Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey

How reliably do action potentials in cortical neurons encode information about a visual stimulus? Most physiological studies do not weigh the occurrences of particular action potentials as significant but treat them only as reflections of average neuronal excitation. We report that single neurons recorded in a previous study by Newsome et al. (1989; see also Britten et al. 1992) from cortical area MT in the behaving monkey respond to dynamic and unpredictable motion stimuli with a markedly reproducible temporal modulation that is precise to a few milliseconds. This temporal modulation is stimulus dependent, being present for highly dynamic random motion but absent when the stimulus translates rigidly

Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role

The lateral geniculate nucleus (LGN) has often been treated in the past as a linear filter that adds little to retinal processing of visual inputs. Here we review anatomical, neurophysiological, brain imaging, and modeling studies that have in recent years built up a much more complex view of LGN . These include effects related to nonlinear dendritic processing, cortical feedback, synchrony and oscillations across LGN populations, as well as involvement of LGN in higher level cognitive processing. Although recent studies have provided valuable insights into early visual processing including the role of LGN, a unified model of LGN responses to real-world objects has not yet been developed. In the light of recent data, we suggest that the role of LGN deserves more careful consideration in developing models of high-level visual processing

The What and Why of Binding: The Modeler's Perspective

In attempts to formulate a computational understanding of brain function, one of the fundamental concerns is the data structure by which the brain represents information. For many decades, a conceptual framework has dominated the thinking of both brain modelers and neurobiologists. That framework is referred to here as "classical neural networks." It is well supported by experimental data, although it may be incomplete. A characterization of this framework will be offered in the next section. Difficulties in modeling important functional aspects of the brain on the basis of classical neural networks alone have led to the recognition that another, general mechanism must be invoked to explain brain function. That mechanism I call "binding." Binding by neural signal synchrony had been mentioned several times in the liter ature (Lege´ndy, 1970; Milner, 1974) before it was fully formulated as a general phenomenon (von der Malsburg, 1981). Although experimental evidence for neural syn chrony was soon found, the idea was largely ignored for many years. Only recently has it become a topic of animated discussion. In what follows, I will summarize the nature and the roots of the idea of binding, especially of temporal binding, and will discuss some of the objec tions raised against it

The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs

How random is the discharge pattern of cortical neurons? We examined recordings from primary visual cortex (V1; Knierim and Van Essen, 1992) and extrastriate cortex (MT; Newsome et al., 1989a) of awake, behaving macaque monkey and compared them to analytical predictions. For nonbursting cells firing at sustained rates up to 300 Hz, we evaluated two indices of firing variability: the ratio of the variance to the mean for the number of action potentials evoked by a constant stimulus, and the rate-normalized coefficient of variation (Cv) of the interspike interval distribution. Firing in virtually all V1 and MT neurons was nearly consistent with a completely random process (e.g., Cv approximately 1). We tried to model this high variability by small, independent, and random EPSPs converging onto a leaky integrate-and- fire neuron (Knight, 1972). Both this and related models predicted very low firing variability (Cv << 1) for realistic EPSP depolarizations and membrane time constants. We also simulated a biophysically very detailed compartmental model of an anatomically reconstructed and physiologically characterized layer V cat pyramidal cell (Douglas et al., 1991) with passive dendrites and active soma. If independent, excitatory synaptic input fired the model cell at the high rates observed in monkey, the Cv and the variability in the number of spikes were both very low, in agreement with the integrate-and-fire models but in strong disagreement with the majority of our monkey data. The simulated cell only produced highly variable firing when Hodgkin-Huxley- like currents (INa and very strong IDR) were placed on distal dendrites. Now the simulated neuron acted more as a millisecond- resolution detector of dendritic spike coincidences than as a temporal integrator. We argue that neurons that act as temporal integrators over many synaptic inputs must fire very regularly. Only in the presence of either fast and strong dendritic nonlinearities or strong synchronization among individual synaptic events will the degree of predicted variability approach that of real cortical neurons

