Cortical mechanisms specific to explicit visual object recognition

The cortical mechanisms associated with conscious object recognition were studied using functional magnetic resonance imaging (fMRI). Participants were required to recognize pictures of masked objects that were presented very briefly, randomly and repeatedly. This design yielded a gradual accomplishment of successful recognition. Cortical activity in a ventrotemporal visual region was linearly correlated with perception of object identity. Therefore, although object recognition is rapid, awareness of an object's identity is not a discrete phenomenon but rather associated with gradually increasing cortical activity. Furthermore, the focus of the activity in the temporal cortex shifted anteriorly as subjects reported an increased knowledge regarding identity. The results presented here provide new insights into the processes underlying explicit object recognition, as well as the analysis that takes place immediately before and after recognition is possible.     

Innate mechanisms of viral recognition

Viruses are obligate parasites which can infect cells of all living organisms. Multiple antiviral defense mechanisms appeared early in the evolution of the immune system. Higher vertebrates possess the most complex antiviral immunity based on both innate and adoptive immune responses. However, a majority of living organisms, including plants and invertebrates, rely exclusively on innate immune mechanisms for protection against viral infections. There are some striking similarities in several components of innate immune recognition in mammals, plants, and insects suggesting that these signaling cascades are highly conserved in the evolution of the immune system. This review summarizes recent advances in the field of innate immune recognition of viruses, with a focus on pattern-recognition receptors.     

Neural mechanisms of object recognition

Single-unit recordings from behaving monkeys and human functional magnetic resonance imaging studies have continued to provide a host of experimental data on the properties and mechanisms of object recognition in cortex. Recent advances in object recognition, spanning issues regarding invariance, selectivity, representation and levels of recognition have allowed us to propose a putative model of object recognition in cortex.     

Learning and neural plasticity in visual object recognition

The capability of the adult primate visual system for rapid and accurate recognition of targets in cluttered, natural scenes far surpasses the abilities of state-of-the-art artificial vision systems. Understanding this capability remains a fundamental challenge in visual neuroscience. Recent experimental evidence suggests that adaptive coding strategies facilitated by underlying neural plasticity enable the adult brain to learn from visual experience and shape its ability to integrate and recognize coherent visual objects.     

Mechanisms of visual object recognition studied in monkeys

Cells in area TE of the inferotemporal cortex of the monkey brain selectively respond to various moderately complex object-features, and those responding to similar features cluster in a columnar region elongated vertical to the cortical surface. Although cells within a column respond to similar features, their selectivity is not identical. The data of optical imaging in TE have suggested that the borders between neighboring columns are not discrete but columns representing related features overlap one another. We have also found, by training adult monkeys for discrimination of a specific set of shapes, that such a long-term training increases the proportion of TE cells responding to the shapes used in the training even in the adult. The data suggested that TE plays important roles in discrimination of complex shapes and in visual expert learning of discriminating a certain class of objects in the adult.     

Signal transformation and pattern recognition in visual pathways

A model of the visual pathways it has been developed consisting of two neural systems working together: a feed-forward mechanism given by convolutions through different layers and a feed-back one consiting of the comparison of the results of the first one. The first mechanism would be predominant at lower centers, and it would be able, for example, to explain the increase in contrast sensitivity at different stations of visual pathways. The second mechanism seems apter to explain the behaviour of complex and hypercomplex cells whose activity is to a great extent independent of the position of the stimulus in the receptive field. The ideas developed in this paper could have some implications for building a machine able to recognize patterns independently of their position.     

Cortical mechanisms of sensory learning and object recognition

Learning about the world through our senses constrains our ability to recognise our surroundings. Experience shapes perception. What is the neural basis for object recognition and how are learning-induced changes in recognition manifested in neural populations? We consider first the location of neurons that appear to be critical for object recognition, before describing what is known about their function. Two complementary processes of object recognition are considered: discrimination among diagnostic object features and generalization across non-diagnostic features. Neural plasticity appears to underlie the development of discrimination and generalization for a given set of features, though tracking these changes directly over the course of learning has remained an elusive task.     

