Medial axis shape coding in macaque inferotemporal cortex

The basic, still unanswered question about visual object representation is this: what specific information is encoded by neural signals? Theorists have long predicted that neurons would encode medial axis or skeletal object shape, yet recent studies reveal instead neural coding of boundary or surface shape. Here, we addressed this theoretical/experimental disconnect, using adaptive shape sampling to demonstrate explicit coding of medial axis shape in high-level object cortex (macaque monkey inferotemporal cortex or IT). Our metric shape analyses revealed a coding continuum, along which most neurons represent a configuration of both medial axis and surface components. Thus, IT response functions embody a rich basis set for simultaneously representing skeletal and external shape of complex objects. This would be especially useful for representing biological shapes, which are often characterized by both complex, articulated skeletal structure and specific surface features.     

Shaping the dynamics of a bidirectional neural interface

Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations.     

Visual information processing using bacteriorhodopsin-based complex LB films

Image extraction and visual information processing using bacteriorhodopsin (bR)-based bioelectronic devices is presented. Image extraction was achieved using a photoreceptor consisting of bR and spiropyran films. The undesired signals from the photoreceptor were automatically eliminated from the whole signal by spiropyran films acting as an optical noise filter that increases the target signal to an undesired signal ratio. For the information processing, the photoreceptor consisting of bR and lipid films deposited with different configurations was used and the target signals were processed to achieve the pattern recognition. The pattern recognition was based on not only the response variability of bacteriorhodopsin, induced by different film configurations, but also on the initial learning process. The input patterns were predicted by simple calculation with the known signals through the initial learning process.     

Multiple gamma rhythms carry distinct spatial frequency information in primary visual cortex

Gamma rhythms in many brain regions, including the primary visual cortex (V1), are thought to play a role in information processing. Here, we report a surprising finding of 3 narrowband gamma rhythms in V1 that processed distinct spatial frequency (SF) signals and had different neural origins. The low gamma (LG; 25 to 40 Hz) rhythm was generated at the V1 superficial layer and preferred a higher SF compared with spike activity, whereas both the medium gamma (MG; 40 to 65 Hz), generated at the cortical level, and the high gamma HG; (65 to 85 Hz), originated precortically, preferred lower SF information. Furthermore, compared with the rates of spike activity, the powers of the 3 gammas had better performance in discriminating the edge and surface of simple objects. These findings suggest that gamma rhythms reflect the neural dynamics of neural circuitries that process different SF information in the visual system, which may be crucial for multiplexing SF information and synchronizing different features of an object.

Gamma rhythms in many brain regions are thought to play a role in information processing. This study reports the surprising coexistence of three narrow-band gamma rhythms in visual cortex with distinct coding properties for visual features and distinct neural origins.     

Transformation of shape information in the ventral pathway

Object perception seems effortless to us, but it depends on intensive neural processing across multiple stages in ventral pathway visual cortex. Shape information at the retinal level is hopelessly complex, variable and implicit. The ventral pathway must somehow transform retinal signals into much more compact, stable and explicit representations of object shape. Recent findings highlight key aspects of this transformation: higher-order contour derivatives, structural representation in object-based coordinates, composite shape tuning dimensions, and long-term storage of object knowledge. These coding principles could help to explain our remarkable ability to perceive, distinguish, remember and understand a virtual infinity of objects.     

Object localization with whiskers

Rats use their large facial hairs (whiskers) to detect, localize and identify objects in their proximal three-dimensional (3D) space. Here, we focus on recent evidence of how object location is encoded in the neural sensory pathways of the rat whisker system. Behavioral and neuronal observations have recently converged to the point where object location in 3D appears to be encoded by an efficient orthogonal scheme supported by primary sensory-afferents: each primary-afferent can signal object location by a spatial (labeled-line) code for the vertical axis (along whisker arcs), a temporal code for the horizontal axis (along whisker rows), and an intensity code for the radial axis (from the face out). Neuronal evidence shows that (i) the identities of activated sensory neurons convey information about the vertical coordinate of an object, (ii) the timing of their firing, in relation to other reference signals, conveys information about the horizontal object coordinate, and (iii) the intensity of firing conveys information about the radial object coordinate. Such a triple-coding scheme allows for efficient multiplexing of 3D object location information in the activity of single neurons. Also, this scheme provides redundancy since the same information may be represented in the activity of many neurons. These features of orthogonal coding increase accuracy and reliability. We propose that the multiplexed information is conveyed in parallel to different readout circuits, each decoding a specific spatial variable. Such decoding reduces ambiguity, and simplifies the required decoding algorithms, since different readout circuits can be optimized for a particular variable.     