State Dependence of Stimulus-Induced Variability Tuning in Macaque MT

Behavioral states marked by varying levels of arousal and attention modulate some properties of cortical responses (e.g. average firing rates or pairwise correlations), yet it is not fully understood what drives these response changes and how they might affect downstream stimulus decoding. Here we show that changes in state modulate the tuning of response variance-to-mean ratios (Fano factors) in a fashion that is neither predicted by a Poisson spiking model nor changes in the mean firing rate, with a substantial effect on stimulus discriminability. We recorded motion-sensitive neurons in middle temporal cortex (MT) in two states: alert fixation and light, opioid anesthesia. Anesthesia tended to lower average spike counts, without decreasing trial-to-trial variability compared to the alert state. Under anesthesia, within-trial fluctuations in excitability were correlated over longer time scales compared to the alert state, creating supra-Poisson Fano factors. In contrast, alert-state MT neurons have higher mean firing rates and largely sub-Poisson variability that is stimulus-dependent and cannot be explained by firing rate differences alone. The absence of such stimulus-induced variability tuning in the anesthetized state suggests different sources of variability between states. A simple model explains state-dependent shifts in the distribution of observed Fano factors via a suppression in the variance of gain fluctuations in the alert state. A population model with stimulus-induced variability tuning and behaviorally constrained information-limiting correlations explores the potential enhancement in stimulus discriminability by the cortical population in the alert state.Comment: 36 pages, 18 figure

Neural representation of complex motion in the primate cortex

This dissertation is concerned with how information about the environment is represented by neural activity in the primate brain. More specifically, it contains several studies that explore the representation of visual motion in the brains of humans and nonhuman primates through behavioral and physiological measures. The majority of this work is focused on the activity of individual neurons in the medial superior temporal area (MST) – a high-level, extrastriate area of the primate visual cortex. The first two studies provide an extensive review of the scientific literature on area MST. The area’s prominent role at the intersection of low-level, bottom-up, sensory processing and high-level, top-down mechanisms is highlighted. Furthermore, a specific article on how information about self-motion and object motion can be decoded from a population of MSTd neurons is reviewed in more detail. The third study describes a published and annotated dataset of MST neurons’ responses to a series of different motion stimuli. This dataset is analyzed using a variety of different analysis approaches in the fifth study. Classical tuning curve approaches confirm that MST neurons have large, but well-defined spatial receptive fields and are independently tuned for linear and spiral motion, as well as speed. We also confirm that the tuning for spiral motion is position invariant in a majority of MST neurons. A bias-free characterization of receptive field profiles based on a new stimulus that generates smooth, complex motion patterns turned out to be predictive of some of the tuning properties of MST neurons, but was generally less informative than similar approaches have been in earlier visual areas. The fifth study introduces a new motion stimulus that consists of hexgonal segments and presents an optimization algorithm for an adaptive online analysis of neurophysiological recordings. Preliminary physiological data and simulations show these tools to have a strong potential in characterizing the response functions of MST neurons. The final study describes a behavioral experiment with human subjects that explores how different stimulus features, such as size and contrast, affect motion perception and discusses what conclusions can be drawn from that about the representation of visual motion in the human brain. Together these studies highlight the visual motion processing pathway of the primate brain as an excellent model system for studying more complex relations of neural activity and external stimuli. Area MST in particular emerges as a gateway between perception, cognition, and action planning.2021-11-1

Higher Order Spike Synchrony in Prefrontal Cortex during Visual Memory

Precise temporal synchrony of spike firing has been postulated as an important neuronal mechanism for signal integration and the induction of plasticity in neocortex. As prefrontal cortex plays an important role in organizing memory and executive functions, the convergence of multiple visual pathways onto PFC predicts that neurons should preferentially synchronize their spiking when stimulus information is processed. Furthermore, synchronous spike firing should intensify if memory processes require the induction of neuronal plasticity, even if this is only for short-term. Here we show with multiple simultaneously recorded units in ventral prefrontal cortex that neurons participate in 3 ms precise synchronous discharges distributed across multiple sites separated by at least 500 μm. The frequency of synchronous firing is modulated by behavioral performance and is specific for the memorized visual stimuli. In particular, during the memory period in which activity is not stimulus driven, larger groups of up to seven sites exhibit performance dependent modulation of their spike synchronization

Visual responses of neurons in the middle temporal area of new World Monkeys after lesions of striate cortex

