Organization, Maturation, and Plasticity of Multisensory Integration: Insights from Computational Modeling Studies

In this paper, we present two neural network models – devoted to two specific and widely investigated aspects of multisensory integration – in order to evidence the potentialities of computational models to gain insight into the neural mechanisms underlying organization, development, and plasticity of multisensory integration in the brain. The first model considers visual–auditory interaction in a midbrain structure named superior colliculus (SC). The model is able to reproduce and explain the main physiological features of multisensory integration in SC neurons and to describe how SC integrative capability – not present at birth – develops gradually during postnatal life depending on sensory experience with cross-modal stimuli. The second model tackles the problem of how tactile stimuli on a body part and visual (or auditory) stimuli close to the same body part are integrated in multimodal parietal neurons to form the perception of peripersonal (i.e., near) space. The model investigates how the extension of peripersonal space – where multimodal integration occurs – may be modified by experience such as use of a tool to interact with the far space. The utility of the modeling approach relies on several aspects: (i) The two models, although devoted to different problems and simulating different brain regions, share some common mechanisms (lateral inhibition and excitation, non-linear neuron characteristics, recurrent connections, competition, Hebbian rules of potentiation and depression) that may govern more generally the fusion of senses in the brain, and the learning and plasticity of multisensory integration. (ii) The models may help interpretation of behavioral and psychophysical responses in terms of neural activity and synaptic connections. (iii) The models can make testable predictions that can help guiding future experiments in order to validate, reject, or modify the main assumptions

Review: Do the Different Sensory Areas within the Cat Anterior Ectosylvian Sulcal Cortex Collectively Represent a Network Multisensory Hub?

Current theory supports that the numerous functional areas of the cerebral cortex are organized and function as a network. Using connectional databases and computational approaches, the cerebral network has been demonstrated to exhibit a hierarchical structure composed of areas, clusters and, ultimately, hubs. Hubs are highly connected, higher-order regions that also facilitate communication between different sensory modalities. One region computationally identified network hub is the visual area of the Anterior Ectosylvian Sulcal cortex (AESc) of the cat. The Anterior Ectosylvian Visual area (AEV) is but one component of the AESc that also includes the auditory (Field of the Anterior Ectosylvian Sulcus - FAES) and somatosensory (Fourth somatosensory representation - SIV). To better understand the nature of cortical network hubs, the present report reviews the biological features of the AESc. Within the AESc, each area has extensive external cortical connections as well as among one another. Each of these core representations is separated by a transition zone characterized by bimodal neurons that share sensory properties of both adjoining core areas. Finally, core and transition zones are underlain by a continuous sheet of layer 5 neurons that project to common output structures. Altogether, these shared properties suggest that the collective AESc region represents a multiple sensory/multisensory cortical network hub. Ultimately, such an interconnected, composite structure adds complexity and biological detail to the understanding of cortical network hubs and their function in cortical processing

An Emergent Model of Multisensory Integration in Superior Colliculus Neurons

Neurons in the cat superior colliculus (SC) integrate information from different senses to enhance their responses to cross-modal stimuli. These multisensory SC neurons receive multiple converging unisensory inputs from many sources; those received from association cortex are critical for the manifestation of multisensory integration. The mechanisms underlying this characteristic property of SC neurons are not completely understood, but can be clarified with the use of mathematical models and computer simulations. Thus the objective of the current effort was to present a plausible model that can explain the main physiological features of multisensory integration based on the current neurological literature regarding the influences received by SC from cortical and subcortical sources. The model assumes the presence of competitive mechanisms between inputs, nonlinearities in NMDA receptor responses, and provides a priori synaptic weights to mimic the normal responses of SC neurons. As a result, it provides a basis for understanding the dependence of multisensory enhancement on an intact association cortex, and simulates the changes in the SC response that occur during NMDA receptor blockade. Finally, it makes testable predictions about why significant response differences are obtained in multisensory SC neurons when they are confronted with pairs of cross-modal and within-modal stimuli. By postulating plausible biological mechanisms to complement those that are already known, the model provides a basis for understanding how SC neurons are capable of engaging in this remarkable process

Exploring the Structural and Functional Organization of the Dorsal Zone of Auditory Cortex in Hearing and Deafness