From sensors to spikes: evolving receptive fields to enhance sensorimotor information in a robot-arm

In biological systems, instead of actual encoders at different joints, proprioception signals are acquired through distributed receptive fields. In robotics, a single and accurate sensor output per link (encoder) is commonly used to track the position and the velocity. Interfacing bio-inspired control systems with spiking neural networks emulating the cerebellum with conventional robots is not a straight forward task. Therefore, it is necessary to adapt this one-dimensional measure (encoder output) into a multidimensional space (inputs for a spiking neural network) to connect, for instance, the spiking cerebellar architecture; i.e. a translation from an analog space into a distributed population coding in terms of spikes. This paper analyzes how evolved receptive fields (optimized towards information transmission) can efficiently generate a sensorimotor representation that facilitates its discrimination from other "sensorimotor states". This can be seen as an abstraction of the Cuneate Nucleus (CN) functionality in a robot-arm scenario. We model the CN as a spiking neuron population coding in time according to the response of mechanoreceptors during a multi-joint movement in a robot joint space. An encoding scheme that takes into account the relative spiking time of the signals propagating from peripheral nerve fibers to second-order somatosensory neurons is proposed. Due to the enormous number of possible encodings, we have applied an evolutionary algorithm to evolve the sensory receptive field representation from random to optimized encoding. Following the nature-inspired analogy, evolved configurations have shown to outperform simple hand-tuned configurations and other homogenized configurations based on the solution provided by the optimization engine (evolutionary algorithm). We have used artificial evolutionary engines as the optimization tool to circumvent nonlinearity responses in receptive fields.     

Benefits of hierarchical generalization and storage of the representations of "object-place" associations in the hippocampal subfields (a hypothesis)

We analyzed the results of experimental research of features of processing sensory information in the hippocampus and neocortex available in literature and results of modelling the perception of information in the neocortex. It is noted that "place" fields of neurons become wider, and overlapping of receptive fields increases during upward moving in trisynaptic hippocampal pathway. These effects specify the generalization of the information processed. The results of our analysis allow us to put forward a hypothesis that a hierarchical complication of"object - place" associations occurs during upward propagation of signals through all hippocampal subfields. Complexity of neural representations of "object - place" associations that are formed and permanently stored in the hippocampal areas increases in process of propagation of signals from the entorhinal cortex to the hierarchically higher dentate gyrus, area CA3 and area CA1. Therefore, with the aim to extract information about "object - place" associations with certain details it is necessary to access that hippocampal area in which associations were processed and stored with the required degree of elaboration. By analogy with the neocortex, it is proposed that such processing of information in the hippocampus makes it possible to avoid the combinatorial explosion and provides storing (memory) the associations accumulated during the life. The proposed mechanism can serve as an addition to the known multiple trace theory, which states that the hippocampus is an integrating part of memory trace and is always involved in recall of long-delayed episodes.     

Visual Ecology and Perception of Coloration Patterns by Domestic Chicks

This article suggests how we might understand the way potential predators see coloration patterns used in aposematism and visual mimicry. We start by briefly reviewing work on evolutionary function of eyes and neural mechanisms of vision. Often mechanisms used for achromatic vision are accurately modeled as adaptations for detection and recognition of the generality of optical stimuli, rather than specific stimuli such as biological signals. Colour vision is less well understood, but for photoreceptor spectral sensitivities of birds and hymenopterans there is no evidence for adaptations to species-specific stimuli, such as those of food or mates. Turning to experimental work, we investigate how achromatic and chromatic stimuli are used for object recognition by foraging domestic chicks (Gallus gallus). Chicks use chromatic and achromatic signals in different ways: discrimination of large targets uses (chromatic) colour differences, and chicks remember chromatic signals accurately. However, detection of small targets, and discrimination of visual textures requires achromatic contrast. The different roles of chromatic and achromatic information probably reflect their utility for object recognition in nature. Achromatic (intensity) variation exceeds chromatic variation, and hence is more informative about change in reflectance – for example, object borders, while chromatic signals yield more information about surface reflectance (object colour) under variable illumination. This revised version was published online in July 2006 with corrections to the Cover Date.     

