Visual categories and concepts in the avian brain.

Birds are excellent model organisms to study perceptual categorization and concept formation. The renewed focus on avian neuroscience has sparked an explosion of new data in the field. At the same time, our understanding of sensory and particularly visual structures in the avian brain has shifted fundamentally. These recent discoveries have revealed how categorization is mediated in the avian brain and has generated a theoretical framework that goes beyond the realm of birds. We review the contribution of avian categorization research-at the methodical, behavioral, and neurobiological levels. To this end, we first introduce avian categorization from a behavioral perspective and the common elements model of categorization. Second, we describe the functional and structural organization of the avian visual system, followed by an overview of recent anatomical discoveries and the new perspective on the avian 'visual cortex'. Third, we focus on the neurocomputational basis of perceptual categorization in the bird's visual system. Fourth, an overview of the avian prefrontal cortex and the prefrontal contribution to perceptual categorization is provided. The fifth section outlines how asymmetries of the visual system contribute to categorization. Finally, we present a mechanistic view of the neural principles of avian visual categorization and its putative extension to concept learning.

Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning.

Pigeons are classic model animals to study perceptual category learning. To achieve a deeper understanding of the cognitive mechanisms of categorization, a careful consideration of the employed stimulus material and a thorough analysis of the choice behavior is mandatory. In the present study, we combined the use of "virtual phylogenesis", an evolutionary algorithm to generate artificial yet naturalistic stimuli termed digital embryos and a machine learning approach on the pigeons' pecking responses to gain insight into the underlying categorization strategies of the animals. In a forced-choice procedure, pigeons learned to categorize these stimuli and transferred their knowledge successfully to novel exemplars. We used peck tracking to identify where on the stimulus the animals pecked and further investigated whether this behavior was indicative of the pigeon's choice. Going beyond the classical analysis of the binary choice, we were able to predict the presented stimulus class based on pecking location using a k-nearest neighbor classifier, indicating that pecks are related to features of interest. By analyzing error trials with this approach, we further identified potential strategies of the pigeons to discriminate between stimulus classes. These strategies remained stable during category transfer, but differed between individuals indicating that categorization learning is not limited to a single learning strategy.

Emergence of complex dynamics of choice due to repeated exposures to extinction learning.

Extinction learning, the process of ceasing an acquired behavior in response to altered reinforcement contingencies, is not only essential for survival in a changing environment, but also plays a fundamental role in the treatment of pathological behaviors. During therapy and other forms of training involving extinction, subjects are typically exposed to several sessions with a similar structure. The effects of this repeated exposure are not well understood. Here, we studied the behavior of pigeons across several sessions of a discrimination-learning task in context A, extinction in context B, and a return to context A to test the context-dependent return of the learned responses (ABA renewal). By focusing on individual learning curves across animals, we uncovered a session-dependent variability of behavior: (1) during extinction, pigeons preferred the unrewarded alternative choice in one-third of the sessions, predominantly during the first one. (2) In later sessions, abrupt transitions of behavior at the onset of context B emerged, and (3) the renewal effect decayed as sessions progressed. We show that the observed results can be parsimoniously accounted for by a computational model based only on associative learning between stimuli and actions. Our work thus demonstrates the critical importance of studying the trial-by-trial dynamics of learning in individual sessions, and the power of "simple" associative learning processes.

The neuroscience of perceptual categorization in pigeons: A mechanistic hypothesis.

We are surrounded by an endless variation of objects. The ability to categorize these objects represents a core cognitive competence of humans and possibly all vertebrates. Research on category learning in nonhuman animals started with the seminal studies of Richard Herrnstein on the category "human" in pigeons. Since then, we have learned that pigeons are able to categorize a large number of stimulus sets, ranging from Cubist paintings to English orthography. Strangely, this prolific field has largely neglected to also study the avian neurobiology of categorization. Here, we present a hypothesis that combines experimental results and theories from categorization research in pigeons with neurobiological insights on visual processing and dopamine-mediated learning in primates. We conclude that in both fields, similar conclusions on the mechanisms of perceptual categorization have been drawn, despite very little cross-reference or communication between these two areas to date. We hypothesize that perceptual categorization is a two-component process in which stimulus features are first rapidly extracted in a feed-forward process, thereby enabling a fast subdivision along multiple category borders. In primates this seems to happen in the inferotemporal cortex, while pigeons may primarily use a cluster of associative visual forebrain areas. The second process rests on dopaminergic error-prediction learning that enables prefrontal areas to connect top down the relevant visual category dimension to the appropriate action dimension.

