Correlation dimension of high-dimensional and high-definition experimental time series.

The correlation dimension (CD) is a nonlinear measure of the complexity of invariant sets. First introduced for describing low-dimensional chaotic attractors, it has been later extended to the analysis of experimental electroencephalographic (EEG), magnetoencephalographic (MEG), and local field potential (LFP) recordings. However, its direct application to high-dimensional (dozens of signals) and high-definition (kHz sampling rate) 2HD data revealed a controversy in the results.

Site-dependent shaping of field potential waveforms.

The activity of neuron populations gives rise to field potentials (FPs) that extend beyond the sources. Their mixing in the volume dilutes the original temporal motifs in a site-dependent manner, a fact that has received little attention. And yet, it potentially rids of physiological significance the time-frequency parameters of individual waves (amplitude, phase, duration).

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices.

The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition.

Spatial Memory in a Spiking Neural Network with Robot Embodiment.

Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity.

Static internal representation of dynamic situations reveals time compaction in human cognition.

Introduction: The human brain has evolved under the constraint of survival in complex dynamic situations. It makes fast and reliable decisions based on internal representations of the environment. Whereas neural mechanisms involved in the internal representation of space are becoming known, entire spatiotemporal cognition remains a challenge.

Universal principles justify the existence of concept cells.

The widespread consensus argues that the emergence of abstract concepts in the human brain, such as a "table", requires complex, perfectly orchestrated interaction of myriads of neurons. However, this is not what converging experimental evidence suggests. Single neurons, the so-called concept cells (CCs), may be responsible for complex tasks performed by humans.

Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot.

Development of spiking neural networks (SNNs) controlling mobile robots is one of the modern challenges in computational neuroscience and artificial intelligence. Such networks, being replicas of biological ones, are expected to have a higher computational potential than traditional artificial neural networks (ANNs). The critical problem is in the design of robust learning algorithms aimed at building a "living computer" based on SNNs.

Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations.

Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood.

Local and Volume-Conducted Contributions to Cortical Field Potentials.

Brain field potentials (FPs) can reach far from their sources, making difficult to know which waves come from where. We show that modern algorithms efficiently segregate the local and remote contributions to cortical FPs by recovering the generator-specific spatial voltage profiles. We investigated experimentally and numerically the local and remote origin of FPs in different cortical areas in anesthetized rats.

The unreasonable effectiveness of small neural ensembles in high-dimensional brain.

Complexity is an indisputable, well-known, and broadly accepted feature of the brain. Despite the apparently obvious and widely-spread consensus on the brain complexity, sprouts of the single neuron revolution emerged in neuroscience in the 1970s. They brought many unexpected discoveries, including grandmother or concept cells and sparse coding of information in the brain.

Fast social-like learning of complex behaviors based on motor motifs.

Social learning is widely observed in many species. Less experienced agents copy successful behaviors exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown.

Latent Factors Limiting the Performance of sEMG-Interfaces.

Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human-machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here, we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game.

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons.

Codifying memories is one of the fundamental problems of modern Neuroscience. The functional mechanisms behind this phenomenon remain largely unknown. Experimental evidence suggests that some of the memory functions are performed by stratified brain structures such as the hippocampus.

The right hippocampus leads the bilateral integration of gamma-parsed lateralized information.

It is unclear whether the two hippocampal lobes convey similar or different activities and how they cooperate. Spatial discrimination of electric fields in anesthetized rats allowed us to compare the pathway-specific field potentials corresponding to the gamma-paced CA3 output (CA1 Schaffer potentials) and CA3 somatic inhibition within and between sides. Bilateral excitatory Schaffer gamma waves are generally larger and lead from the right hemisphere with only moderate covariation of amplitude, and drive CA1 pyramidal units more strongly than unilateral waves.

Diversity of LFPs Activated in Different Target Regions by a Common CA3 Input.

