The auditory system of the barn owl contains several spatial maps. In young barn owls raised with optical prisms over their eyes, these auditory maps are shifted to stay in register with the visual map, suggesting that the visual input imposes a frame of reference on the auditory maps. However, the optic tectum, the first site of convergence of visual with auditory information, is not the site of plasticity for the shift of the auditory maps; the plasticity occurs instead in the inferior colliculus, which contains an auditory map and projects into the optic tectum. We explored a model of the owl remapping in which a global reinforcement signal whose delivery is controlled by visual foveation. A hebb learning rule gated by rein(cid:173) forcement learned to appropriately adjust auditory maps. In addi(cid:173) tion, reinforcement learning preferentially adjusted the weights in the inferior colliculus, as in the owl brain, even though the weights were allowed to change throughout the auditory system.

The barn owl is a nocturnal hunter, capable of capturing prey using au- ditory information alone [1]. The neural basis for this localization be- havior is the existence of auditory neurons with spatial receptive fields [2]. We provide a mathematical description of the operations performed on auditory input signals by the barn owl that facilitate the creation of a representation of auditory space. To develop our model, we first formu- late the sound localization problem solved by the barn owl as a statistical estimation problem. The implementation of the solution is constrained by the known neurobiology.

Sound localization by barn owls is commonly modeled as a matching procedure where localization cues derived from auditory inputs are compared to stored templates. While the matching models can explain properties of neural responses, no model explains how the owl resolves spatial ambiguity in the localization cues to produce accurate localization near the center of gaze. Here, we examine two models for the barn owl's sound localization behavior. First, we consider a maximum likelihood estimator in order to further evaluate the cue matching model. Second, we consider a maximum a posteriori estimator to test if a Bayesian model with a prior that emphasizes directions near the center of gaze can reproduce the owl's localization behavior.

Mammals can be very smart. They also have a brain with a cortex. It has thus often been assumed that the advanced cognitive skills of mammals are closely related to the evolution of the cerebral cortex. However, birds can also be very smart, and several bird species show amazing cognitive abilities. Although birds lack a cerebral cortex, they do have pallium, and this is considered to be analogous, if not homologous, to the cerebral cortex. An outstanding feature of the mammalian cortex is its layered architecture. In a detailed anatomical study of the bird pallium, Stacho et al. describe a similarly layered architecture. Despite the nuclear organization of the bird pallium, it has a cyto-architectonic organization that is reminiscent of the mammalian cortex. Science , this issue p. [eabc5534][1] ### INTRODUCTION For more than a century, the avian forebrain has been a riddle for neuroscientists. Birds demonstrate exceptional cognitive abilities comparable to those of mammals, but their forebrain organization is radically different. Whereas mammalian cognition emerges from the canonical circuits of the six-layered neocortex, the avian forebrain seems to display a simple nuclear organization. Only one of these nuclei, the Wulst, has been generally accepted to be homologous to the neocortex. Most of the remaining pallium is constituted by a multinuclear structure called the dorsal ventricular ridge (DVR), which has no direct counterpart in mammals. Nevertheless, one long-standing theory, along with recent scientific evidence, supports the idea that some parts of the sensory DVR could display connectivity patterns, physiological signatures, and cell type–specific markers that are reminiscent of the neocortex. However, it remains unknown if the entire Wulst and sensory DVR harbor a canonical circuit that structurally resembles mammalian cortical organization. ### RATIONALE The mammalian neocortex comprises a columnar and laminar organization with orthogonally organized fibers that run in radial and tangential directions. These fibers constitute repetitive canonical circuits as computational units that process information along the radial domain and associate it tangentially. In this study, we first analyzed the pallial fiber architecture with three-dimensional polarized light imaging (3D-PLI) in pigeons and subsequently reconstructed local sensory circuits of the Wulst and the sensory DVR in pigeons and barn owls by means of in vivo or in vitro applications of neuronal tracers. We focused on two distantly related bird species to prove the hypothesis that a canonical circuit comparable to the neocortex is a genuine feature of the avian sensory forebrain. ### RESULTS The 3D-PLI fiber analysis showed that both the Wulst and the sensory DVR display an orthogonal organization of radially and tangentially organized fibers along their entire extent. In contrast, nonsensory components of the DVR displayed a complex mosaic-like arrangement with patches of fibers with different orientations. Fiber tracing revealed an iterative circuit motif that was present across modalities (somatosensory, visual, and auditory), brain regions (sensory DVR and Wulst), and species (pigeon and barn owl). Although both species showed a comparable column- and lamina-like circuit organization, small species differences were discernible, particularly for the Wulst, which was more subdifferentiated in barn owls, which fits well with the processing of stereopsis, combined with high visual acuity in the Wulst of this species. The primary sensory zones of the DVR were tightly interconnected with the intercalated nidopallial layers and the overlying mesopallium. In addition, nidopallial and some hyperpallial lamina-like areas gave rise to long-range tangential projections connecting sensory, associative, and motor structures. ### CONCLUSION Our study reveals a hitherto unknown neuroarchitecture of the avian sensory forebrain that is composed of iteratively organized canonical circuits within tangentially organized lamina-like and orthogonally positioned column-like entities. Our findings suggest that it is likely that an ancient microcircuit that already existed in the last common stem amniote might have been evolutionarily conserved and partly modified in birds and mammals. The avian version of this connectivity blueprint could conceivably generate computational properties reminiscent of the neocortex and would thus provide a neurobiological explanation for the comparable and outstanding perceptual and cognitive feats that occur in both taxa. ![Figure][2] Fiber architectures of mammalian and avian forebrains. Schematic drawings of a rat brain (left) and a pigeon brain (right) depict their overall pallial organization. The mammalian dorsal pallium harbors the six-layered neocortex with a granular input layer IV (purple) and supra- and infragranular layers II/III and V/VI, respectively (blue). The avian pallium comprises the Wulst and the DVR, which both, at first glance, display a nuclear organization. Their primary sensory input zones are shown in purple, comparable to layer IV. According to this study, both mammals and birds show an orthogonal fiber architecture constituted by radially (dark blue) and tangentially (white) oriented fibers. Tangential fibers associate distant pallial territories. Whereas this pattern dominates the whole mammalian neocortex, in birds, only the sensory DVR and the Wulst (light green) display such an architecture, and the associative and motor areas (dark green), as in the caudal DVR, are devoid of this cortex-like fiber architecture. NC, caudal nidopallium. 3D RAT BRAIN (LEFT): SCALABLE BRAIN ATLAS, RESEARCH RESOURCE IDENTIFIER (RRID) SCR_006934 Although the avian pallium seems to lack an organization akin to that of the cerebral cortex, birds exhibit extraordinary cognitive skills that are comparable to those of mammals. We analyzed the fiber architecture of the avian pallium with three-dimensional polarized light imaging and subsequently reconstructed local and associative pallial circuits with tracing techniques. We discovered an iteratively repeated, column-like neuronal circuitry across the layer-like nuclear boundaries of the hyperpallium and the sensory dorsal ventricular ridge. These circuits are connected to neighboring columns and, via tangential layer-like connections, to higher associative and motor areas. Our findings indicate that this avian canonical circuitry is similar to its mammalian counterpart and might constitute the structural basis of neuronal computation. [1]: /lookup/doi/10.1126/science.abc5534 [2]: pending:yes