Landfill site selection using geographic information system and analytical hierarchy process: A case study Al-Hillah Qadhaa, Babylon, Iraq.

Al-Hillah Qadhaa is located in the central part of Iraq. It covers an area of 908 km(2) with a total population of 856,804 inhabitants. This Qadhaa is the capital of Babylon Governorate. Presently, no landfill site exists in that area based on scientific site selection criteria. For this reason, an attempt has been carried out to find the best locations for landfills. A total of 15 variables were considered in this process (groundwater depth, rivers, soil types, agricultural land use, land use, elevation, slope, gas pipelines, oil pipelines, power lines, roads, railways, urban centres, villages and archaeological sites) using a geographic information system. In addition, an analytical hierarchy process was used to identify the weight for each variable. Two suitable candidate landfill sites were determined that fulfil the requirements with an area of 9.153 km(2) and 8.204 km(2) These sites can accommodate solid waste till 2030.

Neurons in the pigeon nidopallium caudolaterale signal the selection and execution of perceptual decisions.

Sensory systems provide organisms with information on the current status of the environment, thus enabling adaptive behavior. The neural mechanisms by which sensory information is exploited for action selection are typically studied with mammalian subjects performing perceptual decision-making tasks, and most of what is known about these mechanisms at the single-neuron level is derived from cortical recordings in behaving monkeys. To explore the generality of neural mechanisms underlying perceptual decision making across species, we recorded single-neuron activity in the pigeon nidopallium caudolaterale (NCL), a non-laminated associative forebrain structure thought to be functionally equivalent to mammalian prefrontal cortex, while subjects performed a visual categorisation task. We found that, whereas the majority of NCL neurons unspecifically upregulated or downregulated activity during stimulus presentation, ~20% of neurons exhibited differential activity for the sample stimuli and predicted upcoming choices. Moreover, neural activity in these neurons was ramping up during stimulus presentation and remained elevated until a choice was initiated, a response pattern similar to that found in monkey prefrontal and parietal cortices in saccadic choice tasks. In addition, many NCL neurons coded for movement direction during choice execution and differentiated between choice outcomes (reward and punishment). Taken together, our results implicate the NCL in the selection and execution of operant responses, an interpretation resonating well with the results of previous lesion studies. The resemblance of the response patterns of NCL neurons to those observed in mammalian cortex suggests that, despite differing neural architectures, mechanisms for perceptual decision making are similar across classes of vertebrates.

Why birds are smart.

Many cognitive neuroscientists believe that both a large brain and an isocortex are crucial for complex cognition. Yet corvids and parrots possess non-cortical brains of just 1-25 g, and these birds exhibit cognitive abilities comparable with those of great apes such as chimpanzees, which have brains of about 400 g. This opinion explores how this cognitive equivalence is possible. We propose four features that may be required for complex cognition: a large number of associative pallial neurons, a prefrontal cortex (PFC)-like area, a dense dopaminergic innervation of association areas, and dynamic neurophysiological fundaments for working memory. These four neural features have convergently evolved and may therefore represent 'hard to replace' mechanisms enabling complex cognition.

Working memory performance is tied to stimulus complexity.

Working memory is the cognitive capability to maintain and process information over short periods. Behavioral and computational studies have shown that visual information is associated with working memory performance. However, the underlying neural correlates remain unknown. To identify how visual information affects working memory performance, we conducted behavioral experiments in pigeons (Columba livia) and single unit recordings in the avian prefrontal analog, the nidopallium caudolaterale (NCL). Complex pictures featuring luminance, spatial and color information, were associated with higher working memory performance compared to uniform gray pictures in conjunction with distinct neural coding patterns. For complex pictures, we found a multiplexed neuronal code displaying visual and value-related features that switched to a representation of the upcoming choice during a delay period. When processing gray stimuli, NCL neurons did not multiplex and exclusively represented the choice already during stimulus presentation and throughout the delay period. The prolonged representation possibly resulted in a decay of the memory trace ultimately leading to a decrease in performance. In conclusion, we found that high stimulus complexity is associated with neuronal multiplexing of the working memory representation possibly allowing a facilitated read-out of the neural code resulting in enhancement of working memory performance.

Neurons in the pigeon visual network discriminate between faces, scrambled faces, and sine grating images.

