Biomedical subjects

Gonzalo G de Polavieja

Publications and source records attributed to Gonzalo G de Polavieja.

9 recordsLinked to original sources

PubMed2007

Coding with spike shapes and graded potentials in cortical networks.

In cortical neurones, analogue dendritic potentials are thought to be encoded into patterns of digital spikes. According to this view, neuronal codes and computations are based on the temporal patterns of spikes: spike times, bursts or spike rates. Recently, we proposed an 'action potential waveform code' for cortical pyramidal neurones in which the spike shape carries information. Broader somatic action potentials are reliably produced in response to higher conductance input, allowing for four times more information transfer than spike times alone. This information is preserved during synaptic integration in a single neurone, as back-propagating action potentials of diverse shapes differentially shunt incoming postsynaptic potentials and so participate in the next round of spike generation. An open question has been whether the information in action potential waveforms can also survive axonal conduction and directly influence synaptic transmission to neighbouring neurones. Several new findings have now brought new light to this subject, showing cortical information processing that transcends the classical models.

Action Potentials↗

PubMed2006

Age-independent synaptogenesis by phosphoinositide 3 kinase.

Synapses are specialized communication points between neurons, and their number is a major determinant of cognitive abilities. These dynamic structures undergo developmental- and activity-dependent changes. During brain aging and certain diseases, synapses are gradually lost, causing mental decline. It is, thus, critical to identify the molecular mechanisms controlling synapse number. We show here that the levels of phosphoinositide 3 kinase (PI3K) regulate synapse number in both Drosophila larval motor neurons and adult brain projection neurons. The supernumerary synapses induced by PI3K overexpression are functional and elicit changes in behavior. Remarkably, PI3K activation induces synaptogenesis in aged adult neurons as well. We demonstrate that persistent PI3K activity is necessary for synapse maintenance. We also report that PI3K controls the expression and localization of synaptic markers in human neuroblastoma cells, suggesting that PI3K synaptogenic activity is conserved in humans. Thus, we propose that PI3K stimulation can be applied to prevent or delay synapse loss in normal aging and in neurological disorders.

Aging↗

PubMed2006

Feedback network controls photoreceptor output at the layer of first visual synapses in Drosophila.

At the layer of first visual synapses, information from photoreceptors is processed and transmitted towards the brain. In fly compound eye, output from photoreceptors (R1-R6) that share the same visual field is pooled and transmitted via histaminergic synapses to two classes of interneuron, large monopolar cells (LMCs) and amacrine cells (ACs). The interneurons also feed back to photoreceptor terminals via numerous ligand-gated synapses, yet the significance of these connections has remained a mystery. We investigated the role of feedback synapses by comparing intracellular responses of photoreceptors and LMCs in wild-type Drosophila and in synaptic mutants, to light and current pulses and to naturalistic light stimuli. The recordings were further subjected to rigorous statistical and information-theoretical analysis. We show that the feedback synapses form a negative feedback loop that controls the speed and amplitude of photoreceptor responses and hence the quality of the transmitted signals. These results highlight the benefits of feedback synapses for neural information processing, and suggest that similar coding strategies could be used in other nervous systems.

Animals↗

PubMed2006

Neuronal algorithms that detect the temporal order of events.

One of the basic operations in sensory processing is the computation of the temporal order of excitation of sensors. Motivated by the discrepancy between models and experiments at high signal contrast, we obtain families of algorithms by solutions of a general set of equations that define temporal order detection as an input-to-output relationship. Delays and nonlinear operations are the basis of all algorithms found, but different algorithmic structures exist when the operations are multiplications, OR gates, different types of AND-NOT logical gates, or concatenated AND-NOT gates. Among others, we obtain the Hassenstein-Reichardt model, a network using a multiplicative operation that has been proposed to explain fly optomotor behavior. We also find extensions of the Barlow-Levick model (based on an AND-NOT gate with delayed inhibition and nondelayed excitation as inputs), originally proposed to explain the bipolar cell response of the rabbit retina to motion stimuli. In the extended models, there are two more steps, another AND-NOT gate, and a subtraction or two subtractions that make the model responsive only to motion. In response to low-contrast inputs, the concatenated AND-NOT gates or the AND-NOT gate followed by a subtraction in these new models act as the multiplicative operation in the Hassenstein-Reichardt model. At high contrast, the new models behave like the Hassenstein-Reichardt model except that they are independent of contrast as observed experimentally.