Identifying the pathways contributing to local field potential (LFP) events and oscillations is essential to determine whether synchronous interregional patterns indicate functional connectivity. Here, we studied experimentally and numerically how different target structures receiving input from a common population shape their LFPs. We focused on the bilateral CA3 that sends gamma-paced excitatory packages to the bilateral CA1, the lateral septum, and itself (recurrent input).

Prediction-for-CompAction: navigation in social environments using generalized cognitive maps.

The ultimate navigation efficiency of mobile robots in human environments will depend on how we will appraise them: merely as impersonal machines or as human-like agents. In the latter case, an agent may take advantage of the cooperative collision avoidance, given that it possesses recursive cognition, i.e.

Can pathway-specific LFPs be obtained in cytoarchitectonically complex structures?

Deciphering how the brain encodes the continuous flow of information contained in natural stimuli requires understanding the spontaneous activity of functional assemblies in multiple neuronal populations. A promising integrative approach that combines multisite recordings of local field potentials (LFP) with an independent component analysis (ICA) enables continuous readouts of population specific activities of functionally different neuron groups to be obtained. We previously used this technique successfully in the hippocampus, a single-layer neuronal structure.

Neural network architecture for cognitive navigation in dynamic environments.

Navigation in time-evolving environments with moving targets and obstacles requires cognitive abilities widely demonstrated by even simplest animals. However, it is a long-standing challenging problem for artificial agents. Cognitive autonomous robots coping with this problem must solve two essential tasks: 1) understand the environment in terms of what may happen and how I can deal with this and 2) learn successful experiences for their further use in an automatic subconscious way.

Theory of intermittency applied to classical pathological cases.

The classical theory of intermittency developed for return maps assumes uniform density of points reinjected from the chaotic to laminar region. Though it works fine in some model systems, there exist a number of so-called pathological cases characterized by a significant deviation of main characteristics from the values predicted on the basis of the uniform distribution. Recently, we reported on how the reinjection probability density (RPD) can be generalized.

Determining the true polarity and amplitude of synaptic currents underlying gamma oscillations of local field potentials.

Fluctuations in successive waves of oscillatory local field potentials (LFPs) reflect the ongoing processing of neuron populations. However, their amplitude, polarity and synaptic origin are uncertain due to the blending of electric fields produced by multiple converging inputs, and the lack of a baseline in standard AC-coupled recordings. Consequently, the estimation of underlying currents by laminar analysis yields spurious sequences of inward and outward currents.

Cytoarchitectonic and dynamic origins of giant positive local field potentials in the dentate gyrus.

To determine why some pathways but not others produce sizable local field potentials (LFPs) and how far from the source can these be recorded, complementary experimental analyses and realistic modeling of specific brain structures are required. In the present study, we combined multiple in vivo linear recordings in rats and a tridimensional finite element model of the dentate gyrus, a curved structure displaying abnormally large positive LFPs. We demonstrate that the polarized dendritic arbour of granule cells (GCs), combined with the curved layered configuration of the population promote the spatial clustering of GC currents in the interposed hilus and project them through the open side at a distance from cell domains.

Wave-processing of long-scale information by neuronal chains.

Investigation of mechanisms of information handling in neural assemblies involved in computational and cognitive tasks is a challenging problem. Synergetic cooperation of neurons in time domain, through synchronization of firing of multiple spatially distant neurons, has been widely spread as the main paradigm. Complementary, the brain may also employ information coding and processing in spatial dimension.

Sustained increase of spontaneous input and spike transfer in the CA3-CA1 pathway following long-term potentiation in vivo.

Long-term potentiation (LTP) is commonly used to study synaptic plasticity but the associated changes in the spontaneous activity of individual neurons or the computational properties of neural networks in vivo remain largely unclear. The multisynaptic origin of spontaneous spikes makes it difficult to estimate the impact of a particular potentiated input. Accordingly, we adopted an approach that isolates pathway-specific postsynaptic activity from raw local field potentials (LFPs) in the rat hippocampus in order to study the effects of LTP on ongoing spike transfer between cell pairs in the CA3-CA1 pathway.