Odor representations in the mammalian olfactory bulb

A first key step in studying a sensory modality is to define how the brain represents the features of the sensory stimulus. This has proven to be a challenge in olfaction, where even the stimulus features have been a matter of considerable debate. In this review, we focus on olfactory representations in the first stage of the olfactory pathway, the olfactory bulb (OB). We examine the diverging viewpoints on spatially organized versus distributed representations. We then consider how odor sampling through respiration is a key part of the odorant code. Finally, we ask how the bulb handles the challenging task of representing mixtures. We suggest that current evidence points toward a representation that is spatially organized at the inputs but later distributed, with the spatial organization not being used for much computation. Nevertheless, this is a simple representation that effectively represents multiple individual odorants, as well as odor mixtures

Neurobiological mechanisms for language, symbols and concepts: Clues from brain-constrained deep neural networks

Neural networks are successfully used to imitate and model cognitive processes. However, to provide clues about the neurobiological mechanisms enabling human cognition, these models need to mimic the structure and function of real brains. Brain-constrained networks differ from classic neural networks by implementing brain similarities at different scales, ranging from the micro- and mesoscopic levels of neuronal function, local neuronal links and circuit interaction to large-scale anatomical structure and between-area connectivity. This review shows how brain-constrained neural networks can be applied to study in silico the formation of mechanisms for symbol and concept processing and to work towards neurobiological explanations of specifically human cognitive abilities. These include verbal working memory and learning of large vocabularies of symbols, semantic binding carried by specific areas of cortex, attention focusing and modulation driven by symbol type, and the acquisition of concrete and abstract concepts partly influenced by symbols. Neuronal assembly activity in the networks is analyzed to deliver putative mechanistic correlates of higher cognitive processes and to develop candidate explanations founded in established neurobiological principles

Multiple interactive memory representations underlie the induction of false memory.

Theoretical and computational models such as transfer-appropriate processing (TAP) and global matching models have emphasized the encoding-retrieval interaction of memory representations in generating false memories, but relevant neural mechanisms are still poorly understood. By manipulating the sensory modalities (visual and auditory) at different processing stages (learning and test) in the Deese-Roediger-McDermott task, we found that the auditory-learning visual-test (AV) group produced more false memories (59%) than the other three groups (42∼44%) [i.e., visual learning visual test (VV), auditory learning auditory test (AA), and visual learning auditory test (VA)]. Functional imaging results showed that the AV group's proneness to false memories was associated with (i) reduced representational match between the tested item and all studied items in the visual cortex, (ii) weakened prefrontal monitoring process due to the reliance on frontal memory signal for both targets and lures, and (iii) enhanced neural similarity for semantically related words in the temporal pole as a result of auditory learning. These results are consistent with the predictions based on the TAP and global matching models and highlight the complex interactions of representations during encoding and retrieval in distributed brain regions that contribute to false memories

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Reconstruction of neuronal activity and connectivity patterns in the zebrafish olfactory bulb

In the olfactory bulb (OB), odors evoke distributed patterns of activity across glomeruli that are reorganized by networks of interneurons (INs). This reorganization results in multiple computations including a decorrelation of activity patterns across the output neurons, the mitral cells (MCs). To understand the mechanistic basis of these computations it is essential to analyze the relationship between function and structure of the underlying circuit. I combined in vivo twophoton calcium imaging with dense circuit reconstruction from complete serial block-face electron microscopy (SBEM) stacks of the larval zebrafish OB (4.5 dpf) with a voxel size of 9x9x25nm. To address bottlenecks in the workflow of SBEM, I developed a novel embedding and staining procedure that effectively reduces surface charging in SBEM and enables to acquire SBEM stacks with at least a ten-fold increase in both, signal-to-noise as well as acquisition speed. I set up a high throughput neuron reconstruction pipeline with >30 professional tracers that is available for the scientific community (ariadne-service.com). To assure efficient and accurate circuit reconstruction, I developed PyKNOSSOS, a Python software for skeleton tracing and synapse annotation, and CORE, a skeleton consolidation procedure that combines redundant reconstruction with targeted expert input. Using these procedures I reconstructed all neurons (>1000) in the larval OB. Unlike in the adult OB, INs were rare and appeared to represent specific subtypes, indicating that different sub-circuits develop sequentially. MCs were uniglomerular whereas inter-glomerular projections of INs were complex and biased towards groups of glomeruli that receive input from common types of sensory neurons. Hence, the IN network in the OB exhibits a topological organization that is governed by glomerular identity. Calcium imaging revealed that the larval OB circuitry already decorrelates activity patterns evoked by similar odors. The comparison of inter-glomerular connectivity to the functional interactions between glomeruli indicates that pattern decorrelation depends on specific, non-random inter-glomerular IN projections. Hence, the topology of IN networks in the OB appears to be an important determinant of circuit function

A model of ant route navigation driven by scene familiarity

In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints

Vocal Tract Images Reveal Neural Representations of Sensorimotor Transformation During Speech Imitation

Imitating speech necessitates the transformation from sensory targets to vocal tract motor output, yet little is known about the representational basis of this process in the human brain. Here, we address this question by using real-time MR imaging (rtMRI) of the vocal tract and functional MRI (fMRI) of the brain in a speech imitation paradigm. Participants trained on imitating a native vowel and a similar nonnative vowel that required lip rounding. Later, participants imitated these vowels and an untrained vowel pair during separate fMRI and rtMRI runs. Univariate fMRI analyses revealed that regions including left inferior frontal gyrus were more active during sensorimotor transformation (ST) and production of nonnative vowels, compared with native vowels; further, ST for nonnative vowels activated somatomotor cortex bilaterally, compared with ST of native vowels. Using test representational similarity analysis (RSA) models constructed from participants' vocal tract images and from stimulus formant distances, we found that RSA searchlight analyses of fMRI data showed either type of model could be represented in somatomotor, temporal, cerebellar, and hippocampal neural activation patterns during ST. We thus provide the first evidence of widespread and robust cortical and subcortical neural representation of vocal tract and/or formant parameters, during prearticulatory ST