The context dependence of network response properties in the primary visual cortex of the primate and cat

In the mammalian visual system, stimulus context was investigated with respect to the ways it influenced neuronal mean response magnitude (the average number of spikes fired per second), response temporal structure (the timing of spikes with respect to one another), and the extent to which distributed neurones fired spikes synchronous due to synaptic interaction between them. Neurones were presented with bipartite grating stimuli, in which the spatio-temporal relationship between the grating activating the excitatory receptive field and that presented to the surrounding visual space could be varied systematically. Simultaneous extracellular recordings were made of the responses of up to four single neurones separated by 750-1000µm, in the lateral geniculate nucleus (LGN) of the thalamus in the cat, or the primary visual cortex (V1) of non-human primates or cats. Changing context systematically influenced the activity of groups of cells. The responses of 83% of primate V1 cells to discontinuous stimuli, in which the centre/surround orientation difference was greater than 45°, contained stronger oscillations at frequencies below 80Hz, than responses to continuous stimuli. Many cat and primate V1 neurones exhibited elevated response magnitudes to such stimuli. In primate V1, the strength of a cell's oscillatory discharge was dependent on stimulus configuration rather than response magnitude. In the LGN and V1, cell pairs with different orientation preferences fired synchronised responses when stimulated by specific discontinuous grating configurations. Stimulus specific synchronised LGN input, and reciprocal excitatory and inhibitory cortico-cortical connections could generate these properties of cells, and the network in which they exist. A model is proposed to account for the function significance of contour discontinuities in generating coherent neural representations of objects in the visual world. It involves response synchronisation in horizontal, feedforward and feedback interactions, within and between the LGN, V1, V2 and V4

Stochastic models for near-synchronous neuronal firing activity

It is commonly agreed that cortical information processing is based on the electric discharges (spikes') of nerve cells. Evidence is accumulating which suggests that the temporal interaction among a large number of neurons can take place with high precision, indicating that the efficiency of cortical processing may depend crucially on the precise spike timing of many cells. This work focuses on two temporal properties of parallel spike trains that attracted growing interest in the recent years: In the first place, specific delays (phase offsets') between the firing times of two spike trains are investigated. In particular, it is studied whether small phase offsets can be identified with confidence between two spike trains that have the tendency to fire almost simultaneously. Second, the temporal relations between multiple spike trains are investigated on the basis of such small offsets between pairs of processes. Since the analysis of all delays among the firing activity of n neurons is extremely complex, a method is required with which this highly dimensional information can be collapsed in a straightforward manner such that the temporal interaction among a large number of neurons can be represented consistently in a single temporal map. Finally, a stochastic model is presented that provides a framework to integrate and explain the observed temporal relations that result from the previous analyses.Aktuelle neurophysiologische Studien liefern Hinweise darauf, dass neuronale Informationsverarbeitung auf Mechanismen basiert, die mit hoher zeitlicher Präzision ablaufen. In dieser Arbeit werden drei Ansätze vorgestellt, mit denen die zeitliche Koordination der Feueraktivität von n parallelen Spike Trains statistisch analysiert und modelliert werden kann. Der erste Teil stellt eine Methode vor, mit der eine spezifische Verzögerung (die Phase') zwischen zwei parallelen Spike Trains gemessen werden kann. Inbesondere wird die Genauigkeit untersucht, mit der die Phase bei solchen Spike Trains bestimmt werden kann, die die Tendenz haben, dahezu simultan zu feuern. Im zweiten Teil wird ein Modell vorgestellt, mit dessen Hilfe untersucht werden soll, ob sich die zwischen n Spike Trains paarweise gemessenen Phasen in einer konsistenten, niederdimensionalen Darstellung erfassen lassen, in welcher jedem Prozess ein Punkt auf der Zeitachse zugeordnet wird. Im dritten Teil schließlich wird ein stochastisches Modell für n parallele Spike Trains mit koordinierter rhythmischer Feueraktivität präsentiert, in dessen Rahmen die in den vorherigen Analysen beobachteten zeitlichen Beziehungen integriert und erklärt werden

