The Mechanisms and Roles of Neural Feedback Loops for Visual Processing

Feedback pathways are widely present in various sensory systems transmitting time-delayed and partly-processed information from higher to lower visual centers. Although feedback loops are abundant in visual systems, investigations focusing on the mechanisms and roles of feedback in terms of micro-circuitry and system dynamics have been largely ignored. Here, we investigate the cellular, synaptic and circuit level properties of a cholinergic isthmic neuron: Ipc) to understand the role of isthmotectal feedback loop in visual processing of red-ear turtles, Trachemys scripta elegans. Turtle isthmotectal complex contains two distinct nuclei, Ipc and Imc, which interact exclusively with the optic tectum, but are otherwise isolated from other brain areas. The cholinergic Ipc neurons receive topographic glutamatergic inputs from tectal SGP neurons and project back to upper tectal layers in a topographic manner while GABAergic Imc neurons, which also get inputs from the SGP neurons project back non-topographically to both the tectum and Ipc nucleus. We have used an isolated eye-attached whole-brain preparation for our investigations of turtle isthmotectal feedback loop. We have investigated the cellular properties of the Ipc neurons by whole-cell blind-patch recordings and found that all Ipc neurons exhibit tonic firing responses to somatic current injections that are well-modeled by a leaky integrate-and-fire neuron with spike rate adaptation. Further investigations reveal that the optic nerve stimulations generate balanced excitatory and inhibitory synaptic currents in the Ipc neurons. We have also found that synaptic connection between the Imc to Ipc neuron is inhibitory. The visual response properties of the Ipc neurons to a range of computer-generated stimuli are investigated using extracellular recordings. We have found that the Ipc neurons have a localized excitatory receptive field and show stimulus selectivity and stimulus-size tuning. We also investigate lateral interactions in the Ipc neurons in response to multiple stimuli within the visual field. Finally, we quantify the oscillatory bursts observed in Ipc responses under visual stimulations

An Operating Principle of the Cerebral Cortex, and a Cellular Mechanism for Attentional Trial-and-Error Pattern Learning and Useful Classification Extraction

A feature of the brains of intelligent animals is the ability to learn to respond to an ensemble of active neuronal inputs with a behaviorally appropriate ensemble of active neuronal outputs. Previously, a hypothesis was proposed on how this mechanism is implemented at the cellular level within the neocortical pyramidal neuron: the apical tuft or perisomatic inputs initiate "guess" neuron firings, while the basal dendrites identify input patterns based on excited synaptic clusters, with the cluster excitation strength adjusted based on reward feedback. This simple mechanism allows neurons to learn to classify their inputs in a surprisingly intelligent manner. Here, we revise and extend this hypothesis. We modify synaptic plasticity rules to align with behavioral time scale synaptic plasticity (BTSP) observed in hippocampal area CA1, making the framework more biophysically and behaviorally plausible. The neurons for the guess firings are selected in a voluntary manner via feedback connections to apical tufts in the neocortical layer 1, leading to dendritic Ca2+ spikes with burst firing, which are postulated to be neural correlates of attentional, aware processing. Once learned, the neuronal input classification is executed without voluntary or conscious control, enabling hierarchical incremental learning of classifications that is effective in our inherently classifiable world. In addition to voluntary, we propose that pyramidal neuron burst firing can be involuntary, also initiated via apical tuft inputs, drawing attention towards important cues such as novelty and noxious stimuli. We classify the excitations of neocortical pyramidal neurons into four categories based on their excitation pathway: attentional versus automatic and voluntary/acquired versus involuntary. Additionally, we hypothesize that dendrites within pyramidal neuron minicolumn bundles are coupled via depolarization...Comment: 20 pages, 13 figure

Correlated Activity and Corticothalamic Cell Function in the Early Mouse Visual System

Vision has long been the model for understanding cortical function. Great progress has been made in understanding the transformations that occur within some primary visual cortex (V1) layers, like the emergence of orientation selectivity in layer 4. Less is known about other V1 circuit elements, like the shaping of V1 input via corticothalamic projections, or the population structure of the cortico-cortical output in layer 2/3. Here, we use the mouse early visual system to investigate the structure and function of circuit elements in V1. We use two approaches: comparative physiology and optogenetics. We measured the structure of pairwise correlations in the output layer 2/3 using extracellular recordings. We find that despite a lack of organization in mouse V1 seen in other species, the specificity of connections preserves a correlation structure on multiple timescales. To investigate the role of corticogeniculate projections, we utilize a transgenic mouse line to specifically and reversibly manipulate these projections with millisecond precision. We find that activity of these cells results a mix of inhibition and excitation in the thalamus, is not spatiotemporally specific, and can affect correlated activity. Finally, we classify mouse thalamic cells according to stimuli used for cell classification in primates and cats, finding some, but not complete, homology to the processing streams of primate thalamus and further highlighting fundamentals of mammalian visual system organization