Discriminating between object categories (e.g., conspecifics, food, potential predators) is a critical function of the primate and bird visual systems. We examined whether a similar hierarchical organization in the ventral stream that operates for processing faces in monkeys also exists in the avian visual system. We performed electrophysiological recordings from the pigeon Wulst of the thalamofugal pathway, in addition to the entopallium (ENTO) and mesopallium ventrolaterale (MVL) of the tectofugal pathway, while pigeons viewed images of faces, scrambled controls, and sine gratings. A greater proportion of MVL neurons fired to the stimuli, and linear discriminant analysis revealed that the population response of MVL neurons distinguished between the stimuli with greater capacity than ENTO and Wulst neurons. While MVL neurons displayed the greatest response selectivity, in contrast to the primate system no neurons were strongly face-selective and some responded best to the scrambled images. These findings suggest that MVL is primarily involved in processing the local features of images, much like the early visual cortex.

Trial-by-trial dynamics of reward prediction error-associated signals during extinction learning and renewal.

Reward prediction errors (RPEs) have been suggested to drive associative learning processes, but their precise temporal dynamics at the single-neuron level remain elusive. Here, we studied the neural correlates of RPEs, focusing on their trial-by-trial dynamics during an operant extinction learning paradigm. Within a single behavioral session, pigeons went through acquisition, extinction and renewal - the context-dependent response recovery after extinction. We recorded single units from the avian prefrontal cortex analogue, the nidopallium caudolaterale (NCL) and found that the omission of reward during extinction led to a peak of population activity that moved backwards in time as trials progressed. The chronological order of these signal changes during the progress of learning was indicative of temporal shifts of RPE signals that started during reward omission and then moved backwards to the presentation of the conditioned stimulus. Switches from operant choices to avoidance behavior (and vice versa) coincided with changes in population activity during the animals' decision-making. On the single unit level, we found more diverse patterns where some neurons' activity correlated with RPE signals whereas others correlated with the absolute value during the outcome period. Finally, we demonstrated that mere sensory contextual changes during the renewal test were sufficient to elicit signals likely associated with RPEs. Thus, RPEs are truly expectancy-driven since they can be elicited by changes in reward expectation, without an actual change in the quality or quantity of reward.

Avian pallial circuits and cognition: A comparison to mammals.

Cognitive functions are similar in birds and mammals. So, are therefore pallial cellular circuits and neuronal computations also alike? In search of answers, we move in from bird's pallial connectomes, to cortex-like sensory canonical circuits and connections, to forebrain micro-circuitries and finally to the avian "prefrontal" area. This voyage from macro- to micro-scale networks and areas reveals that both birds and mammals evolved similar neural and computational properties in either convergent or parallel manner, based upon circuitries inherited from common ancestry. Thus, these two vertebrate classes evolved separately within 315 million years with highly similar pallial architectures that produce comparable cognitive functions.

AAV1 is the optimal viral vector for optogenetic experiments in pigeons (Columba livia).

Although optogenetics has revolutionized rodent neuroscience, it is still rarely used in other model organisms as the efficiencies of viral gene transfer differ between species and comprehensive viral transduction studies are rare. However, for comparative research, birds offer valuable model organisms as they have excellent visual and cognitive capabilities. Therefore, the following study establishes optogenetics in pigeons on histological, physiological, and behavioral levels. We show that AAV1 is the most efficient viral vector in various brain regions and leads to extensive anterograde and retrograde ChR2 expression when combined with the CAG promoter. Furthermore, transient optical stimulation of ChR2 expressing cells in the entopallium decreases pigeons' contrast sensitivity during a grayscale discrimination task. This finding demonstrates causal evidence for the involvement of the entopallium in contrast perception as well as a proof of principle for optogenetics in pigeons and provides the groundwork for various other methods that rely on viral gene transfer in birds.

How competitive is cue competition?

Cue competition refers to phenomena indicating that learning about the relationship between a cue and an outcome is influenced by learning about the predictive significance of other cues that are concurrently present. In two autoshaping experiments with pigeons, we investigated the strength of competition among cues for predictive value. In each experiment, animals received an overexpectation training (A+, D+ followed by AD+). In addition, the training schedule of each experiment comprised two control conditions-one condition to evaluate the presence of overexpectation (B+ followed by BY+) and a second one to assess the strength of competition among cues (C+ followed by CZ-). Training trials were followed by a test with individual stimuli (A, B, C). Experiment 1 revealed no evidence for cue competition as responding during the test mirrored the individual cue-outcome contingencies. The test results from Experiment 2, which included an outcome additivity training, showed cue competition in form of an overexpectation effect as responding was weaker for Stimulus A than Stimulus B. However, the test results from Experiment 2 also revealed that responding to Stimulus A was stronger than to Stimulus C, which indicates that competition among cues was not as strong as predicted by some influential theories of associative learning.

