Yves Frégnac

Prediction of Spatiotemporal Patterns of Neural Activity from Pairwise Correlations

We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatiotemporal patterns significantly better than Ising models only based on spatial correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogates that reproduce the spatial and temporal correlations of a given data set.

The Visual Brain: Computing Through Multiscale Complexity

Information coding in sensory neurons is both digital, in terms of neuronal output spike timing and rate, and analog, produced by the irregular subthreshold changes in somatic and dendritic membrane potential resulting from synchronized volleys of synaptic inputs. Intracellular recordings give a unique access to a composite multiscale signal where the local microscopic integration process realized by a single neuron can be studied in the global mesoscopic context of the “unseen” units afferent to the recorded cell. This chapter shows how reverse engineering approaches can be used in the primary visual cortex of higher mammals to reveal the hidden complexity of visual processing and establish causal links between the functional dynamics of synaptic echoes in primary visual cortex and perceptual biases in low-level, non-attentive perception.

Theoretical Approaches and Cellular Analogs of Functional Plasticity in the Developing and Adult Vertebrate Visual Cortex

Visual cortical neurons acquire their functional identity through a number of developmental events, particularly those occurring postnatally, when the animal starts to explore its outside environment. Once the integrative properties of neurons are expressed, do they process incoming signals in the same way throughout life, or can they be considered as adaptive devices capable of modifying their functional properties? This chapter will discuss the importance of activity dependent processes involved in functional plasticity, and the determination of the learning capacities of cells in the primary visual cortex of developing and adult mammals.

Dynamic Clamp with High-Resistance Electrodes Using Active Electrode Compensation In Vitro and In Vivo

The active electrode compensation (AEC) consists of an online correction of the recorded membrane potential based on a computational model of the electrode. This technique may be particularly useful for situations where high-frequency components (such as noise) must be injected. This is particularly important for dynamic-clamp applications because of the real-time feedback between injected current and recorded voltage, since any artifact is amplified and may cause instabilities. We show here that such problems are greatly limited by the AEC, and this technique enables dynamic-clamp injection at high feedback frequencies (>10 kHz) and in demanding conditions. We illustrate AEC with applications such as injection of conductance noise in vivo and in vitro.