nils-herrmann/clean_scirepeval huggingface dataset:
Integrate-and-Fire Neurons Driven by Correlated Stochastic Input

Neurons are sensitive to correlations among synaptic inputs. However, analytical models that explicitly include correlations are hard to solve analytically, so their influence on a neuron's response has been difficult to ascertain. To gain some intuition on this problem, we studied the firing times of two simple integrate-and-fire model neurons driven by a correlated binary variable that represents the total input current. Analytic expressions were obtained for the average firing rate and coefficient of variation (a measure of spike-train variability) as functions of the mean, variance, and correlation time of the stochastic input. The results of computer simulations were in excellent agreement with these expressions. In these models, an increase in correlation time in general produces an increase in both the average firing rate and the variability of the output spike trains. However, the magnitude of the changes depends differentially on the relative values of the input mean and variance: the increase in firing rate is higher when the variance is large relative to the mean, whereas the increase in variability is higher when the variance is relatively small. In addition, the firing rate always tends to a finite limit value as the correlation time increases toward infinity, whereas the coefficient of variation typically diverges. These results suggest that temporal correlations may play a major role in determining the variability as well as the intensity of neuronal spike trains.
Frequency‐selective augmenting responses by short‐term synaptic depression in cat neocortex

Thalamic stimulation at frequencies between 5 and 15 Hz elicits incremental or ‘augmenting’ cortical responses. Augmenting responses can also be evoked in cortical slices and isolated cortical slabs in vivo. Here we show that a realistic network model of cortical pyramidal cells and interneurones including short‐term plasticity of inhibitory and excitatory synapses replicates the main features of augmenting responses as obtained in isolated slabs in vivo. Repetitive stimulation of synaptic inputs at frequencies around 10 Hz produced postsynaptic potentials that grew in size and carried an increasing number of action potentials resulting from the depression of inhibitory synaptic currents. Frequency selectivity was obtained through the relatively weak depression of inhibitory synapses at low frequencies, and strong depression of excitatory synapses together with activation of a calcium‐activated potassium current at high frequencies. This network resonance is a consequence of short‐term synaptic plasticity in a network of neurones without intrinsic resonances. These results suggest that short‐term plasticity of cortical synapses could shape the dynamics of synchronized oscillations in the brain.
Handling Missing Data with Variational Bayesian Learning of ICA

Missing data is common in real-world datasets and is a problem for many estimation techniques. We have developed a variational Bayesian method to perform Independent Component Analysis (ICA) on high-dimensional data containing missing entries. Missing data are handled naturally in the Bayesian framework by integrating the generative density model. Modeling the distributions of the independent sources with mixture of Gaussians allows sources to be estimated with different kurtosis and skewness. The variational Bayesian method automatically determines the dimensionality of the data and yields an accurate density model for the observed data without overfitting problems. This allows direct probability estimation of missing values in the high dimensional space and avoids dimension reduction preprocessing which is not feasible with missing data.
Abstract View RESPONSES OF LOOMING SENSITIVE NEURONS TO CHANGES OF OBJECT CONTRAST

Looming is an apparent increase in the size of an approaching or receding object and can be used to assess changes in the distance between an observer and object. Intracellular recordings of identified neurons in the visual system of Manduca sexta (Sphingidae, Lepidoptera) reveal two cell classes that are sensitive to the retreat or approach of an object. Stimulation with different looming stimuli and illusions (such as a rotating spiral) reveals that these cell types use different visual cues to determine direction and speed of depth motion. The cardinal cues are change of perimeter length for class 1 neurons and expansion or contraction visual flow fields for class 2 neurons (Wicklein and Strausfeld 2000). None of the cells in either class respond when the object and background change their brightness together, thus increasing the overall brightness of the scene but not the contrast between the object and the background. We examined the effects of contrast and found that looming-sensitive cells in both classes are sensitive to changes of object contrast. The amplitude tuning is variable between cells whereas all cells tested respond best to rapid contrast changes. These properties were reproduced in a computational model of these cells.  This work was supported by a grant from NSF.
Analysis of Glaucomatous Visual Field Patterns Found with Unsupervised Learning Using Indpendent Component Analysis and Principal Component Analysis

