1) Development of neural networks to simulate the integration of
different sensory modalities (visual, auditory, tactile) in brain
regions, in agreement with data from the neurophysiological
literature; 2) Development of new methods for the estimation of
effective brain connectivity among brain regions during cognitive
and/or motor tasks, with the use of mathematical models of
interconnected neural masses; 3) Development of models of neural
oscillators to deal with the problem of different objectds
representation in memory, object recognition from incomplete
information, and link between objects and language. 4) Development
of mathematical models able to describe the integrated action of
different cardiovascular and cerebrovascular regulatory mechanims
in the short period. 5) Application of advanced methods of
signal processing (wavelets, Independent Component
analysis) for the study of EEG signals and oculomotor signals
in the awake state and/or during sleep, in normal and pathological
subjects.
1) It is well known the existence in the brain of neurons which
integrate information from different sensory modalities, such
visual, auditory and tactile stimuli. This integration seems to
play a major role in orienting overt behaviour (for instance
movements) based of information coming from the external world.
However, the mechanisms governing the working of these neurons are
still insufficiently understood. Aim of this research is to develop
physiologically realistic neural networks able to explain the main
results in the neurophysiologic literature and the main
psychophysical tests concerning multisensory integration.
Subsequently, in cooperation with the Center for Cognitive
Neurosciences (University of Bologna, Cesena, Prof. Ladavas) and
with the Wake Forest University School of Medicine (North Carolina,
USA, Prof. Stein) the models will be used to design new
experiments, able to discriminate among different hypotheses.
The final aim is to arrive at a deeper theoretical understanding of
the neural mechanisms which consent the integration of different
sensory information, and their use in the clinical practice to
improve cognitive deficits. 2) It is well known that the fulfilment
of cognitive and motor tasks is the results of the participation of
different brain regions mutually interconnected. Brain connectivity
can be estimated starting from neuroimaging measurement, such as
the functional magnetic resonance, the positron emission
tomography, the electroencephalography and the
magnetoencephalography. The methods usually adopted, however, are
based on empirical models of the data. Aim of the research is to
formulate new methods for the study and the analysis of brain
connectivity, by using physiologically inspired mathematical
models. To this aim, the so-called “neural mass models” can be
particularly useful. In these models, the activity of entire
populations of neurons is described using a few state variables, by
neglecting individual fluctuations. The research (performed in
cooperation with the Department of Human Physiology, University of
Rome, Prof. F. Babiloni) is aimed at understanding the effect of
connectivity among different brain regions on the quantities
measured with non-invasive neuroimaging techniques and on the
generation of EEG rhythms. In perspective, the methods can be
adopted to design new and reliable non-linear methods to estimate
brain connectivity from real data in individual subjects. 3) The
representation of objects in brain memory occurs in a distributed
fashion: an object is generally represented as a collection of
features, each mapped in a different brain region. It is thus of
great value to understand how features of the same object are
linked together to form a unitary object representation (binding
problem) and how features of different objects, simultaneously in
memory, are separated (segmentation), in order to arrive at a
coherent representation of the world. A credited hypothesis
assumes that object representation is achieved by the brain via the
synchronization of neural groups, which code for different
attributes of the object in different brain regions. Aim of the
research is to develop mathematical models, based on the use of
oscillating neural groups, to analyse and solve the binding and
segmentation problem, and to reconstruct objects even starting from
incomplete knowledge, both for what concerns a low-level
representation (such segmentation of a visual scene) and for what
concerns a high-level representation (recognition of abstract
objects and its relation with language and semantics). A
peculiarity of these models is the use of a realistic neural
architecture and the use of physiological learning rules, based on
physiological knowledge. 4) Maintenance of cardiovascular
homeostasis is warranted by the action of sophisticate regulatory
mechanisms, which operate at different levels in response to acute
perturbations (such as hypoxia, hypercapnia, hemorrhage, etc...).
Aim of the research is to develop and improve comprehensive
mathematical models, which describe the main regulatory actions
both at a systemic level (cardiac contractility, heart rate,
circulation in less vital peripheral organs) and at the level of
vital organs (coronary and brain circulation, exercising muscles).
These models, developed and implemented with the cooperation of
Medical Philips North America (Ing. Chbat) will be used as a tool
for improving basic physiological knowledge, as a support for the
clinical practice (for instance through estimation of clinical
parameters, taking individual variability into account) and within
the medical education framework. 5) The analysis of biomedical
signals requires the use of advanced processing techniques, such as
the wavelets and the Independent Component Analysis (ICA). Wavelet
analysis is particularly useful to study non-stationary biomedical
signals, in cases where the most important phenomena are
characterized by transient events. ICA allows the search of the
sources of a signal by excluding or reducing the effect of possible
artefacts. These techniques are applied to two different biomedical
signals: i) the scalp electroencephalogram (EEG) obtained in
healthy and pathological subjects to detect and classify different
patterns of cortical activity; ii) the electro-oculogram obtained
on volunteers and pathological subjects (in cooperation with the
Department of Neurogical Sciences, University of Bologna, Prof.
Montagna) during wake and sleep periods. In particular, wavelets
are used to detect the presence of slow-eye-movements, considered
as a marker of neural structures involved in sleep regulation.
