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09:00
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Isabel Labouriau
Centro de Matemática da Universidade do Porto
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The geometry of fast and slow dynamics in nerve impulse
We will discuss the role of different time-scales in the dynamics of
models for excitable tissue, like nerve impulse, and the associated geometry.
These are important both in analysing existing models and in the construction
of new ones.
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09:45
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Mafalda Sousa
Programa Doutoral em Neurociências FMUP
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Models for nociceptive information processing
The processing of nociceptive information, which
involves both spinal and supraspinal mechanisms,
allows nociceptive signals to be modulated as they are transmitted towards
the brain. Understanding how the nociceptive system works requires knowledge
of the neuronal mechanisms distributed along the endogenous pain control
system. Here we present a quantitative approach to the study of this system.
By combining electrophysiological measurements with computational and
biophysical models, we are able to describe neuronal membrane dynamics and
identify important functional mechanisms. The proposed models rely on the
Hodgkin-Huxley formalism to provide biophysically realistic models for the
target areas.
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10:30
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coffee break
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10:45
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Wolfram Erlhagen
Centro de Matemática (CMAT) UMinho
Donders
Institute for Brain, Cognition and Behaviour, Radboud University, The Netherlands
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A dynamic field model of ordinal and timing properties of sequential
events
Recent evidence suggests that the neural
mechanisms underlying working memory for serial order and interval timing of
sequential events are closely linked. I present a dynamic neural field model
which exploits self-stabilized multi-bump solutions with a gradient of
activation to store serial order. Mathematically, the existence and stability
of multi-bumps can be guaranteed by using a coupling function with
oscillatory rather than monotonic decay. The activation gradient is
achieved by applying a state-dependent threshold accommodation process to the
firing rate function. A field dynamics of lateral inhibition type is then
used in combination with a dynamics for the baseline activity to recall the
temporal sequence from memory.
At the end
of my talk I will briefly discuss how we implement and test the model as part of a distributed dynamic field architecture for natural
human-robot interaction that we have developed over the last couple of years.
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11:30
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Eduardo Sousa
Programa Doutoral em Matemática Aplicada UP
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A working memory model capable of storing pattern sequences without
synaptic plasticity
Working memory provides a system where information can be retained for
a short-period of time. This temporary buffer may be used to support specific
cognitive functions, or provide a first step in the mechanisms for long-term
storage of relevant information. Most models for WM rely on short-term
synaptic plasticity and/or specific network level properties. Here we present
an alternative functional model for WM which does not require synaptic
plasticity. Instead, information is stored in the states of the conductance
dynamics of neurons. The interaction between specific ionic currents allows
the neuronal excitability to be modulated by previous inputs. The
self-sustained neuronal activity is used to store information. This model
also differs from other WM models in the sense that it is able to temporarily
store a sequence of multiple patterns. This is achieved assuming a network
connectivity architecture which is capable of storing the order between
patterns.
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12:15
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lunch break
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14:00
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Alfonso Renart
Champalimaud Neuroscience Programme
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The temporal structure of population activity in cortical circuits
Recent theoretical and
experimental results have generated a renewed interest in understanding the
temporal structure of population activity in cortical circuits, and the type
of circuit dynamics and architectures that could be responsible for
generating the observed activity patterns. Unlike previously thought,
cortical circuits can generate activity patterns characterized by negligible
global pair-wise correlations and, interestingly, this is the typical
behavior of balanced, randomly connected networks of excitatory and
inhibitory neurons. The absence of a net global correlation does not imply,
however, absence of correlations between individual pairs. In this
presentation, I will review our recent results on this topic, and discuss
preliminary data on the structure of the correlation matrix measured during
spontaneous activity in rat sensory cortices. I will also discuss our ideas
on the type of network mechanisms that could generate correlation structures
similar to the ones that we observe.
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14:45
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Luísa Castro
Programa Doutoral em Matemática Aplicada UP
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Phase precession
through acceleration of local theta rhythm: a biophysical model for the
interaction between place cells and local inhibitory neurons
We present a biophysical
spiking model for phase precession in hippocampal CA1 which focuses on the
interaction between place cells and local inhibitory interneurons. The
model's functional block is composed of a place cell (PC) connected with a
local inhibitory cell (IC), both receiving excitatory inputs from the
entorhinal cortex (EC). The dynamics of the two neuron types are described by
integrate-and-fire models with conductance synapses, and the EC inputs are
described using non-homogeneous Poisson processes. Phase precession in our
model is caused by increased drive to specific PC/IC pairs when the animal is
in their place field
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15:30
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coffee break
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15:45
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Ricardo Salvador
Instituto de Biofísica e Engª Biomédica FCUL
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Numerical modeling of neural stimulation
techniques
Neural stimulation techniques change cortical excitability by inducing
an electric field that affects neurons. Therefore they are currently being
studied as a possible treatment to a range of psychiatric diseases such as
depression, as well as movement disorders, such as Parkinson. One aspect that
still hampers a more widespread use of these techniques is the lack of
understanding about the mechanisms through which the induced electric field
interacts with neurons. This requires knowledge about
the electric field spatial distribution and time variation which is,
currently, impossible to measure in in vivo studies. In this work I will
describe the use of a powerful numerical technique, the finite element
method, to create models that allow us to predict the electric field induced
in neural stimulation techniques. I will also briefly discuss how this
information can be coupled with accurate cable models of neurons to determine
their response to the applied electric field.
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16:30
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Ana Paula Dias
Centro de Matemática da Universidade do Porto
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Network structure and dynamics of coupled cell systems
In this talk coupled cell systems are systems of ordinary differential
equations associated with a network (graph) whose nodes represent variables
(the cells) and whose edges specify couplings between nodes. It is known that
the structure of the network imposes constraints on the dynamics, with the
result that many new phenomena become `generic' for a given structure. In
this talk we focus at the emergence of general principles characterizing
aspects of the dynamics that are shared by any coupled cell system associated
with a given network.
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