative responses and from P1 K2.2. We found differences in the optimal SF, SF tuning and high SF cut-off among the VECP components. Dichromats had little or no response for all stimulus conditions. The absence of the responses in dichromats, the similarity between the high SF cut-off of the pseudorandom VECPs and psychophysical chromatic visual acuity, and presence of multiple SF tunings suggested that pseudorandom VECPs represented the activity of cells that responded preferentially to the chromatic component of the compound stimuli.

Data-Driven Approaches to Understanding Visual Neuron Activity

With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming limited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However, models that are able to more accurately reproduce observed neural activity often defy simple interpretations. As a result, rather than being used solely to connect with existing theories of visual processing, statistical modeling will increasingly drive the evolution of more sophisticated theories.

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.

The Augmentation of Retinogeniculate Communication during Thalamic Burst Mode

Retinal signals are transmitted to cortex via neurons in the lateral geniculate nucleus (LGN), where they are processed in burst or tonic response mode. Burst mode occurs when LGN neurons are sufficiently hyperpolarized for T-type Ca²⁺ channels to deinactivate, allowing them to open in response to depolarization, which can trigger a high-frequency sequence of Na⁺-based spikes (i.e., burst). In contrast, T-type channels are inactivated during tonic mode and do not contribute to spiking. Although burst mode is commonly associated with sleep and the disruption of retinogeniculate communication, bursts can also be triggered by visual stimulation, thereby transforming the retinal signals relayed to the cortex. To determine how burst mode affects retinogeniculate communication, we made recordings from monosynaptically connected retinal ganglion cells and LGN neurons in male/female cats during visual stimulation. Our results reveal a robust augmentation of retinal signals within the LGN during burst mode. Specifically, retinal spikes were more effective and often triggered multiple LGN spikes during periods likely to have increased T-type Ca²⁺ channel activity. Consistent with the biophysical properties of T-type Ca²⁺ channels, analysis revealed that effect magnitude was correlated with the duration of the preceding thalamic interspike interval and occurred even in the absence of classically defined bursts. Importantly, the augmentation of geniculate responses to retinal input was not associated with a degradation of visual signals. Together, these results indicate a graded nature of response mode and suggest that, under certain conditions, bursts facilitate the transmission of visual information to the cortex by amplifying retinal signals.SIGNIFICANCE STATEMENT The thalamus is the gateway for retinal information traveling to the cortex. The lateral geniculate nucleus, like all thalamic nuclei, has two classically defined categories of spikes-tonic and burst-that differ in their underlying cellular mechanisms. Here we compare retinogeniculate communication during burst and tonic response modes. Our results show that retinogeniculate communication is enhanced during burst mode and visually evoked thalamic bursts, thereby augmenting retinal signals transmitted to cortex. Further, our results demonstrate that the influence of burst mode on retinogeniculate communication is graded and can be measured even in the absence of classically defined thalamic bursts.

Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network

A comprehensive understanding of the stimulus-response properties of individual neurons is necessary to crack the neural code of sensory cortices. However, a barrier to achieving this goal is the difficulty of analysing the nonlinearity of neuronal responses. Here, by incorporating convolutional neural network (CNN) for encoding models of neurons in the visual cortex, we developed a new method of nonlinear response characterisation, especially nonlinear estimation of receptive fields (RFs), without assumptions regarding the type of nonlinearity. Briefly, after training CNN to predict the visual responses to natural images, we synthesised the RF image such that the image would predictively evoke a maximum response. We first demonstrated the proof-of-principle using a dataset of simulated cells with various types of nonlinearity. We could visualise RFs with various types of nonlinearity, such as shift-invariant RFs or rotation-invariant RFs, suggesting that the method may be applicable to neurons with complex nonlinearities in higher visual areas. Next, we applied the method to a dataset of neurons in mouse V1. We could visualise simple-cell-like or complex-cell-like (shift-invariant) RFs and quantify the degree of shift-invariance. These results suggest that CNN encoding model is useful in nonlinear response analyses of visual neurons and potentially of any sensory neurons.

Introducing chaotic codes for the modulation of code modulated visual evoked potentials (c-VEP) in normal adults for visual fatigue reduction

Code modulated Visual Evoked Potentials (c-VEP) based BCI studies usually employ m-sequences as a modulating codes for their broadband spectrum and correlation property. However, subjective fatigue of the presented codes has been a problem. In this study, we introduce chaotic codes containing broadband spectrum and similar correlation property. We examined whether the introduced chaotic codes could be decoded from EEG signals and also compared the subjective fatigue level with m-sequence codes in normal subjects. We generated chaotic code from one-dimensional logistic map and used it with conventional 31-bit m-sequence code. In a c-VEP based study in normal subjects (n = 44, 21 females) we presented these codes visually and recorded EEG signals from the corresponding codes for their four lagged versions. Canonical correlation analysis (CCA) and spatiotemporal beamforming (STB) methods were used for target identification and comparison of responses. Additionally, we compared the subjective self-declared fatigue using VAS caused by presented m-sequence and chaotic codes. The introduced chaotic code was decoded from EEG responses with CCA and STB methods. The maximum total accuracy values of 93.6 ± 11.9% and 94 ± 14.4% were achieved with STB method for chaotic and m-sequence codes for all subjects respectively. The achieved accuracies in all subjects were not significantly different in m-sequence and chaotic codes. There was significant reduction in subjective fatigue caused by chaotic codes compared to the m-sequence codes. Both m-sequence and chaotic codes were similar in their accuracies as evaluated by CCA and STB methods. The chaotic codes significantly reduced subjective fatigue compared to the m-sequence codes.