Algorithms↗

PubMed2005

Stimulus history reliably shapes action potential waveforms of cortical neurons.

Action potentials have been shown to shunt synaptic charge to a degree that depends on their waveform. In this way, they participate in synaptic integration, and thus in the probability of generating succeeding action potentials, in a shape-dependent way. Here we test whether the different action potential waveforms produced during dynamical stimulation in a single cortical neuron carry information about the conductance stimulus history. When pyramidal neurons in rat visual cortex were driven by a conductance stimulus that resembles natural synaptic input, somatic action potential waveforms showed a large variability that reliably signaled the history of the input for up to 50 ms before the spike. The correlation between stimulus history and action potential waveforms had low noise, resulting in information rates that were three to four times larger than for the instantaneous spike rate. The reliable correlation between stimulus history and spike waveforms then acts as a local encoding at the single-cell level. It also directly affects neuronal communication as different waveforms influence the production of succeeding spikes via differential shunting of synaptic charge. Modeling was used to show that slow conductances can implement memory of the stimulus history in cortical neurons, encoding this information in the spike shape.

Action Potentials↗

PubMed2005

Variability in noise-driven integrator neurons.

Neural variability in the presence of noise has been studied mainly in resonator neurons, such as Hodgkin-Huxley or FitzHugh-Nagumo models. Here we investigate this variability for integrator neurons, whose excitability is due to a saddle-node bifurcation of the rest state instead of a Hopf bifurcation. Using simple theoretical expressions for the interspike times distributions, we obtain coefficients of variation in good agreement with numerical calculations in realistic neuron models. The main features of this coefficient as a function of noise depend on the refractory period and on the presence of bistability. The bistability is responsible for the existence of two different time scales in the spiking behavior giving an antiresonance effect.

Action Potentials↗

PubMed2004

Reliable biological communication with realistic constraints.

Communication in biological systems must deal with noise and metabolic or temporal constraints. We include these constraints into information theory to obtain the distributions of signal usage corresponding to a maximal rate of information transfer given any noise structure and any constraints. Generalized versions of the Boltzmann, Gaussian, or Poisson distributions are obtained for linear, quadratic and temporal constraints, respectively. These distributions are shown to imply that biological transformations must dedicate a larger output range to the more probable inputs and less to the outputs with higher noise and higher participation in the constraint. To show the general theory of reliable communication at work, we apply these results to biochemical and neuronal signaling. Noncooperative enzyme kinetics is shown to be suited for transfer of a high signal quality when the input distribution has a maximum at low concentrations while cooperative kinetics for near-Gaussian input statistics. Neuronal codes based on spike rates, spike times or bursts have to balance signal quality and cost-efficiency and at the network level imply sparseness and uncorrelation within the limits of noise, cost, and processing operations.

Action Potentials↗

PubMed2003

The rate of information transfer of naturalistic stimulation by graded potentials.

We present a method to measure the rate of information transfer for any continuous signals of finite duration without assumptions. After testing the method with simulated responses, we measure the encoding performance of Calliphora photoreceptors. We find that especially for naturalistic stimulation the responses are nonlinear and noise is nonadditive, and show that adaptation mechanisms affect signal and noise differentially depending on the time scale, structure, and speed of the stimulus. Different signaling strategies for short- and long-term and dim and bright light are found for this graded system when stimulated with naturalistic light changes.

Action Potentials↗

PubMed2002

Errors drive the evolution of biological signalling to costly codes.

Reduction of costs in biological signalling seems an evolutionary advantage, but recent experiments have shown signalling codes shifted to signals of high cost with an underutilization of low-cost signals. Here I derive a theory for efficient signalling that includes both errors and costs as constraints and I show that errors in the efficient translation of biological states into signals can shift codes to higher costs, effectively performing a quality control. The statistical structure of signal usage is predicted to be of a generalized Boltzmann form that penalizes signals that are costly and sensitive to errors. This predicted distribution of signal usage against signal cost has two main features: an exponential tail required for cost efficiency and an underutilization of the low-cost signals required to protect the signalling quality from the errors. These predictions are shown to correspond quantitatively to the experiments in which gathering signal statistics is feasible as in visual cortex neurons.

Animal Communication↗