164,782 research outputs found
Studying the Behaviour of Model of Mirror Neuron System in Case of Autism
Several experiment done by the researchers conducted that autism is caused by the dysfunctional mirror neuron system and the dysfunctions of mirror neuron system is proportional to the symptom severity of autism. In the present work those experiments were studied as well as studying a model of mirror neuron system called MNS2 developed by a research group. This research examined the behavior of the model in case of autism and compared the result with those studies conducting dysfunctions of mirror neuron system in autism. To perform this, a neural network employing the model was developed which recognized the three types of grasping (faster, normal and slower). The network was implemented with back propagation through time learning algorithm. The whole grasping process was divided into 30 time steps and different hand and object states at each time step was used as the input of the network. Normally the network successfully recognized all of the three types of grasps. The network required more times as the number of inactive neurons increased. And in case of maximum inactive neurons of the mirror neuron system the network became unable to recognize the types of grasp. As the time to recognize the types of grasp is proportional to the number of inactive neurons, the experiment result supports the hypothesis that dysfunctions of MNS is proportional to the symptom severity of autism
Learning with Delayed Synaptic Plasticity
The plasticity property of biological neural networks allows them to perform
learning and optimize their behavior by changing their configuration. Inspired
by biology, plasticity can be modeled in artificial neural networks by using
Hebbian learning rules, i.e. rules that update synapses based on the neuron
activations and reinforcement signals. However, the distal reward problem
arises when the reinforcement signals are not available immediately after each
network output to associate the neuron activations that contributed to
receiving the reinforcement signal. In this work, we extend Hebbian plasticity
rules to allow learning in distal reward cases. We propose the use of neuron
activation traces (NATs) to provide additional data storage in each synapse to
keep track of the activation of the neurons. Delayed reinforcement signals are
provided after each episode relative to the networks' performance during the
previous episode. We employ genetic algorithms to evolve delayed synaptic
plasticity (DSP) rules and perform synaptic updates based on NATs and delayed
reinforcement signals. We compare DSP with an analogous hill climbing algorithm
that does not incorporate domain knowledge introduced with the NATs, and show
that the synaptic updates performed by the DSP rules demonstrate more effective
training performance relative to the HC algorithm.Comment: GECCO201
Statistical physics of neural systems
The ability of processing and storing information is considered a characteristic
trait of intelligent systems. In biological neural networks, learning is strongly
believed to take place at the synaptic level, in terms of modulation of synaptic
efficacy. It can be thus interpreted as the expression of a collective phenomena,
emerging when neurons connect each other in constituting a complex network of
interactions. In this work, we represent learning as an optimization problem, actually
implementing a local search, in the synaptic space, of specific configurations, known
as solutions and making a neural network able to accomplish a series of different
tasks. For instance, we would like the network to adapt the strength of its synaptic
connections, in order to be capable of classifying a series of objects, by assigning to
each object its corresponding class-label. Supported by a series of experiments, it
has been suggested that synapses may exploit a very few number of synaptic states
for encoding information. It is known that this feature makes learning in neural
networks a challenging task. Extending the large deviation analysis performed in
the extreme case of binary synaptic couplings, in this work, we prove the existence
of regions of the phase space, where solutions are organized in extremely dense
clusters. This picture turns out to be invariant to the tuning of all the parameters of
the model. Solutions within the clusters are more robust to noise, thus enhancing the
learning performances. This has inspired the design of new learning algorithms, as
well as it has clarified the effectiveness of the previously proposed ones. We further
provide quantitative evidence that the gain achievable when considering a greater
number of available synaptic states for encoding information, is consistent only up
to a very few number of bits. This is in line with the above mentioned experimental
results. Besides the challenging aspect of low precision synaptic connections, it is
also known that the neuronal environment is extremely noisy. Whether stochasticity
can enhance or worsen the learning performances is currently matter of debate. In
this work, we consider a neural network model where the synaptic connections are random variables, sampled according to a parametrized probability distribution.
We prove that, this source of stochasticity naturally drives towards regions of the
phase space at high densities of solutions. These regions are directly accessible by
means of gradient descent strategies, over the parameters of the synaptic couplings
distribution. We further set up a statistical physics analysis, through which we
show that solutions in the dense regions are characterized by robustness and good
generalization performances. Stochastic neural networks are also capable of building
abstract representations of input stimuli and then generating new input samples,
according to the inferred statistics of the input signal. In this regard, we propose a
new learning rule, called Delayed Correlation Matching (DCM), that relying on the
matching between time-delayed activity correlations, makes a neural network able
to store patterns of neuronal activity. When considering hidden neuronal states, the
DCM learning rule is also able to train Restricted Boltzmann Machines as generative
models. In this work, we further require the DCM learning rule to fulfil some
biological constraints, such as locality, sparseness of the neural coding and the Dale’s
principle. While retaining all these biological requirements, the DCM learning
rule has shown to be effective for different network topologies, and in both on-line
learning regimes and presence of correlated patterns. We further show that it is also
able to prevent the creation of spurious attractor states
Intrinsic adaptation in autonomous recurrent neural networks
A massively recurrent neural network responds on one side to input stimuli
and is autonomously active, on the other side, in the absence of sensory
inputs. Stimuli and information processing depends crucially on the qualia of
the autonomous-state dynamics of the ongoing neural activity. This default
neural activity may be dynamically structured in time and space, showing
regular, synchronized, bursting or chaotic activity patterns.
We study the influence of non-synaptic plasticity on the default dynamical
state of recurrent neural networks. The non-synaptic adaption considered acts
on intrinsic neural parameters, such as the threshold and the gain, and is
driven by the optimization of the information entropy. We observe, in the
presence of the intrinsic adaptation processes, three distinct and globally
attracting dynamical regimes, a regular synchronized, an overall chaotic and an
intermittent bursting regime. The intermittent bursting regime is characterized
by intervals of regular flows, which are quite insensitive to external stimuli,
interseeded by chaotic bursts which respond sensitively to input signals. We
discuss these finding in the context of self-organized information processing
and critical brain dynamics.Comment: 24 pages, 8 figure
Hopf Bifurcation and Chaos in Tabu Learning Neuron Models
In this paper, we consider the nonlinear dynamical behaviors of some tabu
leaning neuron models. We first consider a tabu learning single neuron model.
By choosing the memory decay rate as a bifurcation parameter, we prove that
Hopf bifurcation occurs in the neuron. The stability of the bifurcating
periodic solutions and the direction of the Hopf bifurcation are determined by
applying the normal form theory. We give a numerical example to verify the
theoretical analysis. Then, we demonstrate the chaotic behavior in such a
neuron with sinusoidal external input, via computer simulations. Finally, we
study the chaotic behaviors in tabu learning two-neuron models, with linear and
quadratic proximity functions respectively.Comment: 14 pages, 13 figures, Accepted by International Journal of
Bifurcation and Chao
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