Psyche, Signals and Systems

For a century or so, the multidisciplinary nature of neuroscience has left the field fractured into distinct areas of research. In particular, the subjects of consciousness and perception present unique challenges in the attempt to build a unifying understanding bridging between the micro-, meso-, and macro-scales of the brain and psychology. This chapter outlines an integrated view of the neurophysiological systems, psychophysical signals, and theoretical considerations related to consciousness. First, we review the signals that correlate to consciousness during psychophysics experiments. We then review the underlying neural mechanisms giving rise to these signals. Finally, we discuss the computational and theoretical functions of such neural mechanisms, and begin to outline means in which these are related to ongoing theoretical research

A mechanistic model of motion processing in the early visual system

A prerequisite for the perception of motion in primates is the transformation of varying intensities of light on the retina into an estimation of position, direction and speed of coherent objects. The neuro-computational mechanisms relevant for object feature encoding have been thoroughly explored, with many neurally plausible models able to represent static visual scenes. However, motion estimation requires the comparison of successive scenes through time. Precisely how the necessary neural dynamics arise and how other related neural system components interoperate have yet to be shown in a large-scale, biologically realistic simulation. The proposed model simulates a spiking neural network computation for representing object velocities in cortical areas V1 and middle temporal area (MT). The essential neural dynamics, hypothesized to reside in networks of V1 simple cells, are implemented through recurrent population connections that generate oscillating spatiotemporal tunings. These oscillators produce a resonance response when stimuli move in an appropriate manner in their receptive fields. The simulation shows close agreement between the predicted and actual impulse responses from V1 simple cells using an ideal stimulus. By integrating the activities of like V1 simple cells over space, a local measure of visual pattern velocity can be produced. This measure is also the linear weight of an associated velocity in a retinotopic map of optical flow. As a demonstration, the classic motion stimuli of drifting sinusoidal gratings and variably coherent dots are used as test stimuli and optical flow maps are generated. Vector field representations of this structure may serve as inputs for perception and decision making processes in later brain areas

Computational models relating properties of visual neurons to natural stimulus statistics

The topic of this thesis is mathematical modeling of computations taking place in the visual system, the largest sensory system in the primate brain. While a great deal is known about how certain visual neurons respond to stimuli, a very profound question is why they respond as they do. Here this question is approached by formulating models of computation which might underlie the observed response properties. The main motivation is to improve our understanding of how the brain functions. A better understanding of the computational underpinnings of the visual system may also yield advances in medical technology or computer vision, such as development of visual prostheses, or design of computer vision algorithms. In this thesis several models of computation are examined. An underlying assumption in this work is that the statistical properties of visual stimuli are related to the structure of the visual system. The relationship has formed through the mechanisms of evolution and development. A model of computation specifies this relationship between the visual system and stimulus statistics. Such a model also contains free parameters which correspond to properties of visual neurons. The experimental evaluation of a model consists of estimation of these parameters from a large amount of natural visual data, and comparison of the resulting parameter values against neurophysiological knowledge of the properties of the neurons, or results obtained with other models. The main contribution of this thesis is the introduction of new models of computation in the primary visual cortex. The results obtained with these models suggest that one defining feature of the computations performed by a class of neurons called simple cells, is that the output of a neuron consists of periods of intense neuronal activity. It also seems that the activity levels of nearby simple cells are positively correlated over short time intervals. In addition, the probability of the occurrence of such regions of intense activity in the joint space of time and cortical area seems to be small. Another contribution of the thesis is the examination of the relationship between two previous computational models, namely independent component analysis and local spatial frequency analysis. This examination suggests that results obtained with independent component analysis share some important properties with wavelets, in the way their localization in space and frequency depends on their average spatial frequency.reviewe

Information transmission in normal vision and optogenetically resensitised dystrophic retinas

Phd ThesisThe retina is a sophisticated image processing machine, transforming the visual scene as detected by the photoreceptors into a pattern of action potentials that is sent to the brain by the retinal ganglion cells (RGCs), where it is further processed to help us understand and navigate the world. Understanding this encoding process is important on a number of levels. First, it informs the study of upstream visual processing by elucidating the signals higher visual areas receive as input and how they relate to the outside world. Second, it is important for the development of treatments for retinal blindness, such as retinal prosthetics. In this thesis, I present work using multielectrode array (MEA) recordings of RGC populations from ex-vivo retinal wholemounts to study various aspects of retinal information processing. My results fall into two main themes. In the rst part, in collaboration with Dr Geo rey Portelli and Dr Pierre Kornprobst of INRIA, I use ashed gratings of varying spatial frequency and phase to compare di erent coding strategies that the retina might use. These results show that information is encoded synergistically by pairs of neurons and that, of the codes tested, a Rank Order Code based on the relative order of ring of the rst spikes of a population of neurons following a stimulus provides information about the stimulus faster and more e ciently than other codes. In the later parts, I use optogenetic stimulation of RGCs in congenitally blind retinas to study how visual information is corrupted by the spontaneous hyperactivity that arises as a result of photoreceptor degeneration. I show that by dampening this activity with the gap junction blocker meclofenamic acid, I can improve the signal-to-noise ratio, spatial acuity and contrast sensitivity of prosthetically evoked responses. Taken together, this work provides important insights for the future development of retinal prostheses