Temporal Limits of Visual Motion Processing: Psychophysics and Neurophysiology

Under optimal conditions, just 3–6 ms of visual stimulation suffices for humans to see motion. Motion perception on this timescale implies that the visual system under these conditions reliably encodes, transmits, and processes neural signals with near-millisecond precision. Motivated by in vitro evidence for high temporal precision of motion signals in the primate retina, we investigated how neuronal and perceptual limits of motion encoding relate. Specifically, we examined the correspondence between the time scale at which cat retinal ganglion cells in vivo represent motion information and temporal thresholds for human motion discrimination. The timescale for motion encoding by ganglion cells ranged from 4.6 to 91 ms, and depended non-linearly on temporal frequency, but not on contrast. Human psychophysics revealed that minimal stimulus durations required for perceiving motion direction were similarly brief, 5.6–65 ms, and similarly depended on temporal frequency but, above ~10%, not on contrast. Notably, physiological and psychophysical measurements corresponded closely throughout (r = 0.99), despite more than a 20-fold variation in both human thresholds and optimal timescales for motion encoding in the retina. The match in absolute values of the neurophysiological and psychophysical data may be taken to indicate that from the lateral geniculate nucleus (LGN) through to the level of perception little temporal precision is lost. However, we also show that integrating responses from multiple neurons can improve temporal resolution, and this potential trade-off between spatial and temporal resolution would allow for loss of temporal resolution after the LGN. While the extent of neuronal integration cannot be determined from either our human psychophysical or neurophysiological experiments and its contribution to the measured temporal resolution is unknown, our results demonstrate a striking similarity in stimulus dependence between the temporal fidelity established in the retina and the temporal limits of human motion discrimination.

The dynamic receptive fields of retinal ganglion cells

Retinal ganglion cells (RGCs) were one of the first classes of sensory neurons to be described in terms of a receptive field (RF). Over the last six decades, our understanding of the diversity of RGC types and the nuances of their response properties has grown exponentially. We will review the current understanding of RGC RFs mostly from studies in mammals, but including work from other vertebrates as well. We will argue for a new paradigm that embraces the fluidity of RGC RFs with an eye toward the neuroethology of vision. Specifically, we will focus on (1) different methods for measuring RGC RFs, (2) RF models, (3) feature selectivity and the distinction between fluid and stable RF properties, and (4) ideas about the future of understanding RGC RFs.

Relationship between the Dynamics of Orientation Tuning and Spatiotemporal Receptive Field Structures of Cat LGN Neurons

Simple cells in the cat primary visual cortex usually have elongated receptive fields (RFs), and their orientation selectivity can be largely predicted by their RFs. As to the relay cells in cats' lateral geniculate nucleus (LGN), they also have weak but significant orientation bias (OB). It is thus of interest to investigate the fine spatiotemporal receptive field (STRF) properties in LGN, compare them with the dynamics of orientation tuning, and examine the dynamic relationship between STRF and orientation sensitivity in LGN. We mapped the STRFs of the LGN neurons in cats with white noise and characterized the dynamics of the orientation tuning by flashing gratings. We found that most of the LGN neurons showed elongated RFs and that the elongation axes were consistent with the preferred orientations. STRFs and the dynamics of orientation tuning were closely correlated temporally: the elongation of RFs and OB emerged, peaked and decayed at the same pace, with unchanged elongation axis of RF and preferred orientation but consistently changing aspect ratio of RF and OB strength across time. Importantly, the above consistency between RF and orientation tuning was not influenced by the ablation of the primary visual cortex. Furthermore, biased orientation tuning emerged 20-30 ms earlier than those in the primary visual cortex. These data demonstrated that similar to the primary visual cortex, the orientation sensitivity was closely reflected by the RF properties in LGN. However, the elongated RF and OB in LGN did not originate from the primary visual cortex feedback.

Orientation Tuning of Correlated Activity in the Developing Lateral Geniculate Nucleus

Neural circuits and the cells that comprise them undergo developmental changes in the spatial organization of their connections and in their temporal response properties. Within the lateral geniculate nucleus (LGN) of the dorsal thalamus, these changes have pronounced effects on the spatiotemporal receptive fields (STRFs) of neurons. An open and unresolved question is how STRF maturation affects stimulus-evoked correlated activity between pairs of LGN neurons during development. This is an important question to answer because stimulus-evoked correlated activity likely plays a role in establishing the specificity of thalamocortical connectivity and the receptive fields (RFs) of postsynaptic cortical neurons. Using multielectrode recording methods and white noise stimuli, we recorded neural activity from ensembles of LGN neurons in cats across early development. As expected, there was a progressive maturation of the spatial and temporal properties of visual responses. Using drifting bar stimuli and cross-correlation analysis, we also determined the orientation-tuning bandwidth of correlated activity between pairs of LGN neurons at different stages of development (Sillito and Jones, 2002; Andolina et al., 2007; Stanley et al., 2012; Kelly et al., 2014). Despite the larger RFs and slower responses of immature LGN neurons compared with mature neurons, our results show that correlated activity in the LGN was as tightly tuned for orientation early in development as it was in the adult. Closer examination revealed this age-invariant orientation tuning of correlated activity likely involves cellular mechanisms related to spike fatigue in young animals and a progressive decrease in response latency with development.SIGNIFICANCE STATEMENT Orientation tuning is a fundamental property of neurons in primary visual cortex. An important and unresolved question is how orientation tuning emerges during brain development. This study explores a potential mechanism for the establishment of orientation tuning based on correlated activity patterns among ensembles of maturing neurons in the lateral geniculate nucleus (LGN) of the thalamus. Results show that correlated activity between pairs of LGN neurons is more tightly tuned than predictions based simply on receptive field size, indicating that correlated activity has the properties needed to play an important role in the development of geniculocortical circuits and the emergence of cortical orientation tuning.

