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Sharp wave-ripples (SWRs) are important for memory consolidation. Their signature in the hippocampal extracellular field potential can be decomposed into a ≈100 ms long sharp wave superimposed by ≈200 Hz ripple oscillations. How ripple oscillations are generated is currently not well understood. A promising model for the genesis of ripple oscillations is based on recurrent interneuronal networks (INT-INT). According to this hypothesis, the INT-INT network in CA1 receives a burst of excitation from CA3 that generates the sharp wave, and recurrent inhibition leads to an ultrafast synchronization of the CA1 network causing the ripple oscillations; fast-spiking parvalbumin-positive basket cells (PV+ BCs) may constitute the ripple-generating interneuronal network. PV+ BCs are also coupled by gap junctions (GJs) but the function of GJs for ripple oscillations has not been quantified. Using simulations of CA1 hippocampal networks of PV+ BCs, we show that GJs promote synchrony beyond a level that could be obtained by only inhibition. GJs also increase the neuronal firing rate of the interneuronal ensemble, while they affect the ripple frequency only mildly. The promoting effect of GJs on ripple oscillations depends on fast GJ transmission ( ≲ 0.5 ms), which requires proximal GJ coupling ( ≲ 100 μm from soma), but is robust to variability in the delay and the amplitude of GJ coupling.

The statistical analysis of neuronal spike trains by models of point processes often relies on the assumption of constant process parameters. However, it is a well-known problem that the parameters of empirical spike trains can be highly variable, such as for example the firing rate. In order to test the null hypothesis of a constant rate and to estimate the change points, a Multiple Filter Test (MFT) and a corresponding algorithm (MFA) have been proposed that can be applied under the assumption of independent inter spike intervals (ISIs). As empirical spike trains often show weak dependencies in the correlation structure of ISIs, we extend the MFT here to point processes associated with short range dependencies. By specifically estimating serial dependencies in the test statistic, we show that the new MFT can be applied to a variety of empirical firing patterns, including positive and negative serial correlations as well as tonic and bursty firing. The new MFT is applied to a data set of empirical spike trains with serial correlations, and simulations show improved performance against methods that assume independence. In case of positive correlations, our new MFT is necessary to reduce the number of false positives, which can be highly enhanced when falsely assuming independence. For the frequent case of negative correlations, the new MFT shows an improved detection probability of change points and thus, also a higher potential of signal extraction from noisy spike trains., The final publication is available at http://link.springer.com

This paper introduces a pre-training technique for learning discriminative features from electroencephalography (EEG) recordings using deep neural networks. EEG data are generally only available in small quantities, they are high-dimensional with a poor signal-to-noise ratio, and there is considerable variability between individual subjects and recording sessions. Similarity-constraint encoders as introduced in this paper specifically address these challenges for feature learning. They learn features that allow to distinguish between classes by demanding that encodings of two trials from the same class are more similar to each other than to encoded trials from other classes. This tuple-based training approach is especially suitable for small datasets. The proposed technique is evaluated using the publicly available OpenMIIR dataset of EEG recordings taken while participants listened to and imagined music. For this dataset, a simple convolutional filter can be learned that significantly improves the signal-to-noise ratio while aggregating the 64 EEG channels into a single waveform.

This article addresses the construction of hierarchies from dynamic attractor networks. We claim that such networks, e.g., dynamic neural fields (DNFs), contain a data model which is encoded in their lateral connections, and which describes typical properties of afferent inputs. This allows to infer the most likely interpretation of inputs, robustly expressed through the position of the attractor state. The principal problem resides in the fact that positions of attractor states alone do not reflect the quality of match between input and data model, termed decision confidence. In hierarchies, this inevitably leads to final decisions which are not Bayes-optimal when inputs exhibit different degrees of ambiguity or conflict, since the resulting differences in confidence will be ignored by downstream layers. We demonstrate a solution to this problem by showing that a correctly parametrized DNF layer can encode decision confidence into the latency of the attractor state in a well-defined way. Conversely, we show that input stimuli gain competitive advantages w.r.t. each other as a function of their relative latency, thus allowing downstream layers to decode attractor latency in an equally well-defined way. Putting these encoding and decoding mechanisms together, we construct a three-stage hierarchy of DNF layers and show that the top-level layer can take Bayes-optimal decisions when the decisions in the lowest hierarchy levels have variable degrees of confidence. In the discussion, we generalize these findings, suggesting a novel possibility to represent and manipulate probabilistic information in recurrent networks without any need for log-encoding, just using the biologically well-founded effect of response latency as an additional coding dimension.