To uncover known and new patterns representative of glaucoma from standard automated perimetry.  Data came from 156 eyes with and 189 eyes without glaucomatous optic neuropathy, determined by masked stereophoto evaluation. We used 2 methods of unsupervised learning. In method 1, we partitioned the glaucoma and normal populations into clusters with principal component analysis (PCA) and ranked them by variance. We analyzed the pattern closest to the centroid of each cluster as representative of that cluster. In method 2, with independent component analysis (ICA) we found 5 axes with maximal independence in the glaucoma population and 2 axes in the normal population and ranked them by variance. Along each glaucoma axis, we generated the visual field patterns with respect to normal centroid at ± 1 standard deviation from the glaucoma centroid.  We found expected glaucoma patterns such as nasal step defects, arcuate defects, and altitudinal hemifield defects. We also uncovered nonstandard defects which may be important for diagnosing glaucoma.  Decades of experience have led to the reliance by clinicians on typical patterns for diagnosing glaucoma. Other patterns uncovered by unsupervised learning may also be diagnostic for glaucoma.
Dynamic Remapping 3 4

6 The term dynamic remapping has been used in many different 7 ways, but one of the clearest formulations of this concept comes 8 from the mental rotation studies by Georgopoulos et al. (1989) (see 9 also REACHING: CODING IN MOTOR CORTEX). In these experiments 10 monkeys were trained to move a joystick in the direction of a visual 11 stimulus or 90 counterclockwise from it. The brightness of the 12 stimulus indicated which movement was required on a particular 13 trial; a dim light corresponded to a 90 movement and a bright light 14 to a direct movement. An analysis of reaction time suggested that, 15 by default, the initial motor command always pointed straight at 16 the target and then continuously rotated if the cue indicated a 90 17 rotation, an interpretation that was subsequently confirmed by sin18 gle unit recordings. 19 The term remapping is also commonly used whenever a sensory 20 input in one modality is transformed to a sensory representation in 21 another modality. The best-known example in primates is the re22 mapping of auditory space, from head-centered in the early stages 23 of auditory processing to the retinotopic coordinates used in the 24 superior colliculus (Jay and Sparks, 1987). This type of remapping, 25 equivalent to a change of coordinates, is closely related to senso26 rimotor transformations. It does not have to be performed over time 27 but could be accomplished by the neuronal circuitry connecting 28 different representations. 29 This review is divided into three parts. In the first part, we briefly 30 describe the types of cortical representations typically encountered 31 in dynamic remapping. We then summarize the results from several 32 physiological studies where it has been possible to characterize the 33 responses of neurons involved in temporal and spatial remappings. 34 Finally, in the third part, we review modeling efforts to account for 35 these processes.
Morton-Style Factorial Coding of Color in Primary Visual Cortex

We introduce the notion of Morton-style factorial coding and illustrate how it may help understand information integration and perceptual coding in the brain. We show that by focusing on average responses one may miss the existence of factorial coding mechanisms that become only apparent when analyzing spike count histograms. We show evidence suggesting that the classical/non-classical receptive field organization in the cortex effectively enforces the development of Morton-style factorial codes. This may provide some cues to help understand perceptual coding in the brain and to develop new unsupervised learning algorithms. While methods like ICA (Bell & Sejnowski, 1997) develop independent codes, in Morton-style coding the goal is to make two or more external aspects of the world become independent when conditioning on internal representations.
Kinetic models for synaptic interactions

Modeling synaptic interactions in network models poses a particular challenge. Not only should such models capture the important physiological properties of synaptic interactions, but they must do so in a computationally efficient manner to facilitate simulations of large networks. In this paper, we review several types of models which address these goals. Synaptic currents are mediated by ion channels activated by neurotransmitter released from presynaptic terminals. Kinetic models are expressive enough to describe the behavior of ion channels underlying synaptic currents. Although full representation of the molecular details of the synapse generally requires highly complex kinetic models, we focus here on simpler kinetic models that are more computationally efficient. We show that these models can capture the time course and dynamics of several types of synaptic responses, allowing them to be useful tools for modeling synaptic interactions in large networks.
RFID authentication protocol for low-cost tags