Space-time codependence of retinal ganglion cells can be explained by novel and separable components of their receptive fields

Reverse correlation methods such as spike‐triggered averaging consistently identify the spatial center in the linear receptive fields (RFs) of retinal ganglion cells (GCs). However, the spatial antagonistic surround observed in classical experiments has proven more elusive. Tests for the antagonistic surround have heretofore relied on models that make questionable simplifying assumptions such as space–time separability and radial homogeneity/symmetry. We circumvented these, along with other common assumptions, and observed a linear antagonistic surround in 754 of 805 mouse GCs. By characterizing the RF's space–time structure, we found the overall linear RF's inseparability could be accounted for both by tuning differences between the center and surround and differences within the surround. Finally, we applied this approach to characterize spatial asymmetry in the RF surround. These results shed new light on the spatiotemporal organization of GC linear RFs and highlight a major contributor to its inseparability.

Nonlinear dynamics of cortical responses to color in the human cVEP

The main finding of this paper is that the human visual cortex responds in a very nonlinear manner to the color contrast of pure color patterns. We examined human cortical responses to color checkerboard patterns at many color contrasts, measuring the chromatic visual evoked potential (cVEP) with a dense electrode array. Cortical topography of the cVEPs showed that they were localized near the posterior electrode at position Oz, indicating that the primary cortex (V1) was the major source of responses. The choice of fine spatial patterns as stimuli caused the cVEP response to be driven by double-opponent neurons in V1. The cVEP waveform revealed nonlinear color signal processing in the V1 cortex. The cVEP time-to-peak decreased and the waveform's shape was markedly narrower with increasing cone contrast. Comparison of the linear dynamics of retinal and lateral geniculate nucleus responses with the nonlinear dynamics of the cortical cVEP indicated that the nonlinear dynamics originated in the V1 cortex. The nature of the nonlinearity is a kind of automatic gain control that adjusts cortical dynamics to be faster when color contrast is greater.

Retinal and Nonretinal Contributions to Extraclassical Surround Suppression in the Lateral Geniculate Nucleus

Extraclassical surround suppression is a prominent receptive field property of neurons in the lateral geniculate nucleus (LGN) of the dorsal thalamus, influencing stimulus size tuning, response gain control, and temporal features of visual responses. Despite evidence for the involvement of both retinal and nonretinal circuits in the generation of extraclassical suppression, we lack an understanding of the relative roles played by these pathways and how they interact during visual stimulation. To determine the contribution of retinal and nonretinal mechanisms to extraclassical suppression in the feline, we made simultaneous single-unit recordings from synaptically connected retinal ganglion cells and LGN neurons and measured the influence of stimulus size on the spiking activity of presynaptic and postsynaptic neurons. Results show that extraclassical suppression is significantly stronger for LGN neurons than for their retinal inputs, indicating a role for extraretinal mechanisms. Further analysis revealed that the enhanced suppression can be accounted for by mechanisms that suppress the effectiveness of retinal inputs in evoking LGN spikes. Finally, an examination of the time course for the onset of extraclassical suppression in the LGN and the size-dependent modulation of retinal spike efficacy suggests the early phase of augmented suppression involves local thalamic circuits. Together, these results demonstrate that the LGN is much more than a simple relay for retinal signals to cortex; it also filters retinal spikes dynamically on the basis of stimulus statistics to adjust the gain of visual signals delivered to cortex.

SIGNIFICANCE STATEMENT

The lateral geniculate nucleus (LGN) is the gateway through which retinal information reaches the cerebral cortex. Within the LGN, neuronal responses are often suppressed by stimuli that extend beyond the classical receptive field. This form of suppression, called extraclassical suppression, serves to adjust the size tuning, response gain, and temporal response properties of neurons. Given the important influence of extraclassical suppression on visual signals delivered to cortex, we performed experiments to determine the circuit mechanisms that contribute to extraclassical suppression in the LGN. Results show that suppression is augmented beyond that provided by direct retinal inputs and delayed, consistent with polysynaptic inhibition. Importantly, these mechanisms influence the effectiveness of incoming retinal signals, thereby filtering the signals ultimately conveyed to cortex.

Modulating Lateral Geniculate Nucleus Neuronal Firing for Visual Prostheses: A Kalman Filter-Based Strategy

Developing visual prostheses that target inner brain structures along the visual pathway represent a new hope for patients with completely damaged early visual pathway sites. One of the major challenges in the development of subcortical and cortical visual prostheses is tuning electrical stimulation that could optimally induce desired visual percepts. In this paper, we propose a Kalman filter-based strategy that could be used to identify electrical stimulation patterns that mimic a specific visual input for thalamic visual prostheses. We demonstrate the performance of the proposed strategy using a population of lateral geniculate nucleus neurons modeled using an adapted generalized non-linear model. A mean correlation of 0.69 is obtained between visually evoked and electrically evoked responses-driven using the proposed strategy-for an optimal electrode-placement setup. In addition, we demonstrate the performance for a random electrode-placement setup in which a mean correlation of 0.26 is obtained. For this latter setup, our analysis reveals an inversely proportional relationship between the obtained correlation and the distance between each neuron and the nearest electrode. The proposed strategy could be thus utilized to tune and enhance the performance of thalamic visual prostheses as well as other prosthesis systems.

Breaking the Excitation-Inhibition Balance Makes the Cortical Network's Space-Time Dynamics Distinguish Simple Visual Scenes

Brain dynamics are often taken to be temporal dynamics of spiking and membrane potentials in a balanced network. Almost all evidence for a balanced network comes from recordings of cell bodies of few single neurons, neglecting more than 99% of the cortical network. We examined the space-time dynamics of excitation and inhibition simultaneously in dendrites and axons over four visual areas of ferrets exposed to visual scenes with stationary and moving objects. The visual stimuli broke the tight balance between excitation and inhibition such that the network exhibited longer episodes of net excitation subsequently balanced by net inhibition, in contrast to a balanced network. Locally in all four areas the amount of net inhibition matched the amount of net excitation with a delay of 125 ms. The space-time dynamics of excitation-inhibition evolved to reduce the complexity of neuron interactions over the whole network to a flow on a low-(3)-dimensional manifold within 80 ms. In contrast to the pure temporal dynamics, the low dimensional flow evolved to distinguish the simple visual scenes.

