Novel image enhancement technique using shunting inhibitory cellular neural networks

This paper describes a method for improving image quality in a color CMOS image sensor. The technique simultaneously acts to compress the dynamic range, reorganize the signal to improve visibility, suppress noise, identify local features, achieve color constancy, and lightness rendition. An efficient hardware architecture and a rigorous analysis of the different modules are presented to achieve high quality CMOS digital camera

Persistence and storage of activity patterns in spiking recurrent cortical networks: modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine

Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM). Theorems in the 1970's showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP) currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh) can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 ms or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all (WTA) stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners when the network stabilizes

A generalised feedforward neural network architecture and its applications to classification and regression

Shunting inhibition is a powerful computational mechanism that plays an important role in sensory neural information processing systems. It has been extensively used to model some important visual and cognitive functions. It equips neurons with a gain control mechanism that allows them to operate as adaptive non-linear filters. Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks where the basic synaptic computations are based on shunting inhibition. SIANNs were designed to solve difficult machine learning problems by exploiting the inherent non-linearity mediated by shunting inhibition. The aim was to develop powerful, trainable networks, with non-linear decision surfaces, for classification and non-linear regression tasks. This work enhances and extends the original SIANN architecture to a more general form called the Generalised Feedforward Neural Network (GFNN) architecture, which contains as subsets both SIANN and the conventional Multilayer Perceptron (MLP) architectures. The original SIANN structure has the number of shunting neurons in the hidden layers equal to the number of inputs, due to the neuron model that is used having a single direct excitatory input. This was found to be too restrictive, often resulting in inadequately small or inordinately large network structures

Stability analysis for periodic solutions of fuzzy shunting inhibitory CNNs with delays

https://advancesindifferenceequations.springeropen.com/articles/10.1186/s13662-019-2321-z#rightslinkWe consider fuzzy shunting inhibitory cellular neural networks (FSICNNs) with time-varying coefficients and constant delays. By virtue of continuation theorem of coincidence degree theory and Cauchy–Schwartz inequality, we prove the existence of periodic solutions for FSICNNs. Furthermore, by employing a suitable Lyapunov functional we establish sufficient criteria which ensure global exponential stability of the periodic solutions. Numerical simulations that support the theoretical discussions are depicted