Sparse algorithms for decoding and identification of neural circuits

The brain, as an information processing machine, surpasses any man-made computational device, both in terms of its capabilities and its efficiency. Neuroscience research has made great strides since the foundational works of Cajal and Golgi. However, we still have very little understanding about the algorithmic underpinnings of the brain as an information processor. Identifying mechanistic models of the functional building blocks of the brain will have significant impact not just on neuroscience, but also on artificial computational systems. This provides the main motivation for the work presented in this thesis, summarily i) biologically-inspired algorithms that can be efficiently implemented in silico, ii) functional identification of the processing in certain types of neural circuits, and iii) a collaborative ecosystem for brain research in a model organism, towards the synergistic goal of understanding functional mechanisms employed by the brain. First, this thesis provides a highly parallelizable, biologically-inspired, motion detection algorithm that is based upon the temporal processing of the local (spatial) phase of a visual stimulus. The relation of the phase based motion detector to the widely studied Reichardt detector model, is discussed. Examples are provided comparing the performance of the proposed algorithm with the Reichardt detector as well as the optic flow algorithm, which is the workhorse for motion detection in computer vision. Further, it is shown through examples that the phase based motion detection model provides intuitive explanations for reverse-phi based illusory motion percepts. Then, tractable algorithms are presented for decoding with and identification of neural circuits, comprised of processing that can be described by a second-order Volterra kernel (quadratic filter). It is shown that the Reichardt detector, as well as models of cortical complex cells, can be described by this structure. Examples are provided for decoding of visual stimuli encoded by a population of Reichardt detector cells and complex cells, as well as their identification from observed spike times. Further, the phase based motion detection model is shown to be equivalent to a second-order Volterra kernel acting on two normalized inputs. Subsequently, a general model that computes the ratio of two non-linear functionals, each comprising linear (first order Volterra kernel) and quadratic (second-order Volterra kernel) filters, is proposed. It is shown that, even under these highly non-linear operations, a population of cells can encode stimuli faithfully using a number of measurements that are proportional to the bandwidth of the input stimulus. Tractable algorithms are devised to identify the divisive normalization model and examples of identification are provided for both simulated and biological data. Additionally, an extended framework, comprising parallel channels of divisively normalized cells each subjected to further divisive normalization from lateral feedback connections, is proposed. An algorithm is formulated for identifying all the components in this extended framework from controlled stimulus presentation and observed outputs samples. Finally, the thesis puts forward the Fruit Fly Brain Observatory (FFBO), an initiative to enable a collaborative ecosystem for fruit fly brain research. Key applications in FFBO, and the software and computational infrastructure enabling them, are described along with case studies

Functional Role of Critical Dynamics in Flexible Visual Information Processing

Recent experimental and theoretical work has established the hypothesis that cortical neurons operate close to a critical state which signifies a phase transition from chaotic to ordered dynamics. Critical dynamics are suggested to optimize several aspects of neuronal information processing. However, although signatures of critical dynamics have been demonstrated in recordings of spontaneously active cortical neurons, little is known about how these dynamics are affected by task-dependent changes in neuronal activity when the cortex is engaged in stimulus processing. In fact, some in vivo investigations of the awake and active cortex report either an absence of signatures of criticality or relatively weak ones. In addition, the functional role of criticality in optimizing computation is often reported in abstract theoretical studies, adopting minimalistic models with homogeneous topology and slowly-driven networks. Consequently, there is a lack of concrete links between information theoretical benefits of the critical state and neuronal networks performing a behaviourally relevant task. In this thesis we explore such concrete links by focusing on the visual system, which needs to meet major computational challenges on a daily basis. Among others, the visual system is responsible for the rapid integration of relevant information from a large number of single channels, and in a flexible manner depending on the behavioral and environmental contexts. We postulate that critical neuronal dynamics in the form of cascades of activity spanning large populations of neurons may support such quick and complex computations. Specifically, we consider two notable examples of well-known phenomena in visual information processing: First the enhancement of object discriminability under selective attention, and second, a feature integration and figure-ground segregation scenario. In the first example, we model the top-down modulation of the activity of visuocortical neurons in order to selectively improve the processing of an attended region in a visual scene. In the second example, we model how neuronal activity may be modulated in a bottom-up fashion by the properties of the visual stimulus itself, which makes it possible to perceive different shapes and objects. We find in both scenarios that the task performance may be improved by employing critical networks. In addition, we suggest that the specific task- or stimulus-dependent modulations of information processing may be optimally supported by the tuning of relevant local neuronal networks towards or away from the critical point. Thus, the relevance of this dissertation is summarized by the following points: We formally extend the existing models of criticality to inhomogeneous systems subject to a strong external drive. We present concrete functional benefits for networks operating near the critical point in well-known experimental paradigms. Importantly, we find emergent critical dynamics only in the parts of the network which are processing the behaviourally relevant information. We suggest that the implied locality of critical dynamics in space and time may help explain why some studies report no signatures of criticality in the active cortex