Event-related functional MRI of awake behaving pigeons at 7T.

Animal-fMRI is a powerful method to understand neural mechanisms of cognition, but it remains a major challenge to scan actively participating small animals under low-stress conditions. Here, we present an event-related functional MRI platform in awake pigeons using single-shot RARE fMRI to investigate the neural fundaments for visually-guided decision making. We established a head-fixated Go/NoGo paradigm, which the animals quickly learned under low-stress conditions. The animals were motivated by water reward and behavior was assessed by logging mandibulations during the fMRI experiment with close to zero motion artifacts over hundreds of repeats. To achieve optimal results, we characterized the species-specific hemodynamic response function. As a proof-of-principle, we run a color discrimination task and discovered differential neural networks for Go-, NoGo-, and response execution-phases. Our findings open the door to visualize the neural fundaments of perceptual and cognitive functions in birds-a vertebrate class of which some clades are cognitively on par with primates.

Blocking NMDA-Receptors in the Pigeon's Medial Striatum Impairs Extinction Acquisition and Induces a Motoric Disinhibition in an Appetitive Classical Conditioning Paradigm.

The medial striatum of birds resembles the mammalian dorsal striatum, which plays a key role in the extinction of learned behavior. To uncover the variant and invariant neural properties of extinction learning across species, we use pigeons as an animal model in an appetitive extinction paradigm. Here, we targeted a medial sub-region of the pigeon's striatum that receives executive, visual and motor pallial projections. By locally antagonizing the N-methyl-D-aspartate (NMDA) receptors through 2-Amino-5-phosphonovalerianacid (APV) during extinction, we observed an unspecific disinhibition effect, namely an increase in conditioned pecking to a rewarded control stimulus. In addition, blocking the NMDA receptors substantially deteriorated the extinction acquisition, implying that the pigeons still responded vigorously to the CS- even without food reward during extinction. After correcting for the unspecific effect of APV, the impaired extinction acquisition remained significant, which leads to the assumption that the delayed extinction effect is possibly caused by deficits in the updating of value coding of altered reward contingencies. Also, the APV-induced disinhibition seems to result from local hyperactivity that primarily drives actions towards cues of high appetitive value. The overall correspondence of our results with those from mammals suggests common neural substrates of extinction and highlights the shared functionality of the avian and mammalian dorsal striatum despite 300 million years of independent evolution.

Renewal of extinguished behavior in pigeons (Columba livia) does not require memory consolidation of acquisition or extinction in a free-operant appetitive conditioning paradigm.

Extinction learning is a fundamental capacity for adaptive and flexible behavior. As extinguished conditioned responding is prone to relapse under certain conditions, the necessity of memory consolidation for recovery phenomena to occur has been highlighted recently. Several studies have demonstrated that both acquisition and extinction training need to be properly consolidated for a relapse of the original acquired memory trace to occur. Does this imply that extinguished responses cannot relapse before memory consolidation? To answer this question, we investigated the renewal effect subsequent to an immediate or a delayed (24 h) extinction in a discriminative operant conditioning paradigm. In three different experiments, we could show (1) that acquisition learning does not need to be long-term consolidated for the occurrence of renewal, (2) that the offset of extinction training is a reliable marker for extinction recall in a free-operant extinction learning paradigm where organisms undergo consecutive acquisition training, extinction training as well as testing of conditioned responding and (3), that immediate and long-term consolidated renewal do not demonstrate any qualitative difference in terms of the behavioral output. Our results indicate on the behavioral level that the inhibitory nature of extinction is already present in free-operant learning paradigms and that it does not seem to be affected by the absence of long-term memory consolidation.

Emerging category representation in the visual forebrain hierarchy of pigeons (Columba livia).

Recognizing and categorizing visual stimuli are cognitive functions vital for survival, and an important feature of visual systems in primates as well as in birds. Visual stimuli are processed along the ventral visual pathway. At every stage in the hierarchy, neurons respond selectively to more complex features, transforming the population representation of the stimuli. It is therefore easier to read-out category information in higher visual areas. While explicit category representations have been observed in the primate brain, less is known on equivalent processes in the avian brain. Even though their brain anatomies are radically different, it has been hypothesized that visual object representations are comparable across mammals and birds. In the present study, we investigated category representations in the pigeon visual forebrain using recordings from single cells responding to photographs of real-world objects. Using a linear classifier, we found that the population activity in the visual associative area mesopallium ventrolaterale (MVL) distinguishes between animate and inanimate objects, although this distinction is not required by the task. By contrast, a population of cells in the entopallium, a region that is lower in the hierarchy of visual areas and that is related to the primate extrastriate cortex, lacked this information. A model that pools responses of simple cells, which function as edge detectors, can account for the animate vs. inanimate categorization in the MVL, but performance in the model is based on different features than in MVL. Therefore, processing in MVL cells is very likely more abstract than simple computations on the output of edge detectors.

