7,414 research outputs found
Modeling neural plasticity in echo state networks for time series prediction
In this paper, we investigate the influence of neural plasticity on the learning performance of echo state networks (ESNs) and supervised learning algorithms in training readout connections for two time series prediction problems including the sunspot time series and the Mackey Glass chaotic system. We implement two different plasticity rules that are expected to improve the prediction performance, namely, anti-Oja learning rule and the Bienenstock-Cooper-Munro (BCM) learning rule combined with both offline and online learning of the readout connections. Our experimental results have demonstrated that the neural plasticity can more significantly enhance the learning in offline learning than in online learning
A VLSI-design of the minimum entropy neuron
One of the most interesting domains of feedforward networks is the processing of sensor signals. There do exist some networks which extract most of the information by implementing the maximum entropy principle for Gaussian sources. This is done by transforming input patterns to the base of eigenvectors of the input autocorrelation matrix with the biggest eigenvalues. The basic building block of these networks is the linear neuron, learning with the Oja learning rule. Nevertheless, some researchers in pattern recognition theory claim that for pattern recognition and classification clustering transformations are needed which reduce the intra-class entropy. This leads to stable, reliable features and is implemented for Gaussian sources by a linear transformation using the eigenvectors with the smallest eigenvalues. In another paper (Brause 1992) it is shown that the basic building block for such a transformation can be implemented by a linear neuron using an Anti-Hebb rule and restricted weights. This paper shows the analog VLSI design for such a building block, using standard modules of multiplication and addition. The most tedious problem in this VLSI-application is the design of an analog vector normalization circuitry. It can be shown that the standard approaches of weight summation will not give the convergence to the eigenvectors for a proper feature transformation. To avoid this problem, our design differs significantly from the standard approaches by computing the real Euclidean norm. Keywords: minimum entropy, principal component analysis, VLSI, neural networks, surface approximation, cluster transformation, weight normalization circuit
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation
The primate brain contains a hierarchy of visual areas, dubbed the ventral
stream, which rapidly computes object representations that are both specific
for object identity and relatively robust against identity-preserving
transformations like depth-rotations. Current computational models of object
recognition, including recent deep learning networks, generate these properties
through a hierarchy of alternating selectivity-increasing filtering and
tolerance-increasing pooling operations, similar to simple-complex cells
operations. While simulations of these models recapitulate the ventral stream's
progression from early view-specific to late view-tolerant representations,
they fail to generate the most salient property of the intermediate
representation for faces found in the brain: mirror-symmetric tuning of the
neural population to head orientation. Here we prove that a class of
hierarchical architectures and a broad set of biologically plausible learning
rules can provide approximate invariance at the top level of the network. While
most of the learning rules do not yield mirror-symmetry in the mid-level
representations, we characterize a specific biologically-plausible Hebb-type
learning rule that is guaranteed to generate mirror-symmetric tuning to faces
tuning at intermediate levels of the architecture
Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules
Generating functionals may guide the evolution of a dynamical system and
constitute a possible route for handling the complexity of neural networks as
relevant for computational intelligence. We propose and explore a new objective
function, which allows to obtain plasticity rules for the afferent synaptic
weights. The adaption rules are Hebbian, self-limiting, and result from the
minimization of the Fisher information with respect to the synaptic flux. We
perform a series of simulations examining the behavior of the new learning
rules in various circumstances. The vector of synaptic weights aligns with the
principal direction of input activities, whenever one is present. A linear
discrimination is performed when there are two or more principal directions;
directions having bimodal firing-rate distributions, being characterized by a
negative excess kurtosis, are preferred. We find robust performance and full
homeostatic adaption of the synaptic weights results as a by-product of the
synaptic flux minimization. This self-limiting behavior allows for stable
online learning for arbitrary durations. The neuron acquires new information
when the statistics of input activities is changed at a certain point of the
simulation, showing however, a distinct resilience to unlearn previously
acquired knowledge. Learning is fast when starting with randomly drawn synaptic
weights and substantially slower when the synaptic weights are already fully
adapted
A Neuron as a Signal Processing Device
A neuron is a basic physiological and computational unit of the brain. While
much is known about the physiological properties of a neuron, its computational
role is poorly understood. Here we propose to view a neuron as a signal
processing device that represents the incoming streaming data matrix as a
sparse vector of synaptic weights scaled by an outgoing sparse activity vector.
Formally, a neuron minimizes a cost function comprising a cumulative squared
representation error and regularization terms. We derive an online algorithm
that minimizes such cost function by alternating between the minimization with
respect to activity and with respect to synaptic weights. The steps of this
algorithm reproduce well-known physiological properties of a neuron, such as
weighted summation and leaky integration of synaptic inputs, as well as an
Oja-like, but parameter-free, synaptic learning rule. Our theoretical framework
makes several predictions, some of which can be verified by the existing data,
others require further experiments. Such framework should allow modeling the
function of neuronal circuits without necessarily measuring all the microscopic
biophysical parameters, as well as facilitate the design of neuromorphic
electronics.Comment: 2013 Asilomar Conference on Signals, Systems and Computers, see
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=681029
Comparison between Oja's and BCM neural networks models in finding useful projections in high-dimensional spaces
This thesis presents the concept of a neural network starting from its corresponding biological model, paying particular attention to the learning algorithms proposed by Oja and Bienenstock Cooper & Munro. A brief introduction to Data Analysis is then performed, with particular reference to the Principal Components Analysis and Singular Value Decomposition.
The two previously introduced algorithms are then dealt with more thoroughly, going to study in particular their connections with data analysis. Finally, it is proposed to use the Singular Value Decomposition as a method for obtaining stationary points in the BCM algorithm, in the case of linearly dependent inputs
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