Slow Inhibition and Inhibitory Recruitment in the Hippocampal Dentate Gyrus

L’hippocampe joue un rôle central dans la navigation spatiale, la mémoire et l’organisation spatio-temporelle des souvenirs. Ces fonctions sont maintenues par la capacité du gyrus denté (GD) de séparation des patrons d'activité neuronales. Le GD est situé à l’entrée de la formation hippocampique où il reconnaît la présence de nouveaux motifs parmi la densité de signaux afférant arrivant par la voie entorhinale (voie perforante). Le codage parcimonieux est la marque distinctive du GD. Ce type de codage est le résultat de la faible excitabilité intrinsèque des cellules granulaires (CGs) en combinaison avec une inhibition locale prédominante. En particulier, l’inhibition de type « feedforward » ou circuit inhibiteur antérograde, est engagée par la voie perforante en même temps que les CGs. Ainsi les interneurones du circuit antérograde fournissent des signaux GABAergique aux CGs de manière presque simultanée qu’elles reçoivent les signaux glutamatergiques. Cette thèse est centrée sur l’étude des interactions entre ces signaux excitateurs de la voie entorhinale et les signaux inhibiteurs provenant des interneurones résidant dans le GD et ceci dans le contexte du codage parcimonieux et le patron de décharge en rafale caractéristique des cellules granulaires. Nous avons adressé les relations entre les projections entorhinales et le réseau inhibitoire antérograde du GD en faisant des enregistrements électrophysiologiques des CG pendant que la voie perforante est stimulée de manière électrique ou optogénétique. Nous avons découvert un nouvel mécanisme d’inhibition qui apparait à délais dans les CGs suite à une stimulation dans les fréquences gamma. Ce mécanisme induit une hyperpolarisation de longue durée (HLD) et d’une amplitude prononce. Cette longue hyperpolarisation est particulièrement prolongée et dépasse la durée d’autres types d’inhibition transitoire lente décrits chez les CGs. L’induction de HLD crée une fenêtre temporaire de faible excitabilité suite à laquelle le patron de décharge des CGs et l’intégration d’autres signaux excitateurs sont altérés de manière transitoire. Nous avons donc conclu que l’activité inhibitrice antérograde joue un rôle central dans les processus de codage dans le GD. Cependant, alors qu’il existe une multitude d’études décrivant les interneurones qui font partie de ce circuit inhibiteur, la question de comment ces cellules sont recrutées par la voie entorhinale reste quelque peu explorée. Pour apprendre plus à ce sujet, nous avons enregistré des interneurones résidant iii dans la couche moléculaire du GD tout en stimulant la voie perforante de manière optogénétique. Cette méthode de stimulation nous a permis d’induire la libération de glutamate endogène des terminales entorhinales et ainsi d’observer le recrutement purement synaptique d’interneurones. De manière surprenante, les résultats de cette expérience démontrent un faible taux d’activation des interneurones, accompagné d’un tout aussi faible nombre total de potentiels d’action émis en réponse à la stimulation même à haute fréquence. Ce constat semble contre-intuitif étant donné qu’en générale on assume qu’une forte activité inhibitrice est requise pour le maintien du codage parcimonieux. Tout de même, l’analyse des patrons de décharge des interneurones qui ont été activés a fait ressortir la prééminence de trois grands types: décharge précoce, retardée ou régulière par rapport le début des pulses lumineux. Les résultats obtenus durant cette thèse mettent la lumière sur l’important conséquences fonctionnelles des interactions synaptique et polysynaptique de nature transitoire dans les réseaux neuronaux. Nous aimerions aussi souligner l’effet prononcé de l’inhibition à court terme du type prolongée sur l’excitabilité des neurones et leurs capacités d’émettre des potentiels d’action. De plus que cet effet est encore plus prononcé dans le cas de HLD dont la durée dépasse souvent la seconde et altère l’intégration d’autres signaux arrivants simultanément. Donc on croit que les effets de HLD se traduisent au niveau du réseaux neuronal du GD comme une composante cruciale pour le codage parcimonieux. En effet, ce type de codage semble être la marque distinctive de cette région étant donné que nous avons aussi observé un faible niveau d’activation chez les interneurones. Cependant, le manque d’activité accrue du réseau inhibiteur antérograde peut être compensé par le maintien d’un gradient GABAergique constant à travers le GD via l’alternance des trois modes de décharges des interneurones. En conclusion, il semble que le codage parcimonieux dans le GD peut être préservé même en absence d’activité soutenue du réseau inhibiteur antérograde et ceci grâce à des mécanismes alternatives d’inhibition prolongée à court terme.The hippocampus is implicated in spatial navigation, the generation and recall of memories, as well as their spatio-temporal organization. These functions are supported by the processes of pattern separation that occurs in the dentate gyrus (DG). Situated at the entry of the hippocampal formation, the DG is well placed to detect and sort novelty patterns amongst the high-density excitatory signals that arrive via the entorhinal cortex (EC). A hallmark of the DG is sparse encoding that is enabled by a combination of low intrinsic excitability of the principal cells and local inhibition. Feedforward inhibition (FFI) is recruited directly by the EC and simultaneously with the granule cells (GCs). Therefore, FFI provides fast GABA release and shapes input integration at the millisecond time scale. This thesis aimed to investigate the interplay of entorhinal excitatory signals with GCs and interneurons, from the FFI in the DG, in the framework of sparse encoding and GC’s characteristic burst firing. We addressed the long-range excitation – local inhibitory network interactions using electrophysiological recordings of GCs – while applying an electrical or optogenetic stimulation of the perforant path (PP) in the DG. We discovered and described a novel delayed-onset inhibitory post synaptic potential (IPSP) in GCs, following PP stimulation in the gamma frequency range. Most importantly, the IPSP was characterized by a large amplitude and prolonged decay, outlasting previously described slow inhibitory events in GCs. The long-lasting hyperpolarization (LLH) caused by the slow IPSPs generates a low excitability time window, alters the GCs firing pattern, and interferes with other stimuli that arrive simultaneously. FFI is therefore a key player in the computational processes that occurs in the DG. However, while many studies have been dedicated to the description of the various types of the interneurons from the FFI, the question of how these cells are synaptically recruited by the EC remains not entirely elucidated. We tackled this problem by recording from interneurons in the DG molecular layer during PP-specific optogenetic stimulation. Light-driven activation of the EC terminals enabled a purely synaptic recruitment of interneurons via endogenous glutamate release. We found that this method of stimulation recruits only a subset of interneurons. In addition, the total number of action potentials (AP) was surprisingly low even at high frequency stimulation. This result is counterintuitive, as strong and persistent inhibitory signals are assumed to restrict GC v activation and maintain sparseness. However, amongst the early firing interneurons, late and regular spiking patterns were clearly distinguishable. Interestingly, some interneurons expressed LLH similar to the GCs, arguing that it could be a commonly used mechanism for regulation of excitability across the hippocampal network. In summary, we show that slow inhibition can result in a prolonged hyperpolarization that significantly alters concurrent input’s integration. We believe that these interactions contribute to important computational processes such as sparse encoding. Interestingly, sparseness seems to be the hallmark of the DG, as we observed a rather low activation of the interneuron network as well. However, the alternating firing of ML-INs could compensate the lack of persistent activity by the continuous GABA release across the DG. Taken together these results offer an insight into a mechanism of feedforward inhibition serving as a sparse neural code generator in the DG