The Measurement of Local Variation in Shape

Geometric morphometrics comprises tools for measuring and analyzing shape as captured by an entire set of landmark configurations. Many interesting questions in evolutionary, genetic, and developmental research, however, are only meaningful at a local level, where a focus on ??parts?? or ??traits?? takes priority over properties of wholes. To study variational properties of such traits, current approaches partition configurations into subsets of landmarks which are then studied separately. This approach is unable to fully capture both variational and spatial characteristics of these subsets because interpretability of shape differences is context-dependent. Landmarks omitted from a partition usually contain information about that partition??s shape. We present an interpolation-based approach that can be used to model shape differences at a local, infinitesimal level as a function of information available globally. This approach belongs in a large family of methods that see shape differences as continuous ??fields?? spanning an entire structure, for which landmarks serve as reference parameters rather than as data. We show, via analyses of simulated and real data, how interpolation models provide a more accurate representation of regional shapes than partitioned data. A key difference of this interpolation approach from current morphometric practice is that one must assume an explicit interpolation model, which in turn implies a particular kind of behavior of the regions between landmarks. This choice presents novel methodological challenges, but also an opportunity to incorporate and test biomechanical models that have sought to explain tissue-level processes underlying the generation of morphological shape.     

Size does not matter: size-invariant echo-acoustic object classification

Echolocating bats can not only extract spatial information from the auditory analysis of their ultrasonic emissions, they can also discriminate, classify and identify the three-dimensional shape of objects reflecting their emissions. Effective object recognition requires the segregation of size and shape information. Previous studies have shown that, like in visual object recognition, bats can transfer an echo-acoustic object discrimination task to objects of different size and that they spontaneously classify scaled versions of virtual echo-acoustic objects according to trained virtual-object standards. The current study aims to bridge the gap between these previous findings using a different class of real objects and a classification—instead of a discrimination paradigm. Echolocating bats (Phyllostomus discolor) were trained to classify an object as either a sphere or an hour-glass shaped object. The bats spontaneously generalised this classification to objects of the same shape. The generalisation cannot be explained based on similarities of the power spectra or temporal structures of the echo-acoustic object images and thus require dedicated neural mechanisms dealing with size-invariant echo-acoustic object analysis. Control experiments with human listeners classifying the echo-acoustic images of the objects confirm the universal validity of auditory size invariance. The current data thus corroborate and extend previous psychophysical evidence for sonar auditory-object normalisation and suggest that the underlying auditory mechanisms following the initial neural extraction of the echo-acoustic images in echolocating bats may be very similar in bats and humans.     

Robustness and complexity co-constructed in multimodal signalling networks

In animal communication, signals are frequently emitted using different channels (e.g. frequencies in a vocalization) and different modalities (e.g. gestures can accompany vocalizations). We explore two explanations that have been provided for multimodality: (i) selection for high information transfer through dedicated channels and (ii) increasing fault tolerance or robustness through multichannel signals. Robustness relates to an accurate decoding of a signal when parts of a signal are occluded. We show analytically in simple feed-forward neural networks that while a multichannel signal can solve the robustness problem, a multimodal signal does so more effectively because it can maximize the contribution made by each channel while minimizing the effects of exclusion. Multimodality refers to sets of channels where within each set information is highly correlated. We show that the robustness property ensures correlations among channels producing complex, associative networks as a by-product. We refer to this as the principle of robust overdesign. We discuss the biological implications of this for the evolution of combinatorial signalling systems; in particular, how robustness promotes enough redundancy to allow for a subsequent specialization of redundant components into novel signals.     

Neural induction: old problem, new findings, yet more questions

During neural induction, the embryonic neural plate is specified and set aside from other parts of the ectoderm. A popular molecular explanation is the 'default model' of neural induction, which proposes that ectodermal cells give rise to neural plate if they receive no signals at all, while BMP activity directs them to become epidermis. However, neural induction now appears to be more complex than once thought, and can no longer be fully explained by the default model alone. This review summarizes neural induction events in different species and highlights some unanswered questions about this important developmental process.     

Implicit multisensory associations influence voice recognition

Natural objects provide partially redundant information to the brain through different sensory modalities. For example, voices and faces both give information about the speech content, age, and gender of a person. Thanks to this redundancy, multimodal recognition is fast, robust, and automatic. In unimodal perception, however, only part of the information about an object is available. Here, we addressed whether, even under conditions of unimodal sensory input, crossmodal neural circuits that have been shaped by previous associative learning become activated and underpin a performance benefit. We measured brain activity with functional magnetic resonance imaging before, while, and after participants learned to associate either sensory redundant stimuli, i.e. voices and faces, or arbitrary multimodal combinations, i.e. voices and written names, ring tones, and cell phones or brand names of these cell phones. After learning, participants were better at recognizing unimodal auditory voices that had been paired with faces than those paired with written names, and association of voices with faces resulted in an increased functional coupling between voice and face areas. No such effects were observed for ring tones that had been paired with cell phones or names. These findings demonstrate that brief exposure to ecologically valid and sensory redundant stimulus pairs, such as voices and faces, induces specific multisensory associations. Consistent with predictive coding theories, associative representations become thereafter available for unimodal perception and facilitate object recognition. These data suggest that for natural objects effective predictive signals can be generated across sensory systems and proceed by optimization of functional connectivity between specialized cortical sensory modules.     