Recent neuroscientific research has focused on cortical plasticity, which refers to the ability of the cerebral cortex to adapt as a consequence of experience. Over the past decade, an increasing number of studies have convincingly shown that the brain can adapt to the loss or impairment of a sensory system, resulting in the expansion or heightened ability of the remaining senses. A particular region in cat auditory cortex, the dorsal zone (DZ), has been shown to mediate enhanced visual motion detection in deaf animals. The purpose of this thesis is to further our understanding of the structure and function of DZ in both hearing and deaf animals, in order to better understand how the brain compensates following insult or injury to a sensory system, with the ultimate goal of improving the utility of sensory prostheses. First, I demonstrate that the brain connectivity profile of animals with early- and late-onset deafness is similar to that of hearing animals, but the projection strength to visual brain regions involved in motion processing increases as a consequence of deafness. Second, I specifically evaluate the functional impact of the strongest auditory connections to area DZ using reversible deactivation and electrophysiological recordings. I show that projections that ultimately originate in primary auditory cortex (A1) form much of the basis of the response of DZ neurons to auditory stimulation. Third, I show that almost half of the neurons in DZ are influenced by visual or somatosensory information. I further demonstrate that this modulation by other sensory systems can have effects that are opposite in direction during different portions of the auditory response. I also show that techniques that incorporate the responses of multiple neurons, such as multi-unit and local field potential recordings, may vastly overestimate the degree to which multisensory processing occurs in a given brain region. Finally, I confirm that individual neurons in DZ become responsive mainly to visual stimulation following deafness. Together, these results shed light on the function and structural organization of area DZ in both hearing and deaf animals, and will contribute to the development of a comprehensive model of cross-modal plasticity

Multisensory bayesian inference depends on synapse maturation during training: Theoretical analysis and neural modeling implementation

Recent theoretical and experimental studies suggest that in multisensory conditions, the brain performs a near-optimal Bayesian estimate of external events, giving more weight to the more reliable stimuli. However, the neural mechanisms responsible for this behavior, and its progressive maturation in a multisensory environment, are still insufficiently understood. The aim of this letter is to analyze this problem with a neural network model of audiovisual integration, based on probabilistic population coding-the idea that a population of neurons can encode probability functions to perform Bayesian inference. The model consists of two chains of unisensory neurons (auditory and visual) topologically organized. They receive the corresponding input through a plastic receptive field and reciprocally exchange plastic cross-modal synapses, which encode the spatial co-occurrence of visual-auditory inputs. A third chain of multisensory neurons performs a simple sum of auditory and visual excitations. Thework includes a theoretical part and a computer simulation study. We show how a simple rule for synapse learning (consisting of Hebbian reinforcement and a decay term) can be used during training to shrink the receptive fields and encode the unisensory likelihood functions. Hence, after training, each unisensory area realizes a maximum likelihood estimate of stimulus position (auditory or visual). In crossmodal conditions, the same learning rule can encode information on prior probability into the cross-modal synapses. Computer simulations confirm the theoretical results and show that the proposed network can realize a maximum likelihood estimate of auditory (or visual) positions in unimodal conditions and a Bayesian estimate, with moderate deviations from optimality, in cross-modal conditions. Furthermore, the model explains the ventriloquism illusion and, looking at the activity in the multimodal neurons, explains the automatic reweighting of auditory and visual inputs on a trial-by-trial basis, according to the reliability of the individual cues

Uncovering Multisensory Processing through Non-Invasive Brain Stimulation

Most of current knowledge about the mechanisms of multisensory integration of environmental stimuli by the human brain derives from neuroimaging experiments. However, neuroimaging studies do not always provide conclusive evidence about the causal role of a given area for multisensory interactions, since these techniques can mainly derive correlations between brain activations and behavior. Conversely, techniques of non-invasive brain stimulation (NIBS) represent a unique and powerful approach to inform models of causal relations between specific brain regions and individual cognitive and perceptual functions. Although NIBS has been widely used in cognitive neuroscience, its use in the study of multisensory processing in the human brain appears a quite novel field of research. In this paper, we review and discuss recent studies that have used two techniques of NIBS, namely transcranial magnetic stimulation and transcranial direct current stimulation, for investigating the causal involvement of unisensory and heteromodal cortical areas in multisensory processing, the effects of multisensory cues on cortical excitability in unisensory areas, and the putative functional connections among different cortical areas subserving multisensory interactions. The emerging view is that NIBS is an essential tool available to neuroscientists seeking for causal relationships between a given area or network and multisensory processes. With its already large and fast increasing usage, future work using NIBS in isolation, as well as in conjunction with different neuroimaging techniques, could substantially improve our understanding of multisensory processing in the human brain