Reliability is the reproducibility of a neuron's spike train in response to repeated presentations of the same stimulus. A high reliability characterizes neurons that can support a spike-time code, unlike neurons with discharges forming a renewal process (such as a Poisson process). These two classes of cells cannot be distinguished using a reliability measure based on the firing-rate histogram. In model neurons, state variables representing the neuronal dynamics converge from a set of initial values to an attractor, leading to a reproducible sequence of spike times. Intrinsic noise introduces jitter in the spike times. We define a new measure of reliability, R, based on these attractors and how the jitter increases with the standard deviation d of the noise. When the neuron stays on one attractor, R=1 and the jitter in the nth spike time is proportional to d. When the neuron visits q attractors on different trials, R=1/q and the jitter increases faster than d. For a renewal process, R=0. We determined the reliability and stability of the attractors for an integrate-and-fire neuron driven by constant current I, white noise current with standard deviation d, and external driving force f(t). When f is random or strongly quasiperiodic, most spikes are reliable but there are always unreliable spikes. When f is periodic, either all spikes are reliable, or all are unreliable. This leads to a reliability resonance as a function of driving frequency, the reliability being maximal when the neuron produces one spike per drive cycle. Theoretical results were compared to experimental data from rat neocortical slices. Supported by: Sloan-Swartz Ctr for Theoretical Neurobiology, HHMI
Optimal Smoothing in Visual Motion Perception

When a flash is aligned with a moving object, subjects perceive the flash to lag behind the moving object. Two different models have been proposed to explain this flash-lag effect. In the motion extrapolation model, the visual system extrapolates the location of the moving object to counteract neural propagation delays, whereas in the latency difference model, it is hypothesized that moving objects are processed and perceived more quickly than flashed objects. However, recent psychophysical experiments suggest that neither of these interpretations is feasible (Eagleman & Sejnowski, 2000a, 2000b, 2000c), hypothesizing instead that the visual system uses data from the future of an event before committing to an interpretation. We formalize this idea in terms of the statistical framework of optimal smoothing and show that a model based on smoothing accounts for the shape of psychometric curves from a flash-lag experiment involving random reversals of motion direction. The smoothing model demonstrates how the visual system may enhance perceptual accuracy by relying not only on data from the past but also on data collected from the immediate future of an event.
Abstract View RESPONSES OF LOOMING-SENSITIVE NEURONS IN MANDUCA SEXTA TO OBJECTS DIFFERING IN SIZE, VELOCITY AND CONTRAST

In previous experiments we recorded from looming-sensitive neurons in the hawkmoth Manduca sexta and distinguished two cell classes, which differed in their morphologies and response properties: Class 1 cells were driven by an increase in perimeter length; Class 2 cells were stimulated by expansion/contraction flowfields (Wicklein and Strausfeld 2000). We used anatomical and physiological data to build a network model of these cells with MATLAB and simulated the responses of these neurons with the same stimuli that were used during the physiological characterization of the neurons (Wicklein and Sejnowski 2001). In particular, the model for Class 1 cells predicted how the response should vary with contrast, size and movement velocity of the stimulus; Class 1 cells should be insensitive to the initial size of expanding/contracting objects, but should code for the velocity of the expansion/contraction. In addition the response should increase with increasing contrast between the object and the background. To test these predictions, we measured tuning curves for Class 1 cells to three stimulus parameters contrast, movement velocity and object size. Preliminary results show that class 1 cells are insensitive to the initial size of an expanding/contracting object as predicted by the model and the animal's behavior, but they also seem to be insensitive to different velocities. In addition, the cells respond sensitively to low contrast objects, as expected for crepuscular moths. Literature cited: Wicklein M, Strausfeld NJ (2000) ; Wicklein M, Sejnowski TJ (2001) Neurocomputing, in press Supported by: NSF, HHMI
Parallel Fiber Coding in the Cerebellum for Life-Long Learning