Functional role of the ventrolateral prefrontal cortex in decision making

To make deliberate decisions, we have to utilize detailed information about the environment and our internal states. The ventral visual pathway provides detailed information on object identity, including color and shape, to the ventrolateral prefrontal cortex (VLPFC). The VLPFC also receives motivational and emotional information from the orbitofrontal cortex and subcortical areas, and computes the behavioral significance of external events; this information can be used for elaborate decision making or design of goal-directed behavior. In this review, we discuss recent advances that are revealing the neural mechanisms that underlie the coding of behavioral significance in the VLPFC, and the functional roles of these mechanisms in decision making and action programming in the brain.     

Parts, wholes, and context in reading: a triple dissociation

Research in object recognition has tried to distinguish holistic recognition from recognition by parts. One can also guess an object from its context. Words are objects, and how we recognize them is the core question of reading research. Do fast readers rely most on letter-by-letter decoding (i.e., recognition by parts), whole word shape, or sentence context? We manipulated the text to selectively knock out each source of information while sparing the others. Surprisingly, the effects of the knockouts on reading rate reveal a triple dissociation. Each reading process always contributes the same number of words per minute, regardless of whether the other processes are operating.     

Integration of bimodal looming signals through neuronal coherence in the temporal lobe

The ability to integrate information across multiple sensory systems offers several behavioral advantages, from quicker reaction times and more accurate responses to better detection and more robust learning. At the neural level, multisensory integration requires large-scale interactions between different brain regions--the convergence of information from separate sensory modalities, represented by distinct neuronal populations. The interactions between these neuronal populations must be fast and flexible, so that behaviorally relevant signals belonging to the same object or event can be immediately integrated and integration of unrelated signals can be prevented. Looming signals are a particular class of signals that are behaviorally relevant for animals and that occur in both the auditory and visual domain. These signals indicate the rapid approach of objects and provide highly salient warning cues about impending impact. We show here that multisensory integration of auditory and visual looming signals may be mediated by functional interactions between auditory cortex and the superior temporal sulcus, two areas involved in integrating behaviorally relevant auditory-visual signals. Audiovisual looming signals elicited increased gamma-band coherence between these areas, relative to unimodal or receding-motion signals. This suggests that the neocortex uses fast, flexible intercortical interactions to mediate multisensory integration.     

Spatial modulation of primate inferotemporal responses by eye position

Background

A key aspect of representations for object recognition and scene analysis in the ventral visual stream is the spatial frame of reference, be it a viewer-centered, object-centered, or scene-based coordinate system. Coordinate transforms from retinocentric space to other reference frames involve combining neural visual responses with extraretinal postural information.

Methodology/Principal Findings

We examined whether such spatial information is available to anterior inferotemporal (AIT) neurons in the macaque monkey by measuring the effect of eye position on responses to a set of simple 2D shapes. We report, for the first time, a significant eye position effect in over 40% of recorded neurons with small gaze angle shifts from central fixation. Although eye position modulates responses, it does not change shape selectivity.

Conclusions/Significance

These data demonstrate that spatial information is available in AIT for the representation of objects and scenes within a non-retinocentric frame of reference. More generally, the availability of spatial information in AIT calls into questions the classic dichotomy in visual processing that associates object shape processing with ventral structures such as AIT but places spatial processing in a separate anatomical stream projecting to dorsal structures.     

Transmission delays and frequency detuning can regulate information flow between brain regions

Brain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure. Brain oscillations are hypothesized to underlie time-dependent functional connectivity by periodically changing the excitability of neural populations. In this paper, we investigate the role of the connection delay and the detuning between the natural frequencies of neural populations in the transmission of signals. Based on numerical simulations and analytical arguments, we show that the amount of information transfer between two oscillating neural populations could be determined by their connection delay and the mismatch in their oscillation frequencies. Our results highlight the role of the collective phase response curve of the oscillating neural populations for the efficacy of signal transmission and the quality of the information transfer in brain networks.     

Structural characterization of assemblies from overall shape and subcomplex compositions

We suggest structure characterization of macromolecular assemblies by combining assembly shape determined by electron cyromicroscopy with information about subunit proximity determined by affinity purification. To achieve this aim, structure characterization is expressed as a problem in satisfaction of spatial restraints that (1) represents subunits as spheres, (2) encodes information about the subunit excluded volume, assembly shape, and pulldowns in a scoring function, and (3) finds subunit configurations that satisfy the input restraints by an optimization of the scoring function. Testing of the approach with model systems suggests its feasibility.