Feature blindness: A challenge for understanding and modelling visual object recognition

Humans rely heavily on the shape of objects to recognise them. Recently, it has been argued that Convolutional Neural Networks (CNNs) can also show a shape-bias, provided their learning environment contains this bias. This has led to the proposal that CNNs provide good mechanistic models of shape-bias and, more generally, human visual processing. However, it is also possible that humans and CNNs show a shape-bias for very different reasons, namely, shape-bias in humans may be a consequence of architectural and cognitive constraints whereas CNNs show a shape-bias as a consequence of learning the statistics of the environment. We investigated this question by exploring shape-bias in humans and CNNs when they learn in a novel environment. We observed that, in this new environment, humans (i) focused on shape and overlooked many non-shape features, even when non-shape features were more diagnostic, (ii) learned based on only one out of multiple predictive features, and (iii) failed to learn when global features, such as shape, were absent. This behaviour contrasted with the predictions of a statistical inference model with no priors, showing the strong role that shape-bias plays in human feature selection. It also contrasted with CNNs that (i) preferred to categorise objects based on non-shape features, and (ii) increased reliance on these non-shape features as they became more predictive. This was the case even when the CNN was pre-trained to have a shape-bias and the convolutional backbone was frozen. These results suggest that shape-bias has a different source in humans and CNNs: while learning in CNNs is driven by the statistical properties of the environment, humans are highly constrained by their previous biases, which suggests that cognitive constraints play a key role in how humans learn to recognise novel objects.     

Active manual control of object views facilitates visual recognition

Active exploration of large-scale environments leads to better learning of spatial layout than does passive observation [1] [2] [3]. But active exploration might also help us to remember the appearance of individual objects in a scene. In fact, when we encounter new objects, we often manipulate them so that they can be seen from a variety of perspectives. We present here the first evidence that active control of the visual input in this way facilitates later recognition of objects. Observers who actively rotated novel, three-dimensional objects on a computer screen later showed more efficient visual recognition than observers who passively viewed the exact same sequence of images of these virtual objects. During active exploration, the observers focused mainly on the 'side' or 'front' views of the objects (see also [4] [5] [6]). The results demonstrate that how an object is represented for later recognition is influenced by whether or not one controls the presentation of visual input during learning.     

Invariant visual object recognition: a model, with lighting invariance.

How are invariant representations of objects formed in the visual cortex? We describe a neurophysiological and computational approach which focusses on a feature hierarchy model in which invariant representations can be built by self-organizing learning based on the statistics of the visual input. The model can use temporal continuity in an associative synaptic learning rule with a short term memory trace, and/or it can use spatial continuity in Continuous Transformation learning. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and in this paper we show also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in for example spatial and object search tasks. The model has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene.     

Learning invariant object recognition in the visual system with continuous transformations

The cerebral cortex utilizes spatiotemporal continuity in the world to help build invariant representations. In vision, these might be representations of objects. The temporal continuity typical of objects has been used in an associative learning rule with a short-term memory trace to help build invariant object representations. In this paper, we show that spatial continuity can also provide a basis for helping a system to self-organize invariant representations. We introduce a new learning paradigm “continuous transformation learning” which operates by mapping spatially similar input patterns to the same postsynaptic neurons in a competitive learning system. As the inputs move through the space of possible continuous transforms (e.g. translation, rotation, etc.), the active synapses are modified onto the set of postsynaptic neurons. Because other transforms of the same stimulus overlap with previously learned exemplars, a common set of postsynaptic neurons is activated by the new transforms, and learning of the new active inputs onto the same postsynaptic neurons is facilitated. We demonstrate that a hierarchical model of cortical processing in the ventral visual system can be trained with continuous transform learning, and highlight differences in the learning of invariant representations to those achieved by trace learning.     