GABA Neuron Alterations, Cortical Circuit Dysfunction and Cognitive Deficits in Schizophrenia

Schizophrenia is a brain disorder associated with cognitive deficits that severely affect the patients' capacity for daily functioning. Whereas our understanding of its pathophysiology is limited, postmortem studies suggest that schizophrenia is associated with deficits of GABA-mediated synaptic transmission. A major role of GABA-mediated transmission may be producing synchronized network oscillations which are currently hypothesized to be essential for normal cognitive function. Therefore, cognitive deficits in schizophrenia may result from a GABA synapse dysfunction that disturbs neural synchrony. Here, we highlight recent studies further suggesting alterations of GABA transmission and network oscillations in schizophrenia. We also review current models for the mechanisms of GABA-mediated synchronization of neural activity, focusing on parvalbumin-positive GABA neurons, which are altered in schizophrenia and whose function has been strongly linked to the production of neural synchrony. Alterations of GABA signaling that impair gamma oscillations and, as a result, cognitive function suggest paths for novel therapeutic interventions

A neurobiological and computational analysis of target discrimination in visual clutter by the insect visual system.

Some insects have the capability to detect and track small moving objects, often against cluttered moving backgrounds. Determining how this task is performed is an intriguing challenge, both from a physiological and computational perspective. Previous research has characterized higher-order neurons within the fly brain known as 'small target motion detectors‘ (STMD) that respond selectively to targets, even within complex moving surrounds. Interestingly, these cells still respond robustly when the velocity of the target is matched to the velocity of the background (i.e. with no relative motion cues). We performed intracellular recordings from intermediate-order neurons in the fly visual system (the medulla). These full-wave rectifying, transient cells (RTC) reveal independent adaptation to luminance changes of opposite signs (suggesting separate 'on‘ and 'off‘ channels) and fast adaptive temporal mechanisms (as seen in some previously described cell types). We show, via electrophysiological experiments, that the RTC is temporally responsive to rapidly changing stimuli and is well suited to serving an important function in a proposed target-detecting pathway. To model this target discrimination, we use high dynamic range (HDR) natural images to represent 'real-world‘ luminance values that serve as inputs to a biomimetic representation of photoreceptor processing. Adaptive spatiotemporal high-pass filtering (1st-order interneurons) shapes the transient 'edge-like‘ responses, useful for feature discrimination. Following this, a model for the RTC implements a nonlinear facilitation between the rapidly adapting, and independent polarity contrast channels, each with centre-surround antagonism. The recombination of the channels results in increased discrimination of small targets, of approximately the size of a single pixel, without the need for relative motion cues. This method of feature discrimination contrasts with traditional target and background motion-field computations. We show that our RTC-based target detection model is well matched to properties described for the higher-order STMD neurons, such as contrast sensitivity, height tuning and velocity tuning. The model output shows that the spatiotemporal profile of small targets is sufficiently rare within natural scene imagery to allow our highly nonlinear 'matched filter‘ to successfully detect many targets from the background. The model produces robust target discrimination across a biologically plausible range of target sizes and a range of velocities. We show that the model for small target motion detection is highly correlated to the velocity of the stimulus but not other background statistics, such as local brightness or local contrast, which normally influence target detection tasks. From an engineering perspective, we examine model elaborations for improved target discrimination via inhibitory interactions from correlation-type motion detectors, using a form of antagonism between our feature correlator and the more typical motion correlator. We also observe that a changing optimal threshold is highly correlated to the value of observer ego-motion. We present an elaborated target detection model that allows for implementation of a static optimal threshold, by scaling the target discrimination mechanism with a model-derived velocity estimation of ego-motion. Finally, we investigate the physiological relevance of this target discrimination model. We show that via very subtle image manipulation of the visual stimulus, our model accurately predicts dramatic changes in observed electrophysiological responses from STMD neurons.Thesis (Ph.D.) - University of Adelaide, School of Molecular and Biomedical Science, 200