Energy and information in Hodgkin-Huxley neurons.

The generation of spikes by neurons is energetically a costly process and the evaluation of the metabolic energy required to maintain the signaling activity of neurons a challenge of practical interest. Neuron models are frequently used to represent the dynamics of real neurons but hardly ever to evaluate the electrochemical energy required to maintain that dynamics. This paper discusses the interpretation of a Hodgkin-Huxley circuit as an energy model for real biological neurons and uses it to evaluate the consumption of metabolic energy in the transmission of information between neurons coupled by electrical synapses, i.e., gap junctions. We show that for a single postsynaptic neuron maximum energy efficiency, measured in bits of mutual information per molecule of adenosine triphosphate (ATP) consumed, requires maximum energy consumption. For groups of parallel postsynaptic neurons we determine values of the synaptic conductance at which the energy efficiency of the transmission presents clear maxima at relatively very low values of metabolic energy consumption. Contrary to what could be expected, the best performance occurs at a low energy cost.

Energy aspects of the synchronization of model neurons.

We have deduced an energy function for a Hindmarsh-Rose model neuron and we have used it to evaluate the energy consumption of the neuron during its signaling activity. We investigate the balance of energy in the synchronization of two bidirectional linearly coupled neurons at different values of the coupling strength. We show that when two neurons are coupled there is a specific cost associated to the cooperative behavior. We find that the energy consumption of the neurons is incoherent until very near the threshold of identical synchronization, which suggests that cooperative behaviors without complete synchrony could be energetically more advantageous than those with complete synchrony.

Minimization of the energy flow in the synchronization of nonidentical chaotic systems.

We argue that maintaining a synchronized regime between different chaotic systems requires a net flow of energy between the guided system and an external energy source. This energy flow can be spontaneously reduced if the systems are flexible enough as to structurally approach each other through an adequate adaptive change in their parameter values. We infer that this reduction of energy can play a role in the synchronization of bursting neurons and other natural oscillators.

Energy balance in feedback synchronization of chaotic systems.

In this paper we present a method based on a generalized Hamiltonian formalism to associate to a chaotic system of known dynamics a function of the phase space variables with the characteristics of an energy. Using this formalism we have found energy functions for the Lorenz, Rössler, and Chua families of chaotic oscillators. We have theoretically analyzed the flow of energy in the process of synchronizing two chaotic systems via feedback coupling and used the previously found energy functions for computing the required energy to maintain a synchronized regime between systems of these families. We have calculated the flows of energy at different coupling strengths covering cases of both identical as well as nonidentical synchronization. The energy dissipated by the guided system seems to be sensitive to the transitions in the stability of its equilibrium points induced by the coupling.

Parameter-adaptive identical synchronization disclosing Lorenz chaotic masking.

A parameter-adaptive rule that globally synchronizes oscillatory Lorenz chaotic systems with initially different parameter values is reported. In principle, the adaptive rule requires access to the three state variables of the drive system but it has been readapted to work with the exclusive knowledge of only one variable, a potential message carrier. The rule is very robust and can be used to trace parameter modulation conveying hidden messages. The driven system is defined according to a drive-driven type of coupling that guarantees synchronization if parameters are identical. From any arbitrary initial state, the parameters of the driven system are dynamically adapted to reach convergence to the drive parameter values. At this point, synchronization mismatch or parameter tracing is used to unmask any potential hidden message.

Structure of the high-order Boltzmann machine from independence maps.

In this paper we consider the determination of the structure of the high-order Boltzmann machine (HOBM), a stochastic recurrent network for approximating probability distributions. We obtain the structure of the HOBM, the hypergraph of connections, from conditional independences of the probability distribution to model. We assume that an expert provides these conditional independences and from them we build independence maps, Markov and Bayesian networks, which represent conditional independences through undirected graphs and directed acyclic graphs respectively. From these independence maps we construct the HOBM hypergraph. The central aim of this paper is to obtain a minimal hypergraph. Given that different orderings of the variables provide in general different Bayesian networks, we define their intersection hypergraph. We prove that the intersection hypergraph of all the Bayesian networks (N!) of the distribution is contained by the hypergraph of the Markov network, it is more simple, and we give a procedure to determine a subset of the Bayesian networks that verifies this property. We also prove that the Markov network graph establishes a minimum connectivity for the hypergraphs from Bayesian networks.

An analysis of the GLVQ algorithm.

Generalized learning vector quantization (GLVQ) has been proposed in as a generalization of the simple competitive learning (SCL) algorithm. The main argument of GLVQ proposal is its superior insensitivity to the initial values of the weights (code vectors). In this paper we show that the distinctive characteristics of the definition of GLVQ disappear outside a small domain of applications. GLVQ becomes identical to SCL when either the number of code vectors grows or the size of the input space is large. Besides that, the behavior of GLVQ is inconsistent for problems defined on very small scale input spaces. The adaptation rules fluctuate between performing descent and ascent searches on the gradient of the distortion function.