The multifunctional lateral geniculate nucleus

Providing the critical link between the retina and visual cortex, the well-studied lateral geniculate nucleus (LGN) has stood out as a structure in search of a function exceeding the mundane 'relay'. For many mammals, it is structurally impressive: Exquisite lamination, sophisticated microcircuits, and blending of multiple inputs suggest some fundamental transform. This impression is bolstered by the fact that numerically, the retina accounts for a small fraction of its input. Despite such promise, the extent to which an LGN neuron separates itself from its retinal brethren has proven difficult to appreciate. Here, I argue that whereas retinogeniculate coupling is strong, what occurs in the LGN is judicious pruning of a retinal drive by nonretinal inputs. These nonretinal inputs reshape a receptive field that under the right conditions departs significantly from its retinal drive, even if transiently. I first review design features of the LGN and follow with evidence for 10 putative functions. Only two of these tend to surface in textbooks: parsing retinal axons by eye and functional group and gating by state. Among the remaining putative functions, implementation of the principle of graceful degradation and temporal decorrelation are at least as interesting but much less promoted. The retina solves formidable problems imposed by physics to yield multiple efficient and sensitive representations of the world. The LGN applies context, increasing content, and gates several of these representations. Even if the basic concentric receptive field remains, information transmitted for each LGN spike relative to each retinal spike is measurably increased.

The Second Spiking Threshold: Dynamics of Laminar Network Spiking in the Visual Cortex

Most neurons have a threshold separating the silent non-spiking state and the state of producing temporal sequences of spikes. But neurons in vivo also have a second threshold, found recently in granular layer neurons of the primary visual cortex, separating spontaneous ongoing spiking from visually evoked spiking driven by sharp transients. Here we examine whether this second threshold exists outside the granular layer and examine details of transitions between spiking states in ferrets exposed to moving objects. We found the second threshold, separating spiking states evoked by stationary and moving visual stimuli from the spontaneous ongoing spiking state, in all layers and zones of areas 17 and 18 indicating that the second threshold is a property of the network. Spontaneous and evoked spiking, thus can easily be distinguished. In addition, the trajectories of spontaneous ongoing states were slow, frequently changing direction. In single trials, sharp as well as smooth and slow transients transform the trajectories to be outward directed, fast and crossing the threshold to become evoked. Although the speeds of the evolution of the evoked states differ, the same domain of the state space is explored indicating uniformity of the evoked states. All evoked states return to the spontaneous evoked spiking state as in a typical mono-stable dynamical system. In single trials, neither the original spiking rates, nor the temporal evolution in state space could distinguish simple visual scenes.

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.

Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes

Accurate estimation of neuronal receptive fields is essential for understanding sensory processing in the early visual system. Yet a full characterization of receptive fields is still incomplete, especially with regard to natural visual stimuli and in complete populations of cortical neurons. While previous work has incorporated known structural properties of the early visual system, such as lateral connectivity, or imposing simple-cell-like receptive field structure, no study has exploited the fact that nearby V1 neurons share common feed-forward input from thalamus and other upstream cortical neurons. We introduce a new method for estimating receptive fields simultaneously for a population of V1 neurons, using a model-based analysis incorporating knowledge of the feed-forward visual hierarchy. We assume that a population of V1 neurons shares a common pool of thalamic inputs, and consists of two layers of simple and complex-like V1 neurons. When fit to recordings of a local population of mouse layer 2/3 V1 neurons, our model offers an accurate description of their response to natural images and significant improvement of prediction power over the current state-of-the-art methods. We show that the responses of a large local population of V1 neurons with locally diverse receptive fields can be described with surprisingly limited number of thalamic inputs, consistent with recent experimental findings. Our structural model not only offers an improved functional characterization of V1 neurons, but also provides a framework for studying the relationship between connectivity and function in visual cortical areas.

A Probabilistic Approach to Receptive Field Mapping in the Frontal Eye Fields

Studies of the neuronal mechanisms of perisaccadic vision often lack the resolution needed to determine important changes in receptive field (RF) structure. Such limited analytical power can lead to inaccurate descriptions of visuomotor processing. To address this issue, we developed a precise, probabilistic technique that uses a generalized linear model (GLM) for mapping the visual RFs of frontal eye field (FEF) neurons during stable fixation (Mayo et al., 2015). We previously found that full-field RF maps could be obtained using 1–8 dot stimuli presented at frame rates of 10–150 ms. FEF responses were generally robust to changes in the number of stimuli presented or the rate of presentation, which allowed us to visualize RFs over a range of spatial and temporal resolutions. Here, we compare the quality of RFs obtained over different stimulus and GLM parameters to facilitate future work on the detailed mapping of FEF RFs. We first evaluate the interactions between the number of stimuli presented per trial, the total number of trials, and the quality of RF mapping. Next, we vary the spatial resolution of our approach to illustrate the tradeoff between visualizing RF sub-structure and sampling at high resolutions. We then evaluate local smoothing as a possible correction for situations where under-sampling occurs. Finally, we provide a preliminary demonstration of the usefulness of a probabilistic approach for visualizing full-field perisaccadic RF shifts. Our results present a powerful, and perhaps necessary, framework for studying perisaccadic vision that is applicable to FEF and possibly other visuomotor regions of the brain.

