The impact of spike timing variability on the signal-encoding performance of neural spiking models

It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance

Variability and coding efficiency of noisy neural spike encoders

Encoding synaptic inputs as a train of action potentials is a fundamental function of nerve cells. Although spike trains recorded in vivo have been shown to be highly variable, it is unclear whether variability in spike timing represents faithful encoding of temporally varying synaptic inputs or noise inherent in the spike encoding mechanism. It has been reported that spike timing variability is more pronounced for constant, unvarying inputs than for inputs with rich temporal structure. This could have significant implications for the nature of neural coding, particularly if precise timing of spikes and temporal synchrony between neurons is used to represent information in the nervous system. To study the potential functional role of spike timing variability, we estimate the fraction of spike timing variability which conveys information about the input for two types of noisy spike encoders — an integrate and fire model with randomly chosen thresholds and a model of a patch of neuronal membrane containing stochastic Na+ and K+ channels obeying Hodgkin–Huxley kinetics. The quality of signal encoding is assessed by reconstructing the input stimuli from the output spike trains using optimal linear mean square estimation. A comparison of the estimation performance of noisy neuronal models of spike generation enables us to assess the impact of neuronal noise on the efficacy of neural coding. The results for both models suggest that spike timing variability reduces the ability of spike trains to encode rapid time-varying stimuli. Moreover, contrary to expectations based on earlier studies, we find that the noisy spike encoding models encode slowly varying stimuli more effectively than rapidly varying ones

Connecting the Brain to Itself through an Emulation.

Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids. Arrays of modules can be constructed as early stage whole brain emulators, following canonical intra- and inter-regional circuits. By using machine learning algorithms and classic tasks known to activate quasi-orthogonal functional connectivity patterns, bedside testing can rapidly identify ensemble tuning properties and in turn cycle through a sequence of external module architectures to explore which can causatively alter perception and behavior. Whole brain emulation both (1) serves to augment human neural function, compensating for disease and injury as an auxiliary parallel system, and (2) has its independent operation bootstrapped by a human-in-the-loop to identify optimal micro- and macro-architectures, update synaptic weights, and entrain behaviors. In this manner, closed-loop brain-computer interface pilot clinical trials can advance strong artificial intelligence development and forge new therapies to restore independence in children and adults with neurological conditions

Yeast as a model system to study genetic and post-translational regulation of metabolic pathways in mammals

Dissertation presented to obtain the Ph.D degree in BiologyThe work presented in this thesis describes the use of yeast Saccharomyces cerevisiae as a model system to study two different stress response processes and its extrapolation to higher eukaryotes.(...)Financial Support from Fundação para a Ciência e a Tecnologia (No.SFRH/BD/39389/2007)

Structural dynamics of the selectivity filter in HCN1 ion channel

Les canaux HCN (cycliques nucléotidiques) activés par hyperpolarisation appartiennent à la superfamille des canaux cationiques voltage-dépendants et sont responsables de la génération de courant drôle (If) dans les cellules cardiaques et neuronales. Malgré la similitude structurelle globale avec le potassium voltage-dépendant (Kv) et les canaux ioniques cycliques nucléotidiques (CNG), ils montrent un modèle de sélectivité distinctif pour les ions K+ et Na+. Plus précisément, leur perméabilité accrue aux ions Na+ est essentielle à son rôle dans la dépolarisation des membranes cellulaires. Ils sont également l'une des seules protéines connues à sélectionner entre les ions Na+ et Li+, faisant des HCN des canaux semi-sélectifs. Ici, nous étudions les propriétés de sélectivité uniques des canaux HCN à l'aide de simulations de dynamique moléculaire. Nos simulations suggèrent que le pore HCN1 est très flexible et dilaté par rapport aux canaux Kv et qu'il n'y a qu'un seul site de liaison ionique stable dans le filtre de sélectivité qui les distingue des canaux Kv et CNG. Nous observons également que la coordination et l'hydratation des ions diffèrent dans le filtre de sélectivité de HCN1 par rapport aux canaux Kv et CNG. De plus, la coordination des ions K+ par les groupes carbonyle du filtre de sélectivité est plus stable par rapport aux ions Na+ et Li+, ce qui peut expliquer les propriétés de sélectivité distinctes du canal.Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels belong to the voltage-gated cation channel superfamily and are responsible for the generation of funny current (If) in cardiac and neuronal cells. Despite the overall structural similarity to voltage-gated potassium (Kv) and cyclic nucleotide-gated (CNG) ion channels, they show distinctive selectivity pattern for K+ and Na+ ions. Specifically, their increased permeability to Na+ ions is critical to its role in depolarizing cellular membranes. They are also one of the only known proteins to select between Na+ and Li+ ions, making HCNs semi-selective channels. Here we investigate the unique selectivity properties of HCN channels using molecular dynamics simulations. Our simulations suggest that the HCN1 pore is very flexible and dilatated compared to Kv channels and that there is only one stable ion binding site within the selectivity filter which discriminates them from both Kv and CNG channels. We also observe that ion co-ordination and hydration differ within the selectivity filter of HCN1 compared to Kv and CNG channels. Additionally, the co-ordination of K+ ions by the carbonyl groups of the selectivity filter is more stable compared to Na+ and Li+ ions, which may explain the channel's distinct selectivity properties

Information Theory is abused in neuroscience

In 1948, Claude Shannon introduced his version of a concept that was core to Norbert Wiener's cybernetics, namely, information theory. Shannon's formalisms include a physical framework, namely a general communication system having six unique elements. Under this framework, Shannon information theory offers two particularly useful statistics, channel capacity and information transmitted. Remarkably, hundreds of neuroscience laboratories subsequently reported such numbers. But how (and why) did neuroscientists adapt a communications-engineering framework? Surprisingly, the literature offers no clear answers. To therefore first answer "how", 115 authoritative peer-reviewed papers, proceedings, books and book chapters were scrutinized for neuroscientists' characterizations of the elements of Shannon's general communication system. Evidently, many neuroscientists attempted no identification of the system's elements. Others identified only a few of Shannon's system's elements. Indeed, the available neuroscience interpretations show a stunning incoherence, both within and across studies. The interpretational gamut implies hundreds, perhaps thousands, of different possible neuronal versions of Shannon's general communication system. The obvious lack of a definitive, credible interpretation makes neuroscience calculations of channel capacity and information transmitted meaningless. To now answer why Shannon's system was ever adapted for neuroscience, three common features of the neuroscience literature were examined: ignorance of the role of the observer, the presumption of "decoding" of neuronal voltage-spike trains, and the pursuit of ingrained analogies such as information, computation, and machine. Each of these factors facilitated a plethora of interpretations of Shannon's system elements. Finally, let us not ignore the impact of these "informational misadventures" on society at large. It is the same impact as scientific fraud