In primates, lesions of striate cortex (V1) result in scotomas in which only rudimentary visual abilities remain. These aspects of vision that survive V1 lesions have been attributed to direct thalamic pathways to extrastriate areas, including the middle temporal area (MT). However, studies in New World monkeys and humans have questioned this interpretation, suggesting that remnants of V1 are responsible for both the activation of MT and residual vision. We studied the visual responses of neurons in area MT in New World marmoset monkeys in the weeks after lesions of V1. The extent of the scotoma in each case was estimated by mapping the receptive fields of cells located near the lesion border and by histological reconstruction. Two response types were observed among the cells located in the part of MT that corresponds, in visuotopic coordinates, to the lesioned part of V1. Many neurons (62%) had receptive fields that were displaced relative to their expected location, so that they represented the visual field immediately surrounding the scotoma. This may be a consequence of a process analogous to the reorganization of the V1 map after retinal lesions. However, another 20% of the cells had receptive fields centered inside the scotoma. Most of these neurons were strongly direction-selective, similar to normal MT cells. These results show that MT cells differ in their responses to lesioning of V1 and that only a subpopulation of MT neurons can be reasonably linked to residual vision and blindsight

HIERARCHICAL NEURAL COMPUTATION IN THE MAMMALIAN VISUAL SYSTEM

Our visual system can efficiently extract behaviorally relevant information from ambiguous and noisy luminance patterns. Although we know much about the anatomy and physiology of the visual system, it remains obscure how the computation performed by individual visual neurons is constructed from the neural circuits. In this thesis, I designed novel statistical modeling approaches to study hierarchical neural computation, using electrophysiological recordings from several stages of the mammalian visual system. In Chapter 2, I describe a two-stage nonlinear model that characterized both synaptic current and spike response of retinal ganglion cells with unprecedented accuracy. I found that excitatory synaptic currents to ganglion cells are well described by excitatory inputs multiplied by divisive suppression, and that spike responses can be explained with the addition of a second stage of spiking nonlinearity and refractoriness. The structure of the model was inspired by known elements of the retinal circuit, and implies that presynaptic inhibition from amacrine cells is an important mechanism underlying ganglion cell computation. In Chapter 3, I describe a hierarchical stimulus-processing model of MT neurons in the context of a naturalistic optic flow stimulus. The model incorporates relevant nonlinear properties of upstream V1 processing and explained MT neuron responses to complex motion stimuli. MT neuron responses are shown to be best predicted from distinct excitatory and suppressive components. The direction-selective suppression can impart selectivity of MT neurons to complex velocity fields, and contribute to improved estimation of the three-dimensional velocity of moving objects. In Chapter 4, I present an extended model of MT neurons that includes both the stimulus-processing component and network activity reflected in local field potentials (LFPs). A significant fraction of the trial-to-trial variability of MT neuron responses is predictable from the LFPs in both passive fixation and a motion discrimination task. Moreover, the choice-related variability of MT neuron responses can be explained by their phase preferences in low-frequency band LFPs. These results suggest an important role of network activity in cortical function. Together, these results demonstrated that it is possible to infer the nature of neural computation from physiological recordings using statistical modeling approaches

Decoding Distributed Neuronal Activity in Extrastriate Cortical Areas for the Visual Prosthetic Applications