Sound localization by barn owls is commonly modeled as a matching procedure where localization cues derived from auditory inputs are compared to stored templates. While the matching models can explain properties of neural responses, no model explains how the owl resolves spatial ambiguity in the localization cues to produce accurate localization near the center of gaze. Here, we examine two models for the barn owl's sound localization behavior. First, we consider a maximum likelihood estimator in order to further evaluate the cue matching model. Second, we consider a maximum a posteriori estimator to test if a Bayesian model with a prior that emphasizes directions near the center of gaze can reproduce the owl's localization behavior.

The visual and auditory map alignment in the Superior Colliculus (SC) of barn owl is important for its accurate localization for prey behavior. Prism learning or Blindness may interfere this alignment and cause loss of the capability of accurate prey. However, juvenile barn owl could recover its sensory map alignment by shifting its auditory map. The adaptation of this map alignment is believed based on activity dependent axon developing in Inferior Colliculus (IC). A model is built to explore this mechanism. In this model, axon growing process is instructed by an inhibitory network in SC while the strength of the inhibition adjusted by Spike Timing Dependent Plasticity (STDP). We test and analyze this mechanism by application of the neural structures involved in spatial localization in a robotic system.

Sound localization by barn owls is commonly modeled as a matching procedure where localization cues derived from auditory inputs are compared to stored templates. While the matching models can explain properties of neural responses, no model explains how the owl resolves spatial ambiguity in the localization cues to produce accurate localization near the center of gaze. Here, we examine two models for the barn owl's sound localization behavior. First, we consider a maximum likelihood estimator in order to further evaluate the cue matching model. Second, we consider a maximum a posteriori estimator to test if a Bayesian model with a prior that emphasizes directions near the center of gaze can reproduce the owl's localization behavior. We show that the maximum likelihood estimator can not reproduce the owl's behavior, while the maximum a posteriori estimator is able to match the behavior. This result suggests that the standard cue matching model will not be sufficient to explain sound localization behavior in the barn owl. The Bayesian model provides a new framework for analyzing sound localization in the barn owl and leads to predictions about the owl's localization behavior.

The barn owl is a nocturnal hunter, capable of capturing prey using auditory information alone [1]. The neural basis for this localization behavior is the existence of auditory neurons with spatial receptive fields [2]. We provide a mathematical description of the operations performed on auditory input signals by the barn owl that facilitate the creation of a representation of auditory space. To develop our model, we first formulate the sound localization problem solved by the barn owl as a statistical estimation problem. The implementation of the solution is constrained by the known neurobiology.

The barn owl is a nocturnal hunter, capable of capturing prey using auditory information alone [1]. The neural basis for this localization behavior is the existence of auditory neurons with spatial receptive fields [2]. We provide a mathematical description of the operations performed on auditory input signals by the barn owl that facilitate the creation of a representation of auditory space. To develop our model, we first formulate the sound localization problem solved by the barn owl as a statistical estimation problem. The implementation of the solution is constrained by the known neurobiology.

The barn owl is a nocturnal hunter, capable of capturing prey using auditory information alone [1].