Landfill sites selection using MCDM and comparing method of change detection for Babylon Governorate, Iraq.

Landfill site's selection represents a complicated process due to the large number of variables to be adopted. In this study, an arid area (Babylon Governorate as a case study) was selected. It is located in the middle region of Iraq. In this area, the landfills do not satisfy the required international criteria. Fifteen of the most significant criteria were selected for this purpose. For suitable weight for each criterion, the multi-criteria decision-making (MCDM) methods were applied. These methods are AHP and RSW. In the GIS software 10.5, the raster maps of the chosen criterion were arranged and analyzed. The method of change detection was implemented to determine the matching pixels and non-matching pixels. The final results showed that there are two candidate locations for landfills for each district in the governorate (ten sites). The areas of the selected sites were sufficient to contain the cumulative quantity of solid waste from 2020 until 2030.

Active electrolocation in Gnathonemus petersii: behaviour, sensory performance, and receptor systems.

Weakly electric fish can serve as model systems for active sensing because they actively emit electric signals into the environment, which they also perceive with more than 2000 electroreceptor organs (mormyromasts) distributed over almost their entire skin surface. In a process called active electrolocation, animals are able to detect and analyse objects in their environment, which allows them to perceive a detailed electrical picture of their surroundings even in complete darkness. The African mormyrid fish Gnathonemus petersii can not only detect nearby objects, but in addition can perceive other properties such as their distance, their complex electrical impedance, and their three-dimensional shape. Because most of the sensory signals the fish perceive during their nightly activity period are self-produced, evolution has shaped and adapted the mechanisms for signal production, signal perception and signal analysis by the brain. Like in many other sensory systems, so-called prereceptor mechanisms exist, which passively improve the sensory signals in such a way that the signal carrier is optimized for the extraction of relevant sensory information. In G. petersii prereceptor mechanisms include properties of the animal's skin and internal tissue and the shape of the fish's body. These lead to a specific design of the signal carrier at different skin regions of the fish, preparing them to perform certain detection tasks. Prereceptor mechanisms also ensure that the moveable skin appendix of G. petersii, the 'Schnauzenorgan', receives an optimal sensory signal during all stages of its movement. Another important aspect of active sensing in G. petersii concerns the locomotor strategies during electrolocation. When foraging, the animals adopt a particular position with the body slanted forward bringing the so-called 'nasal region' in a position to examine the environment in front of and at the side of the fish. Simultaneously, the Schnauzenorgan performs rhythmic left-right searching movements. When an object of interest is encountered, the Schnauzenorgan is brought in a twitching movement towards the object and is moved over it for further exploration. The densities of electroreceptor organs is extraordinary high at the Schnauzenorgan and, to a lesser extend, at the nasal region. In these so-called foveal regions, the mormyromasts have a different morphology compared to other parts of the electroreceptive skin. Our results on mormyromast density and morphology, prereceptor mechanisms and electric images, central processing of electroreceptive information, and on behavioural strategies of G. petersii lead us to formulate the hypothesis that these fish possess two separate electric foveae, each of which is specialized for certain perceptional tasks.

Categories in the pigeon brain: A reverse engineering approach.

Pigeons are well known for their visual capabilities as well as their ability to categorize visual stimuli at both the basic and superordinate level. We adopt a reverse engineering approach to study categorization learning: Instead of training pigeons on predefined categories, we simply present stimuli and analyze neural output in search of categorical clustering on a solely neural level. We presented artificial stimuli, pictorial and grating stimuli, to pigeons without the need of any differential behavioral responding while recording from the nidopallium frontolaterale (NFL), a higher visual area in the avian brain. The pictorial stimuli differed in color and shape; the gratings differed in spatial frequency and amplitude. We computed representational dissimilarity matrices to reveal categorical clustering based on both neural data and pecking behavior. Based on neural output of the NFL, pictorial and grating stimuli were differentially represented in the brain. Pecking behavior showed a similar pattern, but to a lesser extent. A further subclustering within pictorial stimuli according to color and shape, and within gratings according to frequency and amplitude, was not present. Our study gives proof-of-concept that this reverse engineering approach-namely reading out categorical information from neural data--can be quite helpful in understanding the neural underpinnings of categorization learning.