Mathematical models of cognitive processes

The research activity carried out during the PhD course was focused on the development of mathematical models of some cognitive processes and their validation by means of data present in literature, with a double aim: i) to achieve a better interpretation and explanation of the great amount of data obtained on these processes from different methodologies (electrophysiological recordings on animals, neuropsychological, psychophysical and neuroimaging studies in humans), ii) to exploit model predictions and results to guide future research and experiments. In particular, the research activity has been focused on two different projects: 1) the first one concerns the development of neural oscillators networks, in order to investigate the mechanisms of synchronization of the neural oscillatory activity during cognitive processes, such as object recognition, memory, language, attention; 2) the second one concerns the mathematical modelling of multisensory integration processes (e.g. visual-acoustic), which occur in several cortical and subcortical regions (in particular in a subcortical structure named Superior Colliculus (SC)), and which are fundamental for orienting motor and attentive responses to external world stimuli. This activity has been realized in collaboration with the Center for Studies and Researches in Cognitive Neuroscience of the University of Bologna (in Cesena) and the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA). PART 1. Objects representation in a number of cognitive functions, like perception and recognition, foresees distribute processes in different cortical areas. One of the main neurophysiological question concerns how the correlation between these disparate areas is realized, in order to succeed in grouping together the characteristics of the same object (binding problem) and in maintaining segregated the properties belonging to different objects simultaneously present (segmentation problem). Different theories have been proposed to address these questions (Barlow, 1972). One of the most influential theory is the so called “assembly coding”, postulated by Singer (2003), according to which 1) an object is well described by a few fundamental properties, processing in different and distributed cortical areas; 2) the recognition of the object would be realized by means of the simultaneously activation of the cortical areas representing its different features; 3) groups of properties belonging to different objects would be kept separated in the time domain. In Chapter 1.1 and in Chapter 1.2 we present two neural network models for object recognition, based on the “assembly coding” hypothesis. These models are networks of Wilson-Cowan oscillators which exploit: i) two high-level “Gestalt Rules” (the similarity and previous knowledge rules), to realize the functional link between elements of different cortical areas representing properties of the same object (binding problem); 2) the synchronization of the neural oscillatory activity in the γ-band (30-100Hz), to segregate in time the representations of different objects simultaneously present (segmentation problem). These models are able to recognize and reconstruct multiple simultaneous external objects, even in difficult case (some wrong or lacking features, shared features, superimposed noise). In Chapter 1.3 the previous models are extended to realize a semantic memory, in which sensory-motor representations of objects are linked with words. To this aim, the network, previously developed, devoted to the representation of objects as a collection of sensory-motor features, is reciprocally linked with a second network devoted to the representation of words (lexical network) Synapses linking the two networks are trained via a time-dependent Hebbian rule, during a training period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from linguistic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with some shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits). PART 2. The ability of the brain to integrate information from different sensory channels is fundamental to perception of the external world (Stein et al, 1993). It is well documented that a number of extraprimary areas have neurons capable of such a task; one of the best known of these is the superior colliculus (SC). This midbrain structure receives auditory, visual and somatosensory inputs from different subcortical and cortical areas, and is involved in the control of orientation to external events (Wallace et al, 1993). SC neurons respond to each of these sensory inputs separately, but is also capable of integrating them (Stein et al, 1993) so that the response to the combined multisensory stimuli is greater than that to the individual component stimuli (enhancement). This enhancement is proportionately greater if the modality-specific paired stimuli are weaker (the principle of inverse effectiveness). Several studies have shown that the capability of SC neurons to engage in multisensory integration requires inputs from cortex; primarily the anterior ectosylvian sulcus (AES), but also the rostral lateral suprasylvian sulcus (rLS). If these cortical inputs are deactivated the response of SC neurons to cross-modal stimulation is no different from that evoked by the most effective of its individual component stimuli (Jiang et al 2001). This phenomenon can be better understood through mathematical models. The use of mathematical models and neural networks can place the mass of data that has been accumulated about this phenomenon and its underlying circuitry into a coherent theoretical structure. In Chapter 2.1 a simple neural network model of this structure is presented; this model is able to reproduce a large number of SC behaviours like multisensory enhancement, multisensory and unisensory depression, inverse effectiveness. In Chapter 2.2 this model was improved by incorporating more neurophysiological knowledge about the neural circuitry underlying SC multisensory integration, in order to suggest possible physiological mechanisms through which it is effected. This endeavour was realized in collaboration with Professor B.E. Stein and Doctor B. Rowland during the 6 months-period spent at the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA), within the Marco Polo Project. The model includes four distinct unisensory areas that are devoted to a topological representation of external stimuli. Two of them represent subregions of the AES (i.e., FAES, an auditory area, and AEV, a visual area) and send descending inputs to the ipsilateral SC; the other two represent subcortical areas (one auditory and one visual) projecting ascending inputs to the same SC. Different competitive mechanisms, realized by means of population of interneurons, are used in the model to reproduce the different behaviour of SC neurons in conditions of cortical activation and deactivation. The model, with a single set of parameters, is able to mimic the behaviour of SC multisensory neurons in response to very different stimulus conditions (multisensory enhancement, inverse effectiveness, within- and cross-modal suppression of spatially disparate stimuli), with cortex functional and cortex deactivated, and with a particular type of membrane receptors (NMDA receptors) active or inhibited. All these results agree with the data reported in Jiang et al. (2001) and in Binns and Salt (1996). The model suggests that non-linearities in neural responses and synaptic (excitatory and inhibitory) connections can explain the fundamental aspects of multisensory integration, and provides a biologically plausible hypothesis about the underlying circuitry