Temporal and spatial tuning of dorsal lateral geniculate nucleus neurons in unanesthetized rats

Visual response properties of neurons in the dorsolateral geniculate nucleus (dLGN) have been well described in several species, but not in rats. Analysis of responses from the unanesthetized rat dLGN will be needed to develop quantitative models that account for visual behavior of rats. We recorded visual responses from 130 single units in the dLGN of 7 unanesthetized rats. We report the response amplitudes, temporal frequency, and spatial frequency sensitivities in this population of cells. In response to 2-Hz visual stimulation, dLGN cells fired 15.9 ± 11.4 spikes/s (mean ± SD) modulated by 10.7 ± 8.4 spikes/s about the mean. The optimal temporal frequency for full-field stimulation ranged from 5.8 to 19.6 Hz across cells. The temporal high-frequency cutoff ranged from 11.7 to 33.6 Hz. Some cells responded best to low temporal frequency stimulation (low pass), and others were strictly bandpass; most cells fell between these extremes. At 2- to 4-Hz temporal modulation, the spatial frequency of drifting grating that drove cells best ranged from 0.008 to 0.18 cycles per degree (cpd) across cells. The high-frequency cutoff ranged from 0.01 to 1.07 cpd across cells. The majority of cells were driven best by the lowest spatial frequency tested, but many were partially or strictly bandpass. We conclude that single units in the rat dLGN can respond vigorously to temporal modulation up to at least 30 Hz and spatial detail up to 1 cpd. Tuning properties were heterogeneous, but each fell along a continuum; we found no obvious clustering into discrete cell types along these dimensions.

Stimulus Contrast and Retinogeniculate Signal Processing

Neuronal signals conveying luminance contrast play a key role in nearly all aspects of perception, including depth perception, texture discrimination, and motion perception. Although much is known about the retinal mechanisms responsible for encoding contrast information, relatively little is known about the relationship between stimulus contrast and the processing of neuronal signals between visual structures. Here, we describe simultaneous recordings from monosynaptically connected retinal ganglion cells and lateral geniculate nucleus (LGN) neurons in the cat to determine how stimulus contrast affects the communication of visual signals between the two structures. Our results indicate that: (1) LGN neurons typically reach their half-maximal response at lower contrasts than their individual retinal inputs and (2) LGN neurons exhibit greater contrast-dependent phase advance (CDPA) than their retinal inputs. Further analyses suggests that increased sensitivity relies on spatial convergence of multiple retinal inputs, while increased CDPA is achieved, in part, on temporal summation of arriving signals.

Increasing Spontaneous Retinal Activity before Eye Opening Accelerates the Development of Geniculate Receptive Fields

Visually evoked activity is necessary for the normal development of the visual system. However, little is known about the capacity for patterned spontaneous activity to drive the maturation of receptive fields before visual experience. Retinal waves provide instructive retinotopic information for the anatomical organization of the visual thalamus. To determine whether retinal waves also drive the maturation of functional responses, we increased the frequency of retinal waves pharmacologically in the ferret (Mustela putorius furo) during a period of retinogeniculate development before eye opening. The development of geniculate receptive fields after receiving these increased neural activities was measured using single-unit electrophysiology. We found that increased retinal waves accelerate the developmental reduction of geniculate receptive field sizes. This reduction is due to a decrease in receptive field center size rather than an increase in inhibitory surround strength. This work reveals an instructive role for patterned spontaneous activity in guiding the functional development of neural circuits.

Retinal waves regulate afferent terminal targeting in the early visual pathway

Current models of retinogeniculate development have proposed that connectivity between the retina and the dorsal lateral geniculate nucleus (dLGN) is established by gradients of axon guidance molecules, to allow initial coarse connections, and by competitive Hebbian-like processes, to drive eye-specific segregation and refine retinotopy. Here we show that when intereye competition is eliminated by monocular enucleation, blocking cholinergic stage II retinal waves disrupts the intraeye competition-mediated expansion of the retinogeniculate projection and results in the permanent disorganization of its laminae. This disruption of stage II retinal waves also causes long-term impacts on receptive field size and fine-scale retinotopy in the dLGN. Our results reveal a novel role for stage II retinal waves in regulating retinogeniculate afferent terminal targeting by way of intraeye competition, allowing for correct laminar patterning and the even allocation of synaptic territory. These findings should contribute to answering questions regarding the role of neural activity in guiding the establishment of neural circuits.

Visual processing of informative multipoint correlations arises primarily in V2

Using the visual system as a model, we recently showed that the efficient coding principle accounted for the allocation of computational resources in central sensory processing: when sampling an image is the main limitation, resources are devoted to compute the statistical features that are the most variable, and therefore the most informative (eLife 2014;3:e03722. DOI: 10.7554/eLife.03722 Hermundstad et al., 2014). Building on these results, we use single-unit recordings in the macaque monkey to determine where these computations—sensitivity to specific multipoint correlations—occur. We find that these computations take place in visual area V2, primarily in its supragranular layers. The demonstration that V2 neurons are sensitive to the multipoint correlations that are informative about natural images provides a common computational underpinning for diverse but well-recognized aspects of neural processing in V2, including its sensitivity to corners, junctions, illusory contours, figure/ground, and ‘naturalness.’

