Neural activity in the lower pathway of the somatosensory system in the presence of silicon interfaces

In this communication, we present results of experimental work carried out in the frame of the ROSANA project aiming at the investigation of the interactions between sensory inputs and the activity of Central Nervous System (CNS) neurons in the creation of the internal representations of real-world stimuli. We implanted sieve microelectrodes in the peripheral nerve of rats and we obtained functional regeneration of the sensory nerves. We recorded the electrical activity of the regenerated nerve fibers and also of the relay neurons of the first station of the somatosensory pathway. Finally we developed mathematical models of the oscillatory neurons involved in the information processing that fit well with our experimental data

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. Here, we extend this methodology and apply it to different dynamical systems exhibiting anomalous type-II and type-III intermittencies. Estimation of the universal RPD is based on fitting a linear function to experimental data and requires no a priori knowledge on the dynamical model behind. We provide special fitting procedure that enables robust estimation of the RPD from relatively short data sets (dozens of points). Thus, the method is applicable for a wide variety of data sets including numerical simulations and real-life experiments. Estimated RPD enables analytic evaluation of the length of the laminar phase of intermittent behaviors. We show that the method copes well with dynamical systems exhibiting significantly different statistics reported in the literature. We also derive and classify characteristic relations between the mean laminar length and main controlling parameter in perfect agreement with data provided by numerical simulation

Synchronization of Heteroclinic Circuits Through Learning in Chains of Neural Motifs

The synchronization of oscillatory activity in networks of neural networks is usually implemented through coupling the state variables describing neuronal dynamics. In this study we discuss another but complementary mechanism based on a learning process with memory. A driver network motif, acting as a teacher, exhibits winner-less competition (WLC) dynamics, while a driven motif, a learner, tunes its internal couplings according to the oscillations observed in the teacher. We show that under appropriate training the learner motif can dynamically copy the coupling pattern of the teacher and thus synchronize oscillations with the teacher. Then, we demonstrate that the replication of the WLC dynamics occurs for intermediate memory lengths only. In a unidirectional chain of N motifs coupled through teacher-learner paradigm the time interval required for pattern replication grows linearly with the chain size, hence the learning process does not blow up and at the end we observe phase synchronized oscillations along the chain. We also show that in a learning chain closed into a ring the network motifs come to a consensus, i.e. to a state with the same connectivity pattern corresponding to the mean initial pattern averaged over all network motifs

Spatial Chaos in a Chain of Coupled Bistable Oscillators

The spatiotemporal behavior of a chain of diffusively coupled bistable oscillators is investigated. It is stated that there is spatial disorder and its evolutionary character is demonstrated

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. CA3 waves lock to the ipsilateral Schaffer potentials, although bilateral coherence was weak. Notably, Schaffer activity may run laterally, as seen after the disruption of the connecting pathways. Thus, asymmetric operations promote the entrainment of CA3-autonomous gamma oscillators bilaterally, synchronizing lateralized gamma strings to converge optimally on CA1 targets. The findings support the view that interhippocampal connections integrate different aspects of information that flow through the left and right lobes

Parallel Readout of Pathway-Specific Inputs to Laminated Brain Structures

Local field potentials (LFPs) capture the electrical activity produced by principal cells during integration of converging synaptic inputs from multiple neuronal populations. However, since synaptic currents mix in the extracellular volume, LFPs have complex spatiotemporal structure, making them hard to exploit. Here we propose a biophysical framework to identify and separate LFP-generators. First we use a computational multineuronal model that scales up single cell electrogenesis driven by several synaptic inputs to realistic aggregate LFPs. This approach relies on the fixed but distinct locations of synaptic inputs from different presynaptic populations targeting a laminated brain structure. Thus the LFPs are contributed by several pathway-specific LFP-generators, whose electrical activity is defined by the spatial distribution of synaptic terminals and the time course of synaptic currents initiated in target cells by the corresponding presynaptic population. Then we explore the efficacy of independent component analysis to blindly separate converging sources and reconstruct pathway-specific LFP-generators. This approach can optimally locate synaptic inputs with subcellular accuracy while the reconstructed time course of pathway-specific LFP-generators is reliable in the millisecond scale. We also describe few cases where the non-linear intracellular interaction of strongly overlapping LFP-generators may lead to a significant cross-contamination and the appearance of derivative generators. We show that the approach reliably disentangle ongoing LFPs in the hippocampus into contribution of several LFP-generators. We were able to readout in parallel the pathway-specific presynaptic activity of projection cells in the entorhinal cortex and pyramidal cells in the ipsilateral and contralateral CA3. Thus we provide formal mathematical and experimental support for parallel readout of the activity of converging presynaptic populations in working neuronal circuits from common LFPs

Stability of Neural Firing in the Trigeminal Nuclei under Mechanical Whisker Stimulation

Sensory information handling is an essentially nonstationary process even under a periodic stimulation. We show how the time evolution of ridges in the wavelet spectrum of spike trains can be used for quantification of the dynamical stability of the neuronal responses to a stimulus. We employ this method to study neuronal responses in trigeminal nuclei of the rat provoked by tactile whisker stimulation. Neurons from principalis (Pr5) and interpolaris (Sp5i) show the maximal stability at the intermediate (50 ms) stimulus duration, whereas Sp5o cells “prefer” shorter (10 ms) stimulation. We also show that neurons in all three nuclei can perform as stimulus frequency filters. The response stability of about 33% of cells exhibits low-pass frequency dynamics. About 57% of cells have band-pass dynamics with the optimal frequency at 5 Hz for Pr5 and Sp5i, and 4 Hz for Sp5o, and the remaining 10% show no prominent dependence on the stimulus frequency. This suggests that the neural coding scheme in trigeminal nuclei is not fixed, but instead it adapts to the stimulus characteristics