Perceptual Strategies and Neuronal Underpinnings underlying Pattern Recognition through Visual and Tactile Sensory Modalities in Rats

The aim of my PhD project was to investigate multisensory perception and multimodal recognition abilities in the rat, to better understand the underlying perceptual strategies and neuronal mechanisms. I have chosen to carry out this project on the laboratory rat, for two reasons. First, the rat is a flexible and highly accessible experimental model, where it is possible to combine state-of-the-art neurophysiological approaches (such as multi-electrode neuronal recordings) with behavioral investigation of perception and (more in general) cognition. Second, extensive research concerning multimodal integration has already been conducted in this species, both at the neurophysiological and behavioral level. My thesis work has been organized in two projects: a psychophysical assessment of object categorization abilities in rats, and a neurophysiological study of neuronal tuning in the primary visual cortex of anaesthetized rats. In both experiments, unisensory (visual and tactile) and multisensory (visuo-tactile) stimulation has been used for training and testing, depending on the task. The first project has required development of a new experimental rig for the study of object categorization in rat, using solid objects, so as to be able to assess their recognition abilities under different modalities: vision, touch and both together. The second project involved an electrophysiological study of rat primary visual cortex, during visual, tactile and visuo-tactile stimulation, with the aim of understanding whether any interaction between these modalities exists, in an area that is mainly deputed to one of them. The results of both of the studies are still preliminary, but they already offer some interesting insights on the defining features of these abilities

fMR-adaptation indicates selectivity to audiovisual content congruency in distributed clusters in human superior temporal cortex

<p>Abstract</p> <p>Background</p> <p>Efficient multisensory integration is of vital importance for adequate interaction with the environment. In addition to basic binding cues like temporal and spatial coherence, meaningful multisensory information is also bound together by content-based associations. Many functional Magnetic Resonance Imaging (fMRI) studies propose the (posterior) superior temporal cortex (STC) as the key structure for integrating meaningful multisensory information. However, a still unanswered question is how superior temporal cortex encodes content-based associations, especially in light of inconsistent results from studies comparing brain activation to semantically matching (congruent) versus nonmatching (incongruent) multisensory inputs. Here, we used fMR-adaptation (fMR-A) in order to circumvent potential problems with standard fMRI approaches, including spatial averaging and amplitude saturation confounds. We presented repetitions of audiovisual stimuli (letter-speech sound pairs) and manipulated the associative relation between the auditory and visual inputs (congruent/incongruent pairs). We predicted that if multisensory neuronal populations exist in STC and encode audiovisual content relatedness, adaptation should be affected by the manipulated audiovisual relation.</p> <p>Results</p> <p>The results revealed an occipital-temporal network that adapted independently of the audiovisual relation. Interestingly, several smaller clusters distributed over superior temporal cortex within that network, adapted stronger to congruent than to incongruent audiovisual repetitions, indicating sensitivity to content congruency.</p> <p>Conclusions</p> <p>These results suggest that the revealed clusters contain multisensory neuronal populations that encode content relatedness by selectively responding to congruent audiovisual inputs, since unisensory neuronal populations are assumed to be insensitive to the audiovisual relation. These findings extend our previously revealed mechanism for the integration of letters and speech sounds and demonstrate that fMR-A is sensitive to multisensory congruency effects that may not be revealed in BOLD amplitude per se.</p