DOI:http://dx.doi.org/10.7554/eLife.06604.001

Weak orientation and direction selectivity in lateral geniculate nucleus representing central vision in the gray squirrel Sciurus carolinensis

Classic studies of lateral geniculate nucleus (LGN) and visual cortex (V1) in carnivores and primates have found that a majority of neurons in LGN exhibit a center-surround organization, while V1 neurons exhibit strong orientation selectivity and, in many species, direction selectivity. Recent work in the mouse and the monkey has discovered previously unknown classes of orientation- and direction-selective neurons in LGN. Furthermore, some recent studies in the mouse report that many LGN cells exhibit pronounced orientation biases that are of comparable strength to the subthreshold inputs to V1 neurons. These results raise the possibility that, in rodents, orientation biases of individual LGN cells make a substantial contribution to cortical orientation selectivity. Alternatively, the size and contribution of orientation- or direction-selective channels from LGN to V1 may vary across mammals. To address this question, we examined orientation and direction selectivity in LGN and V1 neurons of a highly visual diurnal rodent: the gray squirrel. In the representation of central vision, only a few LGN neurons exhibited strong orientation or direction selectivity. Across the population, LGN neurons showed weak orientation biases and were much less selective for orientation compared with V1 neurons. Although direction selectivity was weak overall, LGN layers 3abc, which contain neurons that express calbindin, exhibited elevated direction selectivity index values compared with LGN layers 1 and 2. These results suggest that, for central visual fields, the contribution of orientation- and direction-selective channels from the LGN to V1 is small in the squirrel. As in other mammals, this small contribution is elevated in the calbindin-positive layers of the LGN.

Monitoring activity in neural circuits with genetically encoded indicators

Recent developments in genetically encoded indicators of neural activity (GINAs) have greatly advanced the field of systems neuroscience. As they are encoded by DNA, GINAs can be targeted to genetically defined cellular populations. Combined with fluorescence microscopy, most notably multi-photon imaging, GINAs allow chronic simultaneous optical recordings from large populations of neurons or glial cells in awake, behaving mammals, particularly rodents. This large-scale recording of neural activity at multiple temporal and spatial scales has greatly advanced our understanding of the dynamics of neural circuitry underlying behavior—a critical first step toward understanding the complexities of brain function, such as sensorimotor integration and learning. Here, we summarize the recent development and applications of the major classes of GINAs. In particular, we take an in-depth look at the design of available GINA families with a particular focus on genetically encoded calcium indicators (GCaMPs), sensors probing synaptic activity, and genetically encoded voltage indicators. Using the family of the GCaMP as an example, we review established sensor optimization pipelines. We also discuss practical considerations for end users of GINAs about experimental methods including approaches for gene delivery, imaging system requirements, and data analysis techniques. With the growing toolbox of GINAs and with new microscopy techniques pushing beyond their current limits, the age of light can finally achieve the goal of broad and dense sampling of neuronal activity across time and brain structures to obtain a dynamic picture of brain function.

Early detection of subclinical visual damage after blast-mediated TBI enables prevention of chronic visual deficit by treatment with P7C3-S243

PURPOSE

Traumatic brain injury (TBI) frequently leads to chronic visual dysfunction. The purpose of this study was to investigate the effect of TBI on retinal ganglion cells (RGCs), and to test whether treatment with the novel neuroprotective compound P7C3-S243 could prevent in vivo functional deficits in the visual system.

METHODS

Blast-mediated TBI was modeled using an enclosed over-pressure blast chamber. The RGC physiology was evaluated using a multielectrode array and pattern electroretinogram (PERG). Histological analysis of RGC dendritic field and cell number were evaluated at the end of the study. Visual outcome measures also were evaluated based on treatment of mice with P7C3-S243 or vehicle control.

RESULTS

We show that deficits in neutral position PERG after blast-mediated TBI occur in a temporally bimodal fashion, with temporary recovery 4 weeks after injury followed by chronically persistent dysfunction 12 weeks later. This later time point is associated with development of dendritic abnormalities and irreversible death of RGCs. We also demonstrate that ongoing pathologic processes during the temporary recovery latent period (including abnormalities of RGC physiology) lead to future dysfunction of the visual system. We report that modification of PERG to provocative postural tilt testing elicits changes in PERG measurements that correlate with a key in vitro measures of damage: the spontaneous and light-evoked activity of RGCs. Treatment with P7C3-S243 immediately after injury and throughout the temporary recovery latent period protects mice from developing chronic visual system dysfunction.

CONCLUSIONS

Provocative PERG testing serves as a noninvasive test in the living organism to identify early damage to the visual system, which may reflect corresponding damage in the brain that is not otherwise detectable by noninvasive means. This provides the basis for developing an earlier diagnostic test to identify patients at risk for developing chronic CNS and visual system damage after TBI at an earlier stage when treatments may be more effective in preventing these sequelae. In addition, treatment with the neuroprotective agent P7C3-S243 after TBI protects from visual system dysfunction after TBI.

Columnar organization of spatial phase in visual cortex

Images are processed in the primary visual cortex by neurons that encode different stimulus orientations and spatial phases. In primates and carnivores, neighboring cortical neurons share similar orientation preferences but spatial phases were thought to be randomly distributed. Here we reveal a columnar organization for spatial phase in cats that shares resemblances with the columnar organization for orientation. For both orientation and phase, the mean difference across vertically aligned neurons was less than 1/4 of a cycle. Cortical neurons showed three times more diversity in phase than orientation preference, however, the average phase of local neuronal populations was similar through the depth of layer 4. We conclude that columnar organization for visual space is not only defined by the spatial location of the stimulus but also by absolute phase. Taken together with previous studies, our results suggest that this phase-visuotopy is responsible for the emergence of orientation maps.

Neuronal and perceptual differences in the temporal processing of darks and lights

Visual information is mediated by two major thalamic pathways that signal light decrements (OFF) and increments (ON) in visual scenes, the OFF pathway being faster than the ON. Here, we demonstrate that this OFF temporal advantage is transferred to visual cortex and has a correlate in human perception. OFF-dominated cortical neurons in cats responded ∼3 ms faster to visual stimuli than ON-dominated cortical neurons, and dark-mediated suppression in ON-dominated neurons peaked ∼14 ms faster than light-mediated suppression in OFF-dominated neurons. Consistent with the neuronal differences, human observers were 6-14 ms faster at detecting darks than lights and better at discriminating dark than light flickers. Neuronal and perceptual differences both vanished if backgrounds were biased toward darks. Our results suggest that the cortical OFF pathway is faster than the ON pathway at increasing and suppressing visual responses, and these differences have parallels in the human visual perception of lights and darks.