Neural mechanisms of infant learning: differences in frontal theta activity during object exploration modulate subsequent object recognition

Investigating learning mechanisms in infancy relies largely on behavioural measures like visual attention, which often fail to predict whether stimuli would be encoded successfully. This study explored EEG activity in the theta frequency band, previously shown to predict successful learning in adults, to directly study infants'' cognitive engagement, beyond visual attention. We tested 11-month-old infants (N = 23) and demonstrated that differences in frontal theta-band oscillations, recorded during infants'' object exploration, predicted differential subsequent recognition of these objects in a preferential-looking test. Given that theta activity is modulated by motivation to learn in adults, these findings set the ground for future investigation into the drivers of infant learning.     

Divergent mechanisms for the tuning of shortwave sensitive visual pigments in vertebrates.

Of the four classes of vertebrate cone visual pigments, the shortwave-sensitive SWS1 class shows the shortest lambda(max) values with peaks in different species in either the violet (390-435 nm) or ultraviolet (around 365 nm) regions of the spectrum. Phylogenetic evidence indicates that the ancestral pigment was probably UV-sensitive (UVS) and that the shifts between violet and UV have occurred many times during evolution. This is supported by the different mechanisms for these shifts in different species. All visual pigments possess a chromophore linked via a Schiff base to a Lys residue in opsin protein. In violet-sensitive (VS) pigments, the Schiff base is protonated whereas in UVS pigments, it is almost certainly unprotonated. The generation of VS from ancestral UVS pigments most likely involved amino acid substitutions in the opsin protein that serve to stabilise protonation. The key residues in the opsin protein for this are at sites 86 and 90 that are adjacent to the Schiff base and the counterion at Glu113. In this review, the different molecular mechanisms for the UV or violet shifts are presented and discussed in the context of the structural model of bovine rhodopsin.     

The mechanisms of kin discrimination and the evolution of kin recognition in vertebrates: a critical re-evaluation

I re-examine the four most widely proposed mechanisms of kin discrimination among vertebrates and conclude that the current categorization of kin discrimination mechanisms has been counterproductive because it has a hindered a clear understanding of the basic mechanisms by which animals discriminate kin. I suggest that there likely is only one authentic mechanism of kin discrimination and that this mechanism is learning, particularly associative learning and habituation. Observed differences in the way animals discriminate between kin and non-kin are due only to the cues (e.g., individually-distinctive, family-distinctive, or self) that are used, and not to different mechanisms per se. I also consider whether kin discrimination is mediated by specially evolved kin recognition systems, defined as neural mechanisms that allow animals to directly classify conspecifics as either kin or non-kin. A preliminary analysis of vertebrate recognition systems suggests that specialized neural, endocrine, and developmental mechanisms specifically for recognizing kin have not evolved. Rather, kin discrimination results from an extension of other, non-specialized sensory and cognitive abilities of animals, and may be derived from other forms of social recognition, such as individual, group, or species recognition.     

Amyotrophic lateral sclerosis (motor neuron disease): proposed mechanisms and pathways to treatment

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterised by loss of motor neurons. The cause of disease is unknown other than in the rare cases of familial disease arising from mutations in the superoxide dismutase 1 gene. Many theories for pathogenesis have been proposed - including oxidative stress, excitotoxicity, mitochondrial dysfunction and abnormal protein aggregation - based on studies of human post mortem tissue, research on animal models, and in vitro work. Here we review the evidence for the main pathogenic mechanisms and outline how they might interact to cause motor neuron death. Clinical trials have as yet failed to identify any truly effective therapies in ALS, with only riluzole providing a modest improvement in survival. Ongoing trials are exploring the value of antiglutamatergic agents, including the cephalosporin antibiotic ceftriaxone, as well as antioxidants, mitochondrial enhancers and anti-apoptotic drugs. It is likely that effective therapy will involve combinations of agents acting on different mechanisms. Gene therapy with neurotrophic factors will soon be in clinical trials, while work on stem cell therapy remains preclinical. In addition to finding effective therapies, research also needs to identify early disease markers because therapy is likely to be of most benefit when given early in the course of disease.