Microsaccadic Efficacy and Contribution to Foveal and Peripheral Vision

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

The Dynamic Brain in Action: Cortical Oscillations and Coordination Dynamics

Cortical oscillations are electrical activities with rhythmic and/or repetitive nature generated spontaneously and in response to stimuli. Study of cortical oscillations has become an area of converging interests since the last two decades and has deepened our understanding of its physiological basis across different behavioral states. Experimental and modeling work has taught us that there is a wide diversity of cellular and circuit mechanisms underlying the generation of cortical rhythms. A wildly diverse set of functions has pertained to synchronous oscillations but their significance in cognition should be better appraised in the more general framework of correlation between spike times of neurons. Oscillations are the core mechanism in adjusting neuronal interactions and shaping temporal coordination of neural activity. In the first part of this thesis, we review essential feature of cortical oscillations in membrane potentials and local field potentials recorded from turtle ex vivo preparation. Then we develop a simple computational model that reproduces the observed features. This modeling investigation suggests a plausible underlying mechanism for rhythmogenesis through cellular and circuit properties. The second part of the thesis is about temporal coordination dynamics quantified by signal and noise correlations. Here, again, we present a computational model to show how temporal coordination and synchronous oscillations can be sewn together. More importantly, what biophysical ingrediants are necessary for a network to reproduce the observed coordination dynamics

Nonlinear Processing of Shape Information in Rat Lateral Extrastriate Cortex

In rodents, the progression of extrastriate areas located laterally to primary visual cortex (V1) has been assigned to a putative object-processing pathway (homologous to the primate ventral stream), based on anatomical considerations. Recently, we found functional support for such attribution (Tafazoli et al., 2017), by showing that this cortical progression is specialized for coding object identity despite view changes, the hallmark property of a ventral-like pathway. Here, we sought to clarify what computations are at the base of such specialization. To this aim, we performed multielectrode recordings from V1 and laterolateral area LL (at the apex of the putative ventral-like hierarchy) of male adult rats, during the presentation of drifting gratings and noise movies. We found that the extent to which neuronal responses were entrained to the phase of the gratings sharply dropped from V1 to LL, along with the quality of the receptive fields inferred through reverse correlation. Concomitantly, the tendency of neurons to respond to different oriented gratings increased, whereas the sharpness of orientation tuning declined. Critically, these trends are consistent with the nonlinear summation of visual inputs that is expected to take place along the ventral stream, according to the predictions of hierarchical models of ventral computations and a meta-analysis of the monkey literature. This suggests an intriguing homology between the mechanisms responsible for building up shape selectivity and transformation tolerance in the visual cortex of primates and rodents, reasserting the potential of the latter as models to investigate ventral stream functions at the circuitry level.SIGNIFICANCE STATEMENT Despite the growing popularity of rodents as models of visual functions, it remains unclear whether their visual cortex contains specialized modules for processing shape information. To addresses this question, we compared how neuronal tuning evolves from rat primary visual cortex (V1) to a downstream visual cortical region (area LL) that previous work has implicated in shape processing. In our experiments, LL neurons displayed a stronger tendency to respond to drifting gratings with different orientations while maintaining a sustained response across the whole duration of the drift cycle. These trends match the increased complexity of pattern selectivity and the augmented tolerance to stimulus translation found in monkey visual temporal cortex, thus revealing a homology between shape processing in rodents and primates