Neuronal nonlinearity explains greater visual spatial resolution for darks than lights

Astronomers and physicists noticed centuries ago that visual spatial resolution is higher for dark than light stimuli, but the neuronal mechanisms for this perceptual asymmetry remain unknown. Here we demonstrate that the asymmetry is caused by a neuronal nonlinearity in the early visual pathway. We show that neurons driven by darks (OFF neurons) increase their responses roughly linearly with luminance decrements, independent of the background luminance. However, neurons driven by lights (ON neurons) saturate their responses with small increases in luminance and need bright backgrounds to approach the linearity of OFF neurons. We show that, as a consequence of this difference in linearity, receptive fields are larger in ON than OFF thalamic neurons, and cortical neurons are more strongly driven by darks than lights at low spatial frequencies. This ON/OFF asymmetry in linearity could be demonstrated in the visual cortex of cats, monkeys, and humans and in the cat visual thalamus. Furthermore, in the cat visual thalamus, we show that the neuronal nonlinearity is present at the ON receptive field center of ON-center neurons and ON receptive field surround of OFF-center neurons, suggesting an origin at the level of the photoreceptor. These results demonstrate a fundamental difference in visual processing between ON and OFF channels and reveal a competitive advantage for OFF neurons over ON neurons at low spatial frequencies, which could be important during cortical development when retinal images are blurred by immature optics in infant eyes.

Delayed suppression shapes disparity selective responses in monkey V1

The stereo correspondence problem poses a challenge to visual neurons because localized receptive fields potentially cause false responses. Neurons in the primary visual cortex (V1) partially resolve this problem by combining excitatory and suppressive responses to encode binocular disparity. We explored the time course of this combination in awake, monkey V1 neurons using subspace mapping of receptive fields. The stimulus was a binocular noise pattern constructed from discrete spatial frequency components. We forward correlated the firing of the V1 neuron with the occurrence of binocular presentations of each spatial frequency component. The forward correlation yielded a complete set of response time courses to every combination of spatial frequency and interocular phase difference. Some combinations produced suppressive responses. Typically, if an interocular phase difference for a given spatial frequency produced strong excitation, we saw suppression in response to the opposite interocular phase difference at lower spatial frequencies. The suppression was delayed relative to the excitation, with a median difference in latency of 7 ms. We found that the suppressive mechanism explains a well-known mismatch of monocular and binocular signals. The suppressive components increased power at low spatial frequencies in disparity tuning, whereas they reduced the monocular response to low spatial frequencies. This long-recognized mismatch of binocular and monocular signals reflects a suppressive mechanism that helps reduce the response to false matches.

Tuning dissimilarity explains short distance decline of spontaneous spike correlation in macaque V1

Fast spike correlation is a signature of neural ensemble activity thought to underlie perception, cognition, and action. To relate spike correlation to tuning and other factors, we focused on spontaneous activity because it is the common 'baseline' across studies that test different stimuli, and because variations in correlation strength are much larger across cell pairs than across stimuli. Is the probability of spike correlation between two neurons a graded function of lateral cortical separation, independent of functional tuning (e.g. orientation preferences)? Although previous studies found a steep decline in fast spike correlation with horizontal cortical distance, we hypothesized that, at short distances, this decline is better explained by a decline in receptive field tuning similarity. Here we measured macaque V1 tuning via parametric stimuli and spike-triggered analysis, and we developed a generalized linear model (GLM) to examine how different combinations of factors predict spontaneous spike correlation. Spike correlation was predicted by multiple factors including color, spatiotemporal receptive field, spatial frequency, phase and orientation but not ocular dominance beyond layer 4. Including these factors in the model mostly eliminated the contribution of cortical distance to fast spike correlation (up to our recording limit of 1.4mm), in terms of both 'correlation probability' (the incidence of pairs that have significant fast spike correlation) and 'correlation strength' (each pair's likelihood of fast spike correlation). We suggest that, at short distances and non-input layers, V1 fast spike correlation is determined more by tuning similarity than by cortical distance or ocular dominance.

Mapping the primate lateral geniculate nucleus: a review of experiments and methods

Mapping neuronal responses in the lateral geniculate nucleus (LGN) is key to understanding how visual information is processed in the brain. This paper focuses on our current knowledge of the dynamics the receptive field (RF) as broken down into the classical receptive field (CRF) and the extra-classical receptive field (ECRF) in primate LGN. CRFs in the LGN are known to be similar to those in the retinal ganglion cell layer in terms of both spatial and temporal characteristics, leading to the standard interpretation of the LGN as a relay center from retina to primary visual cortex. ECRFs have generally been found to be large and inhibitory, with some differences in magnitude between the magno-, parvo-, and koniocellular pathways. The specific contributions of the retina, thalamus, and visual cortex to LGN ECRF properties are presently unknown. Some reports suggest a retinal origin for extra-classical suppression based on latency arguments and other reports have suggested a thalamic origin for extra-classical suppression. This issue is complicated by the use of anesthetized animals, where cortical activity is likely to be altered. Thus further study of LGN ECRFs is warranted to reconcile these discrepancies. Producing descriptions of RF properties of LGN neurons could be enhanced by employing preferred naturalistic stimuli. Although there has been significant work in cats with natural scene stimuli and noise that statistically imitates natural scenes, we highlight a need for similar data from primates. Obtaining these data may be aided by recent advancements in experimental and analytical techniques that permit the efficient study of nonlinear RF characteristics in addition to traditional linear factors. In light of the reviewed topics, we conclude by suggesting experiments to more clearly elucidate the spatial and temporal structure of ECRFs of primate LGN neurons.