Continuous and real-time learning is a difficult problem in robotics. To learn efficiently, it is important to recognize the current situation and learn appropriately for that context. To be effective, this requires the integration of a large number of sensorimotor and cognitive signals. So far, few principles on how to perform this integration have been proposed. Another limitation is the difficulty to include the complete contextual information to avoid destructive interference while learning different tasks.We suggest that a vertebrate brain structure important for sensorimotor coordination, the cerebellum, may provide answers to these difficult problems. We investigate how learning in the input layer of the cerebellum may successfully encode contextual knowledge in a representation useful for coordination and life-long learning. We propose that a sparsely-distributed and statistically-independent representation provides a valid criterion for the self-organizing classification and integration of context signals. A biologically motivated unsupervised learning algorithm that approximate such a representation is derived from maximum likelihood. This representation is beneficial for learning in the cerebellum by simplifying the credit assignment problem between what must be learned and the relevant signals in the current context for learning it. Due to its statistical independence, this representation is also beneficial for life-long learning by reducing the destructive interference across tasks, while retaining the ability to generalize. The benefits of the learning algorithm are investigated in a spiking model that learns to generate predictive smooth pursuit eye movements to follow target trajectories.
View FAST ODOR LEARNING AND RELIABILITY OF ODOR RESPONSES IN THE LOCUST ANTENNAL LOBE

Intracellular and local field potential recordings in the locust antennal lobe (AL) have revealed stimulus-dependent changes in projection neurons (PNs) and local neurons (LNs) response patterns over repeated odor presentations (Stopfer and Laurent, 1999). The PNs responded with nonsynchronized bursts of spikes when a novel stimulus was presented. After 4-6 presentations, however, the PN response intensity decreased while spike time precision increased, leading to coherent oscillations. Here we tested with a computational model the hypothesis that initially weak inhibitory synapses between LNs and PNs are partially responsible for the absence of oscillatory responses to stimulation with novel odors. In our simulations, activity-driven facilitation of fast GABAergic synapses between LNs and PNs during repetitive stimulus presentations created a temporary network structure tuned specifically for the repeated stimulus, enabling coherent responses in the population of responding PNs. Using stimuli with added noise, we found that at the level of the AL, this fast learning enhanced the reliability of odor identification. A model network endowed with synaptic plasticity responded to a familiar stimulus with stereotypical spatio-temporal patterns from trial to trial despite random input fluctuations. A network without learning, however, responded with patterns that changed markedly between trials, reflecting input variability. Thus, fast odor learning could selectively improve the signal/noise ratio for responses to odorants encountered or sampled repeatedly. Supported by: NIH and NIDCD
ANATOMICAL AND FUNCTIONAL MICRODOMAINS OF CALCIUM AND CALCIUM-DEPENDENT PROTEINS IN DENDRITIC SPINES

Intracellular Ca2+ concentration is central in regulating synaptic efficacy, but it also has many other effects. The focus of this study is on dendritic spines, which act as specialized compartments for calcium signals. To augment imaging methods that lack the spatiotemporal resolution to explore this compartmentalization, we have combined immunocytochemical data with biophysical modeling. High-resolution immunogold studies reveal that both ligand-gated NMDA receptors and L-type voltage-gated calcium channels concentrate at the synapse in neocortex and hippocampus. In addition, several calcium-binding proteins concentrate at the PSD, as may the phosphatases PP1 and PP2B. Monte Carlo simulations of Ca2+ dynamics following different types of neuronal activity result in different Ca2+ distributions within the spine, and consequently to differential activation of calcium-dependent proteins. Both CaMKII and PP2B, implicated in the induction of LTP and LTD, respectively, are activated by calmodulin (CaM). When action potentials and EPSPs are paired to induce
Separation of post-nonlinear mixtures using ACE and temporal decorrelation