Visual processing already starts at the very first synapse of the visual system. In the mouse retina, three different kinds of photoreceptors (M-cones, S-cones and rods) provide input to 14 parallel bipolar cell types. The precise pattern of connectivity between them determines which signals are available to downstream circuits and therefore the entire visual system. While it has been shown that cone bipolar cell types 1 contact M-cones and type 9 contacts S-cones selectively, most bipolar cell types are thought to contact all cones within their dendritic field. Also, ON bipolar cells are thought to contact cones exclusively via so-called invaginating synapses, while OFF bipolar cells are thought to form basal synapses. By mining publically available electron microscopy data, we confirm that there are no additional M- or S-cone selective bipolar cell types in the mouse retina; however, we found interesting violations of the established rules of outer retinal connectivity: Cone bipolar cell type X contacted only ~20% of the cones in its dendritic field and made mostly atypical non-invaginating contacts with cones. Types 5T, 5O and 8 also contacted fewer cones than expected from the extent of their dendritic field. In addition, we provide anatomical evidence that rod and cone pathways are interconnected in both directions: Not only OFF-types 3A, 3B and 4 get direct input from rods but also rod bipolar cells from cones. Together, this suggests that the organization of the outer plexiform layer is less straightforward than classically thought.

Processing natural scenes requires the visual system to integrate local features into global object descriptions. To achieve coherent representations, the human brain uses statistical dependencies to guide weighting of local feature conjunctions. Pairwise interactions among feature detectors in early visual areas may form the early substrate of these local feature bindings. To investigate local interaction structures in visual cortex, we combined psychophysical experiments with computational modeling and natural scene analysis. We first measured contrast thresholds for 2 × 2 grating patch arrangements (plaids), which differed in spatial frequency composition (low, high, or mixed), number of grating patch co-alignments (0, 1, or 2), and inter-patch distances (1° and 2° of visual angle). Contrast thresholds for the different configurations were compared to the prediction of probability summation (PS) among detector families tuned to the four retinal positions. For 1° distance the thresholds for all configurations were larger than predicted by PS, indicating inhibitory interactions. For 2° distance, thresholds were significantly lower compared to PS when the plaids were homogeneous in spatial frequency and orientation, but not when spatial frequencies were mixed or there was at least one misalignment. Next, we constructed a neural population model with horizontal laminar structure, which reproduced the detection thresholds after adaptation of connection weights. Consistent with prior work, contextual interactions were medium-range inhibition and long-range, orientation-specific excitation. However, inclusion of orientation-specific, inhibitory interactions between populations with different spatial frequency preferences were crucial for explaining detection thresholds. Finally, for all plaid configurations we computed their likelihood of occurrence in natural images. The likelihoods turned out to be inversely related to the detection thresholds obtained at larger inter-patch distances. However, likelihoods were almost independent of inter-patch distance, implying that natural image statistics could not explain the crowding-like results at short distances. This failure of natural image statistics to resolve the patch distance modulation of plaid visibility remains a challenge to the approach.

International audience; Retinal waves are bursts of activity occurring spontaneously in the developing retina of vertebrate species, contributing to the shaping of the visual system organization. They are characterized by localized groups of neurons becoming simultaneously active, initiated at random points. Based on our previous modelling work [1], we now propose a classification of stage II retinal waves patterns as a function of acetylcholine coupling strength and a possible mechanism for waves generation. Our model predicts that spatiotemporal patterns evolve upon maturation or pharmacological manipulation and that there is a regime of cholinergic coupling, only for which, waves are characterized by power-law distributions.

Overrepresentation of bidirectional connections in local cortical networks has been repeatedly reported and is in the focus of the ongoing discussion of non-random connectivity. Here we show in a brief mathematical analysis that in a network in which connection probabilities are symmetric in pairs, $P_{ij} = P_{ji}$, the occurrence of bidirectional connections and non-random structures are inherently linked; an overabundance of reciprocally connected pairs emerges necessarily when the network structure deviates from a random network in any form., Comment: 16 pages, 3 figures