The Necessity for a Time Local Dimension in Systems with Time Varying Attractors

We show that a simple non-linear system of ordinary differential equations may possess a time varying attractor dimension. This indicates that it is infeasible to characterize EEG and MEG time series with a single time global dimension. We suggest another measure for the description of non-stationary attractors.Comment: 13 Postscript pages, 12 Postscript figures (figures 3b and 4 by request from Y. Ashkenazy: [email protected]

Coverage, Continuity and Visual Cortical Architecture

The primary visual cortex of many mammals contains a continuous representation of visual space, with a roughly repetitive aperiodic map of orientation preferences superimposed. It was recently found that orientation preference maps (OPMs) obey statistical laws which are apparently invariant among species widely separated in eutherian evolution. Here, we examine whether one of the most prominent models for the optimization of cortical maps, the elastic net (EN) model, can reproduce this common design. The EN model generates representations which optimally trade of stimulus space coverage and map continuity. While this model has been used in numerous studies, no analytical results about the precise layout of the predicted OPMs have been obtained so far. We present a mathematical approach to analytically calculate the cortical representations predicted by the EN model for the joint mapping of stimulus position and orientation. We find that in all previously studied regimes, predicted OPM layouts are perfectly periodic. An unbiased search through the EN parameter space identifies a novel regime of aperiodic OPMs with pinwheel densities lower than found in experiments. In an extreme limit, aperiodic OPMs quantitatively resembling experimental observations emerge. Stabilization of these layouts results from strong nonlocal interactions rather than from a coverage-continuity-compromise. Our results demonstrate that optimization models for stimulus representations dominated by nonlocal suppressive interactions are in principle capable of correctly predicting the common OPM design. They question that visual cortical feature representations can be explained by a coverage-continuity-compromise.Comment: 100 pages, including an Appendix, 21 + 7 figure

The clinical global impression scale and the influence of patient or staff perspective on outcome

<p>Abstract</p> <p>Background</p> <p>Since its first publication, the Clinical Global Impression Scale (CGI) has become one of the most widely used assessment instruments in psychiatry. Although some conflicting data has been presented, studies investigating the CGI's validity have only rarely been conducted so far. It is unclear whether the improvement index CGI-I or a difference score of the severity index CGI-S_difis more valid in depicting clinical change. The current study examined the validity of these two measures and investigated whether therapists' CGI ratings correspond to the view the patients themselves have on their condition.</p> <p>Methods</p> <p>Thirty-one inpatients of a German psychotherapeutic hospital suffering from a major depressive disorder (age M = 45.3, SD = 17.2; 58.1% women) participated. Patients filled in the Beck Depression Inventory (BDI). CGI-S and CGI-I were rated from three perspectives: the treating therapist (THER), the team of therapists involved in the patient's treatment (TEAM), and the patient (PAT). BDI and CGI-S were filled in at admission and discharge, CGI-I at discharge only. Data was analysed using effect sizes, Spearman's <it>ρ </it>and intra-class correlations (ICC).</p> <p>Results</p> <p>Effect sizes between CGI-I and CGI-S _difratings were large for all three perspectives with substantially higher change scores on CGI-I than on CGI-S _dif. BDI_difcorrelated moderately with PAT ratings, but did not correlate significantly with TEAM or THER ratings. Congruence between CGI-ratings from the three perspectives was low for CGI-S _dif(ICC = .37; Confidence Interval [CI] .15 to .59; <it>F</it>_30,60= 2.77, <it>p </it>< .001; mean <it>ρ </it>= 0.36) and moderate for CGI-I (ICC = .65 (CI .47 to .80; <it>F</it>_30,60= 6.61, <it>p </it>< .001; mean <it>ρ </it>= 0.59).</p> <p>Conclusions</p> <p>Results do not suggest a definite recommendation for whether CGI-I or CGI-S _difshould be used since no strong evidence for the validity of neither of them could be found. As congruence between CGI ratings from patients' and staff's perspective was not convincing it cannot be assumed that CGI THER or TEAM ratings fully represent the view of the patient on the severity of his impairment. Thus, we advocate for the incorporation of multiple self- and clinician-reported scales into the design of clinical trials in addition to CGI in order to gain further insight into CGI's relation to the patients' perspective.</p

Optimality of Human Contour Integration

For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy

Molecular and Electrophysiological Characterization of GFP-Expressing CA1 Interneurons in GAD65-GFP Mice

The use of transgenic mice in which subtypes of neurons are labeled with a fluorescent protein has greatly facilitated modern neuroscience research. GAD65-GFP mice, which have GABAergic interneurons labeled with GFP, are widely used in many research laboratories, although the properties of the labeled cells have not been studied in detail. Here we investigate these cells in the hippocampal area CA1 and show that they constitute ∼20% of interneurons in this area. The majority of them expresses either reelin (70±2%) or vasoactive intestinal peptide (VIP; 15±2%), while expression of parvalbumin and somatostatin is virtually absent. This strongly suggests they originate from the caudal, and not the medial, ganglionic eminence. GFP-labeled interneurons can be subdivided according to the (partially overlapping) expression of neuropeptide Y (42±3%), cholecystokinin (25±3%), calbindin (20±2%) or calretinin (20±2%). Most of these subtypes (with the exception of calretinin-expressing interneurons) target the dendrites of CA1 pyramidal cells. GFP-labeled interneurons mostly show delayed onset of firing around threshold, and regular firing with moderate frequency adaptation at more depolarized potentials

A biophysical model of endocannabinoid-mediated short term depression in hippocampal inhibition

Memories are believed to be represented in the synaptic pathways of vastly interconnected networks of neurons. The plasticity of synapses, that is, their strengthening and weakening depending on neuronal activity, is believed to be the basis of learning and establishing memories. An increasing number of studies indicate that endocannabinoids have a widespread action on brain function through modulation of synap–tic transmission and plasticity. Recent experimental studies have characterised the role of endocannabinoids in mediating both short- and long-term synaptic plasticity in various brain regions including the hippocampus, a brain region strongly associated with cognitive functions, such as learning and memory. Here, we present a biophysically plausible model of cannabinoid retrograde signalling at the synaptic level and investigate how this signalling mediates depolarisation induced suppression of inhibition (DSI), a prominent form of shortterm synaptic depression in inhibitory transmission in hippocampus. The model successfully captures many of the key characteristics of DSI in the hippocampus, as observed experimentally, with a minimal yet sufficient mathematical description of the major signalling molecules and cascades involved. More specifically, this model serves as a framework to test hypotheses on the factors determining the variability of DSI and investigate under which conditions it can be evoked. The model reveals the frequency and duration bands in which the post-synaptic cell can be sufficiently stimulated to elicit DSI. Moreover, the model provides key insights on how the state of the inhibitory cell modulates DSI according to its firing rate and relative timing to the post-synaptic activation. Thus, it provides concrete suggestions to further investigate experimentally how DSI modulates and is modulated by neuronal activity in the brain. Importantly, this model serves as a stepping stone for future deciphering of the role of endocannabinoids in synaptic transmission as a feedback mechanism both at synaptic and network level