Les prothèses visuelles corticales sont planifiées pour restaurer la vision chez les individus non-voyants en appliquant du courant électrique à des sites discrets sur le cortex visuel. À ce jour, la qualité de la vision rapportée dans la littérature est celle d'un petit nombre de phosphènes (percept de spots lumineux spatialement localisés) sans organisation pour générer un percept significatif. Le principal défi consiste à développer des méthodes pour transférer les informations d'une scène visuelle dans un schéma de stimulation compréhensible pour le cerveau. Une connaissance clé pour résoudre ce défi est de comprendre comment les caractéristiques du phosphène (ou en général, les caractéristiques visuelles) sont représentées dans le modèle distribué d'activité neuronale. Une approche pour obtenir ces connaissances consiste à déterminer dans quelle mesure les réponses neuronales bien réparties peuvent détecter les changements dans une caractéristique visuelle spécifique des stimuli. Pour atteindre cet objectif, nous avons étudié la capacité de discrimination des zones corticales extrastriées V4 chez les singes macaques. Ces zones extrastriées ont de petites régions rétinotopiques qui offrent la possibilité d'échantillonner une grande région de l'espace visuel à l'aide de réseaux de microelectrodes standard telles que celles de l'Université d'Utah. Cela aide à construire des prothèses mini-invasives. Notre contribution concerne la résolution spatiale des potentiels de champs locaux (LFP) dans la zone V4 pour déterminer les limites de la capacité des prothèses visuelles à induire des phosphènes à plusieurs positions. Les LFP ont été utilisés car ils représentent une activité neuronale sur une échelle de 400 microns, ce qui est comparable à la propagation de l'effet de microstimulation dans le cortex. La zone visuelle extrastriée V4 contient également une carte rétinotopique de l'espace visuel et offre la possibilité de récupérer l'emplacement des stimuli statiques. Nous avons appliqué la méthode «Support vector machine» (SVM) pour déterminer la capacité des LFP (par rapport aux réponses à plusieurs unités - MUA) à discriminer les réponses (phosphènes) aux stimuli à différentes séparations spatiales. Nous avons constaté que malgré les grandes tailles de champs récepteurs dans V4, les réponses combinées de plusieurs sites étaient capables de discrimination fine et grossière des positions. Nous avons proposé une stratégie de sélection des électrodes basée sur les poids linéaires des décodeurs (en utilisant les valeurs de poids les plus élevées) qui a considérablement réduit le nombre d'électrodes requis pour la discrimination avec une augmentation des performances. L'application de cette stratégie présente l'avantage potentiel de réduire les dommages tissulaires dans les applications réelles. Nous avons conclu que pour un fonctionnement correct des prothèses, la microstimulation électrique devrait générer un schéma d'activité neuronale similaire à l'activité évoquée correspondant à un percept attendu. De plus, lors de la conception d'une prothèse visuelle, les limites de la capacité de discrimination des zones cérébrales implantées doivent être prises en compte. Ces limites peuvent différer pour MUA et LFP.----------ABSTRACT Cortical visual prostheses are intended to restore vision to blind individuals by applying a pattern of electrical currents at discrete sites on the visual cortex. To date, the quality of vision reported in the literature is that of a small number of phosphenes (percept of spatially localized spots of light) with no organization to generate a meaningful percept. The main challenge consists of developing methods to transfer information of a visual scene into a pattern of stimulation that is understandable to the brain. The key to solving this challenge is understanding how phosphene characteristics (or in general, visual characteristics) are represented in a distributed pattern of neural activity. One approach is to determine how well neural responses can detect changes in a specific characteristic of stimuli. To this end, we have studied the discrimination capability of V4 extrastriate cortical area in macaque monkeys. Extrastriate cortical areas have small retinotopic maps that can provide an opportunity to sample a large region of visual space using standard devices such as Utah arrays. Thus, this helps to build minimally invasive prosthetic devices. Our contribution relates to the spatial resolution of local field potentials (LFPs) in area V4 to determine the limits in the capability of visual prosthetic devices in generation of phosphenes in multiple positions. LFPs were used because they represent neural activity over a scale of 400 microns, which is comparable to the spread of microstimulation effects in the cortex. Extrastriate visual area V4 also contains a retinotopic map of visual space and offers an opportunity to recover the location of static stimuli. We applied support vector machines (SVM) to determine the capability of LFPs (compared to multi-unit responses) in discriminating responses to phosphene-like stimuli (probes) located with different spatial separations. We found that despite large receptive field sizes in V4, combined responses from multiple sites were capable of fine and coarse discrimination of positions. We proposed an electrode selection strategy based on the linear weights of the decoder (using the highest weight values) that significantly reduced the number of electrodes required for discrimination, while at the same time, increased performance. Applying this strategy has the potential to reduce tissue damages in real applications. We concluded that for the correct operation of prosthetic devices, electrical microstimulation should generate a pattern of neural activity similar to the evoked activity corresponding to an expected percept. Moreover, in the design of visual prosthesis, limits in the discrimination capability of the implanted brain areas should be taken into account. These limits may differ for MUA and LFP