ABSTRACTWe propose an efﬁcient method based on the concept ofmaximal correlation that reduces the post-nonlinear blindsource separation problem (PNL BSS) to a linear BSS prob-lem. For this we apply the Alternating Conditional Expec-tation (ACE) algorithm – a powerful technique from non-parametricstatistics –toapproximatelyinvertthe (post-)non-linear functions. Interestingly, in the framework of the ACEmethod convergence can be proven and in the PNL BSSscenario the optimal transformation found by ACE will co-incide with the desired inverse functions. After the non-linearities have been removed by ACE, temporal decorrela-tion (TD) allows us to recover the source signals. An ex-cellent performance underlines the validity of our approachand demonstrates the ACE-TD method on realistic exam-ples.1. INTRODUCTIONBlind source separation (BSS) research has mainly beenfocused on variants of linear ICA and temporal decorrela-tion methods (see e.g. [14, 6, 5, 7, 1, 2, 13, 29, 22, 12]).Linear BSS assumes that at time
THE FLASH LAG ILLUSION IS A CONSEQUENCE OF ADAPTIVE SPATIAL FILTERING

In the flash-lag illusion (FLI), a flash aligned with a moving object appears to be offset. Previously, we presented evidence that information from ~80 ms after an event may be used by the visual system to attribute a position estimate to the time of the event (Eagleman & Sejnowski, Science, 2000a). The attributed position of the moving object is an interpolation of its previous positions, which suggests that the FLI is a spatial illusion. Our framework contrasts with the latency difference hypothesis, a temporal model which assumes that a flash takes a longer time to reach awareness than a continuously moving object. Our framework predicts that when an object has a lower signal-to-noise ratio (SNR), more past positional information is included in the interpolation (Eagleman & Sejnowski, Science, 2000b,c). Here we report 3 new experiments that support this spatial filtering explanation: (1) Subjects viewed a random black and white grid upon which a translating patch was defined by second-order motion. The SNR of the moving patch was determined by the fraction of the patch that changed polarity. With degrading SNR, the FLI is sytematically reduced. This result extends demonstrations that reducing the luminance of the moving object reduces the FLI, but, since luminance is held constant here, this cannot be explained by latency difference. Additionally, we show (2) FLI is greatly modified by predictability of a flash (even while luminance remains constant), and (3) subjects can correctly judge temporal order of moving and flashed stimuli, even under the same experimental conditions that produce the FLI. Taken together, these results are inconsistent with a simple model of perceptual latency difference. Supported by: NIMH
Abstract View MODEL FOR SLOW (2-3 HZ) NEOCORTICAL PAROXYSMAL OSCILLATIONS IN VIVO

The oscillations observed in EEG and intracellular recordings during slow neocortical paroxysmal activity are larger than those during slow-wave sleep (SWS) activity as a consequence of increased synchrony of neuronal firing. The mechanisms underlying paroxysmal activity were investigated in computational network models of cortical neurons, which included voltage and Ca2+-dependent currents. Intraand extracellular K+ and Na+ concentrations were continuously updated based on the current flows, Na+/K+ pumps and glial buffering. A transition from SWS oscillations to 2-3 Hz seizure-like activity occurred in the model following increases in the extracellular K+ concentration. The amplitudes of both hyperpolarizing and depolarizing membrane potentials were increased and neurons fired highly synchronously; in some cells depolarization led to spike inactivation lasting 50-100 ms. Enhanced activation of Ih in a small set of neurons was the main factor maintaining 2-3 Hz activity. Slow changes of K+ and Na+ reversal potentials reduced the frequency of paroxysmal oscillations during the first 3-5 sec after seizure onset and also decreased the hyperpolarizing potentials. In some cases, this led to fast oscillations in the frequency range 10-15 Hz, which were similar to the fast runs observed during seizures in vivo. These results suggest that modification of the intrinsic currents mediated by an increased concentration of extracellular K+ can explain neocortical paroxysmal oscillations in vivo. Supported by: NIH, HHMI, Human Frontier Science Program and the Canadian Institute of Health Research.