No-reference visually significant blocking artifact metric for natural scene images

Quantifying visually annoying blocking artifacts is essential for image and video quality assessment. This paper presents a no-reference technique that uses the multi neural channels aspect of human visual system (HVS) to quantify visual impairment by altering the outputs of these sensory channels independently using statistical “standard score” formula in the Fourier domain. It also uses the bit patterns of the least significant bits (LSB) to extract blocking artifacts. Simulation results show that the blocking artifact extracted using this approach follows subjective visual interpretation of blocking artifacts. This paper also presents a visually significant blocking artifact metric (VSBAM) along with some experimental results

Timing Precision in Population Coding of Natural Scenes in the Early Visual System

The timing of spiking activity across neurons is a fundamental aspect of the neural population code. Individual neurons in the retina, thalamus, and cortex can have very precise and repeatable responses but exhibit degraded temporal precision in response to suboptimal stimuli. To investigate the functional implications for neural populations in natural conditions, we recorded in vivo the simultaneous responses, to movies of natural scenes, of multiple thalamic neurons likely converging to a common neuronal target in primary visual cortex. We show that the response of individual neurons is less precise at lower contrast, but that spike timing precision across neurons is relatively insensitive to global changes in visual contrast. Overall, spike timing precision within and across cells is on the order of 10 ms. Since closely timed spikes are more efficient in inducing a spike in downstream cortical neurons, and since fine temporal precision is necessary to represent the more slowly varying natural environment, we argue that preserving relative spike timing at a ∼10-ms resolution is a crucial property of the neural code entering cortex

Dynamic Encoding of Natural Luminance Sequences by LGN Bursts

In the lateral geniculate nucleus (LGN) of the thalamus, visual stimulation produces two distinct types of responses known as tonic and burst. Due to the dynamics of the T-type Ca (2+) channels involved in burst generation, the type of response evoked by a particular stimulus depends on the resting membrane potential, which is controlled by a network of modulatory connections from other brain areas. In this study, we use simulated responses to natural scene movies to describe how modulatory and stimulus-driven changes in LGN membrane potential interact to determine the luminance sequences that trigger burst responses. We find that at low resting potentials, when the T channels are de-inactivated and bursts are relatively frequent, an excitatory stimulus transient alone is sufficient to evoke a burst. However, to evoke a burst at high resting potentials, when the T channels are inactivated and bursts are relatively rare, prolonged inhibitory stimulation followed by an excitatory transient is required. We also observe evidence of these effects in vivo, where analysis of experimental recordings demonstrates that the luminance sequences that trigger bursts can vary dramatically with the overall burst percentage of the response. To characterize the functional consequences of the effects of resting potential on burst generation, we simulate LGN responses to different luminance sequences at a range of resting potentials with and without a mechanism for generating bursts. Using analysis based on signal detection theory, we show that bursts enhance detection of specific luminance sequences, ranging from the onset of excitatory sequences at low resting potentials to the offset of inhibitory sequences at high resting potentials. These results suggest a dynamic role for burst responses during visual processing that may change according to behavioral state

Development of Maps of Simple and Complex Cells in the Primary Visual Cortex

Hubel and Wiesel (1962) classified primary visual cortex (V1) neurons as either simple, with responses modulated by the spatial phase of a sine grating, or complex, i.e., largely phase invariant. Much progress has been made in understanding how simple-cells develop, and there are now detailed computational models establishing how they can form topographic maps ordered by orientation preference. There are also models of how complex cells can develop using outputs from simple cells with different phase preferences, but no model of how a topographic orientation map of complex cells could be formed based on the actual connectivity patterns found in V1. Addressing this question is important, because the majority of existing developmental models of simple-cell maps group neurons selective to similar spatial phases together, which is contrary to experimental evidence, and makes it difficult to construct complex cells. Overcoming this limitation is not trivial, because mechanisms responsible for map development drive receptive fields (RF) of nearby neurons to be highly correlated, while co-oriented RFs of opposite phases are anti-correlated. In this work, we model V1 as two topographically organized sheets representing cortical layer 4 and 2/3. Only layer 4 receives direct thalamic input. Both sheets are connected with narrow feed-forward and feedback connectivity. Only layer 2/3 contains strong long-range lateral connectivity, in line with current anatomical findings. Initially all weights in the model are random, and each is modified via a Hebbian learning rule. The model develops smooth, matching, orientation preference maps in both sheets. Layer 4 units become simple cells, with phase preference arranged randomly, while those in layer 2/3 are primarily complex cells. To our knowledge this model is the first explaining how simple cells can develop with random phase preference, and how maps of complex cells can develop, using only realistic patterns of connectivity

The statistics of local motion signals in naturalistic movies

Extraction of motion from visual input plays an important role in many visual tasks, such as separation of figure from ground and navigation through space. Several kinds of local motion signals have been distinguished based on mathematical and computational considerations (e.g., motion based on spatiotemporal correlation of luminance, and motion based on spatiotemporal correlation of flicker), but little is known about the prevalence of these different kinds of signals in the real world. To address this question, we first note that different kinds of local motion signals (e.g., Fourier, non-Fourier, and glider) are characterized by second-and higher-order correlations in slanted spatiotemporal regions. The prevalence of local motion signals in natural scenes can thus be estimated by measuring the extent to which each of these correlations are present in space-time patches and whether they are coherent across spatiotemporal scales. We apply this technique to several popular movies. The results show that all three kinds of local motion signals are present in natural movies. While the balance of the different kinds of motion signals varies from segment to segment during the course of each movie, the overall pattern of prevalence of the different kinds of motion and their subtypes, and the correlations between them, is strikingly similar across movies (but is absent from white noise movies). In sum, naturalistic movies contain a diversity of local motion signals that occur with a consistent prevalence and pattern of covariation, indicating a substantial regularity of their high-order spatiotemporal image statistics

Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer

Distributed neural processing likely entails the capability of networks to reconfigure dynamically the directionality and strength of their functional connections. Yet, the neural mechanisms that may allow such dynamic routing of the information flow are not yet fully understood. We investigated the role of gamma band (50–80 Hz) oscillations in transient modulations of communication among neural populations by using measures of direction-specific causal information transfer. We found that the local phase of gamma-band rhythmic activity exerted a stimulus-modulated and spatially-asymmetric directed effect on the firing rate of spatially separated populations within the primary visual cortex. The relationships between gamma phases at different sites (phase shifts) could be described as a stimulus-modulated gamma-band wave propagating along the spatial directions with the largest information transfer. We observed transient stimulus-related changes in the spatial configuration of phases (compatible with changes in direction of gamma wave propagation) accompanied by a relative increase of the amount of information flowing along the instantaneous direction of the gamma wave. These effects were specific to the gamma-band and suggest that the time-varying relationships between gamma phases at different locations mark, and possibly causally mediate, the dynamic reconfiguration of functional connections

The diverse roles of inhibition in identified neural circuits

Inhibitory interneurons represent a diverse population of cell types in the central nervous system, whose general role is to suppress activity of target neurons. The timing of spikes in principal neurons has millisecond precision, and I asked what are the roles of inhibition in shaping the temporal codes that emerge from different parallel local neural circuits. First I investigated the local circuitry of melanopsin-containing ganglion cells in the mouse retina, which are intrinsically photosensitive and responsible for circadian photoentrainment. Using transsynaptic viral tracing, I identified three types of melanopsin-containing ganglion cell, and found that inhibitory (GABAergic) dopaminergic amacrine cells are presynaptic to one of these types. These results provided a direct circuitry link between the medium time scale process of light-dark adaptation, which involves dopamine, and the longer time scale of the circadian rhythm. Next I characterised a subpopulation of genetically-identified neurons in the mouse retina, in order to compare the precise timing of inhibition in different circuits at a high temporal resolution. I identified eight physiologically and morphologically distinct ganglion cell types and found that each circuit could be described by a 'motif' that represented the inhibitory-excitatory interactions that lead to cell-type-specific firing patterns. The cell would fire only when the change in excitation was faster than the change in inhibition. Therefore the role of inhibition is to detect 'irrelevance' in the visual scene, only allowing the ganglion cell to fire at specific time points relating to functions that are both parallel and complementary to the other cell types. Finally, I looked deeper within the neural circuitry of one of the genetically-identified cell types, to study the mechanism of 'fast inhibition' in detecting approaching objects. Through two-photon targeted paired recordings of postsynaptic ganglion cells and presynaptic amacrine cells, I found evidence that the AII amacrine cell - a well-characterised glycinergic inhibitory interneuron known to be involved in night vision circuits - conveys fast inhibitory information to the ganglion cell via an electrical synapse with an excitatory neuron of day vision circuitry only during non-approach motion. Therefore, it appears that the role of inhibition is to dynamically interact with direct excitatory neural pathways during 'irrelevant' stimulation, suppressing or completely blocking activity, resulting in precisely timed spikes that occur in the brief moments when excitation changes faster than inhibition

Investigation of police decision making using combined EEG and virtual reality methods

Police officers in the UK are granted additional powers to allow them to protect life and property crime. Of these powers, the sanction to use stopping, potentially lethal, force given to Authorised Firearms Officers (AFO) is arguably the most salient. Each decision made by an AFO to discharge their firearm or not has great impact and so it is important we research the cognitive processes that lead to such a decision.One challenge of researching these cognitive processes is eliciting ecologically valid behaviour while maintaining internal validity. We approached this challenge by developing combined electroencephalography (EEG) and virtual reality research methods. Using these methods, we produced scenarios that reflected features of AFO training. First, we tested simple versions of the scenarios on a novice population. Following this, we increased the complexity of the scenarios and collected data from both AFOs and novices.We found that participants were fastest when responding to threatening scenarios. Further, AFOs had consistently faster response times than novices. In line with similar ‘Go/No-Go’ paradigms, we found greater increases in pre-response frontal-midline theta when participants did not shoot versus when they did. Comparisons of EEG between AFOs and novices revealed greater pre-response increases in frontal-midline theta and central delta when they equipped a firearm. Greater differences in delta activity were also observed between different levels of threat in the AFO group.Together, these findings suggest that differences in performance between experts and novices may be due to their greater attention towards threat. Further investigation of expert decision making should build on our use of naturalistic stimuli and expert participants to ensure that findings are ecologically valid.With increasing accessibility of modern game engines and virtual reality technology, this approach will be beneficial to researchers in many fields where ecological validity is required

Temporal correlations of orientations in natural scenes

The visual system performs complicated operations such as visual grouping efficiently on its natural input. To study this adaptation to natural stimuli we measure spatio-temporal interactions of orientations in scenes with natural temporal structure recorded using a camera mounted to a cat’s head. We find long range spatial and long lasting temporal correlations of orientations with collinear interactions being most prevalent and preserved over time. The spatial extent of correlations corresponds to the length of horizontal cortical connections and the temporal duration of the interactions allows co-activation of lateral and bottom up input by the same visual event