Neural mechanism for sensing fast motion in dim light

Luminance is a fundamental property of visual scenes. A population of neurons in primary visual cortex (V1) is sensitive to uniform luminance. In natural vision, however, the retinal image often changes rapidly. Consequently the luminance signals visual cells receive are transiently varying. How V1 neurons respond to such luminance changes is unknown. By applying large static uniform stimuli or grating stimuli altering at 25 Hz that resemble the rapid luminance changes in the environment, we show that approximately 40% V1 cells responded to rapid luminance changes of uniform stimuli. Most of them strongly preferred luminance decrements. Importantly, when tested with drifting gratings, the preferred speeds of these cells were significantly higher than cells responsive to static grating stimuli but not to uniform stimuli. This responsiveness can be accounted for by the preferences for low spatial frequencies and high temporal frequencies. These luminance-sensitive cells subserve the detection of fast motion under the conditions of dim illumination.

Heterogeneity in the spatial receptive field architecture of multisensory neurons of the superior colliculus and its effects on multisensory integration

Multisensory integration has been widely studied in neurons of the mammalian superior colliculus (SC). This has led to the description of various determinants of multisensory integration, including those based on stimulus- and neuron-specific factors. The most widely characterized of these illustrate the importance of the spatial and temporal relationships of the paired stimuli as well as their relative effectiveness in eliciting a response in determining the final integrated output. Although these stimulus-specific factors have generally been considered in isolation (i.e., manipulating stimulus location while holding all other factors constant), they have an intrinsic interdependency that has yet to be fully elucidated. For example, changes in stimulus location will likely also impact both the temporal profile of response and the effectiveness of the stimulus. The importance of better describing this interdependency is further reinforced by the fact that SC neurons have large receptive fields, and that responses at different locations within these receptive fields are far from equivalent. To address these issues, the current study was designed to examine the interdependency between the stimulus factors of space and effectiveness in dictating the multisensory responses of SC neurons. The results show that neuronal responsiveness changes dramatically with changes in stimulus location - highlighting a marked heterogeneity in the spatial receptive fields of SC neurons. More importantly, this receptive field heterogeneity played a major role in the integrative product exhibited by stimulus pairings, such that pairings at weakly responsive locations of the receptive fields resulted in the largest multisensory interactions. Together these results provide greater insight into the interrelationship of the factors underlying multisensory integration in SC neurons, and may have important mechanistic implications for multisensory integration and the role it plays in shaping SC-mediated behaviors.

Laminar firing and membrane dynamics in four visual areas exposed to two objects moving to occlusion

It is not known how visual cortical neurons react to several moving objects and how their firing to the motion of one object is affected by neurons firing to another moving object. Here we combine imaging of voltage sensitive dye (VSD) signals, reflecting the population membrane potential from ferret visual areas 17, 18, 19, and 21, with laminar recordings of multiunit activity, (MUA), when two bars moved toward each other in the visual field, occluded one another, and continued on in opposite directions. Two zones of peak MUA, mapping the bars' motion, moved toward each other along the area 17/18 border, which in the ferret maps the vertical meridian of the field of view. This was reflected also in the VSD signal, at both the 17/18 border as well as at the 19/21 border with a short delay. After some 125 ms at the area 19/21 border, the VSD signal increased and became elongated in the direction of motion in front of both of the moving representations. This was directly followed by the phase of the signal reversing and travelling back from the 19/21 border toward the 17/18 border, seemingly without respect for retinotopic boundaries, where it arrived at 150 ms after stimulus onset. At this point the VSD signal in front of the moving bar representations along the 17/18 border also increased and became elongated in the direction of object motion; the signal now being the linear sum of what has been observed in response to single moving bars. When the neuronal populations representing the bars were some 600 μm apart on the cortex, the dye signal and laminar MUA decreased strongly, with the MUA scaling to that of a single bar during occlusion. Despite a short rebound of the dye signal and MUA, the MUA after the occlusion was significantly depressed. The interactions between the neuronal populations mapping the bars' position, and the neurons in between these populations were, apart from 19/21 to 17/18 interaction, mainly lateral-horizontal; first excitatory and inducing firing at the site of future occlusion, then inhibitory just prior to occlusion. After occlusion the neurons that had fired already to the first bar showed delayed and prolonged inhibition in response to the second bar. Thus, the interactions that were particular to the occlusion condition in these experiments were local and inhibitory at short cortical range, and delayed and inhibitory after the occlusion when the bars moved further apart.

Gap junction permeability modulated by dopamine exerts effects on spatial and temporal correlation of retinal ganglion cells' firing activities

Synchronized activities among retinal ganglion cells (RGCs) via gap junctions can be increased by exogenous dopamine (DA). During DA application, single neurons' firing activities become more synchronized with its adjacent neighbors. One intriguing question is how the enhanced spatial synchronization alters the temporal firing structure of single neurons. In the present study, firing activities of bullfrog's dimming detectors in response to binary pseudo-random checker-board flickering were recorded via a multi-channel recording system. DA was applied in the retina to modulate synchronized activities between RGCs, and the effect of DA on firing activities of single neurons was examined. It was found that, during application of DA, synchronized activities between single neuron and its neighboring neurons was enhanced. At the meantime, the temporal structures of single neuron spike train changed significantly, and the temporal correlation in single neuron's response decreased. The pharmacological study results indicated that the activation of D1 receptor might have effects on gap junction permeability between RGCs. Our results suggested that the dopaminergic pathway participated in the modulation of spatial and temporal correlation of RGCs' firing activities, and may exert critical effects on visual information processing in the retina.