Mechanisms of Feedback in the Visual System

Feedback is an ubiquitous feature of neural systems though there is little consensus on the roles of mechanisms involved with feedback. We set up an in vivo preparation to study and characterize an accessible and isolated feedback loop within the visual system of the leopard frog, Rana pipiens. We recorded extracellularly within the nucleus isthmi, a nucleus providing direct topographic feedback to the optic tectum, a nucleus that receives the vast majority of retinal output. The optic tectum and nucleus isthmi of the amphibian are homologous structures to the superior colliculus and parabigeminal nucleus in mammals, respectively. We formulated a novel threshold for detecting neuronal spikes within a low signal-to-noise environment, as exists in the nucleus isthmi due to its high density of small neuronal cell bodies. Combining this threshold with a recently developed spike sorting procedure enabled us to extract simultaneous recordings from up to 7 neurons at a time from a single extracellular electrode. We then stimulated the frog using computer driven dynamic spatiotemporal visual stimuli to characterize the responses of the nucleus isthmi neurons. We found that the responses display surprisingly long time courses to simple visual stimuli. Furthermore, we found that when stimulated with complex contextual stimuli the response of the nucleus isthmi is quite counter-intuitive. When a stimulus is presented outside of the classical receptive field along with a stimulus within the receptive field, the response is actually higher than the response to just a stimulus within the classical receptive field. Finally, we compared the responses of all of the simultaneously recorded neurons and, together with data from in vitro experiments within the nucleus isthmi, conclude that the nucleus isthmi of the frog is composed of just one electrophysiological population of cells

Computational modeling of the brain limbic system and its application in control engineering

This study mainly deals with the various aspects of modeling the learning processes within the brain limbic system and studying the various aspects of using it for different applications in control engineering. The current study is a multi-aspect research effort which not only requires a background of control engineering, but also a basic knowledge of some biomorphic systems. The main focus of this study is on biological systems which are involved in emotional processes. In mammalians, a part of the brain called the limbic system is mainly responsible for emotional processes. Therefore, general brain emotional processes and specific aspects of the limbic system are reviewed in the early parts of this study. Next, we describe developing a computational model of the limbic system based on these concepts. Since the focus of this study is on the application of the model in engineering systems and not on the biological concepts, the model established is not a very complicated model and does not include all the components of the limbic system. In fact, we are trying to develop a model which captures the minimal and basic properties of the limbic system which are mainly known as the Amygdala-Orbitofrontal Cortex system. The main chapter of this thesis, Chapter IV, shows the utilization of the Brain Emotional Learning (BEL) model in different applications of control and signal fusion systems. The main effort is focused on applying the model to control systems where the model acts as the controller block. Furthermore, the application of the model in signal fusion is also considered where simulation results support the applicability of the model. Finally, we studied different analytical aspects of the model including the behavior of the system during the adaptation phase and the stability of the system. For the first issue, we simplify the model, e.g. remove the nonlinearities, to develop mathematical formulations for behavior of the system. To study the stability of the system, we use the cell-to-cell mapping algorithm which reveals the stability conditions of the system in different representations. This thesis finishes with some concluding remarks and some topics for future research on this field

Computational role of disinhibition in brain function

Neurons are connected to form functional networks in the brain. When neurons are combined in sequence, nontrivial effects arise. One example is disinhibition; that is, inhibition to another inhibitory factor. Disinhibition may be serving an important purpose because a large number of local circuits in the brain contain disinhibitory connections. However, their exact functional role is not well understood. The objective of this dissertation is to analyze the computational role of disinhibition in brain function, especially in visual perception and attentional control. My approach is to propose computational models of disinhibition and then map the model to the local circuits in the brain to explain psychological phenomena. Several computational models are proposed in this dissertation to account for disinhibition. (1) A static inverse difference of Gaussian filter (IDoG) is derived to account explicitly for the spatial effects of disinhibition. IDoG can explain a number of complex brightness-contrast illusions, such as the periphery problem in the Hermann grid and the White's effect. The IDoG model can also be used to explain orientation perception of multiple lines as in the modified version of Poggendorff illusion. (2) A spatio-temporal model (IDoGS) in early vision is derived and it successfully explains the scintillating grid illusion, which is a stationary display giving rise to a striking, dynamic, scintillating effect. (3) An interconnected Cohen-Grossberg neural network model (iCGNN) is proposed to address the dynamics of disinhibitory neural networks with a layered structure. I derive a set of sufficient conditions for such an interconnected system to reach asymptotic stability. (4) A computational model combining recurrent and feed-forward disinhibition is designed to account for input-modulation in temporal selective attention. The main contribution of this research is that it developed a unified framework of disinhibition to model several different kinds of neural circuits to account for various perceptual and attentional phenomena. Investigating the role of disinhibition in the brain can provide us with a deeper understanding of how the brain can give rise to intelligent and complex functions

The Role of Temporal Parameters in a Thalamocortical Model of Analogy

Abstract—How multiple specialized cortical areas in the brain interact with each other to give rise to an integrated behavior is a largely unanswered question. This paper proposes that such an integration can be understood under the framework of analogy and that part of the thalamus and the thalamic reticular nucleus (TRN) may be playing a key role in this respect. The proposed thalamocortical model of analogy heavily depends on a diverse set of temporal parameters including axonal delay and membrane time constant, each of which is critical for the proper functioning of the model. The model requires a specific set of conditions derived from the need of the model to process analogies. Computational results with a network of integrate and fire (IF) neurons suggest that these conditions are indeed necessary, and furthermore, data found in the experimental literature also support these conditions. The model suggests that there is a very good reason for each temporal parameter in the thalamocortical network having a particular value, and that to understand the integrated behavior of the brain, we need to study these parameters simultaneously, not separately. Index Terms—Analogy, axonal delay, cortiothalamic feedback, membrane time constant, thalamic reticular nucleus, thalamus. I