nils-herrmann/clean_scirepeval huggingface dataset:
Spiking-bursting activity in the thalamic reticular nucleus initiates sequences of spindle oscillations in thalamic networks.

Recent intracellular and local field potential recordings from thalamic reticular (RE) neurons in vivo as well as computational modeling of the isolated RE nucleus suggest that, at relatively hyperpolarized levels of membrane potentials, the inhibitory postsynaptic potentials (IPSPs) between RE cells can be reversed and gamma-aminobutyric acid-A (GABA(A)) -mediated depolarization can generate persistent spatio-temporal patterns in the RE nucleus. Here we investigate how this activity affects the spatio-temporal properties of spindle oscillations with computer models of interacting RE and thalamocortical (TC) cells. In a one-dimensional network of RE and TC cells, sequences of spindle oscillations alternated with localized patterns of spike-burst activity propagating inside the RE network. New sequences of spindle oscillations were initiated after removal of I(h)-mediated depolarization of the TC cells. The length of the interspindle lulls depended on the intrinsic and synaptic properties of RE and TC cells and was in the range of 3-20 s. In a two-dimensional model, GABA(A)-mediated 2-3 Hz oscillations persisted in the RE nucleus during interspindle lulls and initiated spindle sequences at many foci within the RE-TC network simultaneously. This model predicts that the intrinsic properties of the reticular thalamus may contribute to the synchrony of spindle oscillations observed in vivo.
Abstract View IMPACT OF CORRELATED INPUT SPIKE TRAINS ON THE FIRING RATE AND VARIABILITY OF A POSTSYNAPTIC NEURON--A MODELING STUDY

In cortical neurons, the arrival of a spike from one input may or may not be correlated with the arrival of other spikes from different inputs. We asked how the firing rate and variability of a postsynaptic neuron vary as functions of the correlations between its input spike trains. We developed a stochastic model in which the membrane potential performs a random-walk toward threshold, and derived an analytic solution for the mean output firing rate as a function of the firing rates and pairwise correlations of the inputs. This model made three quantitative predictions. (1) Correlations between excitatory-excitatory or inhibitory-inhibitory input pairs increase the fluctuations in synaptic drive, whereas correlations between excitatory-inhibitory pairs decrease them. (2) These fluctuations may increase firing probability substantially, particularly when the output firing rate is low. (3) The strength and details of these interactions depend critically on the balance between excitation and inhibition. The results were confirmed and extended through computer simulations of an integrate-and-fire neuron with multiple conductances and hundreds of inputs. The input-output rate curves and the changes produced by various correlation patterns observed in simulations were in excellent agreement with the theoretical predictions. The results suggest that weak temporal correlations produced by signals shared among inputs may have two major effects upon a typical cortical neuron: first, to raise its firing probability appreciably, particularly around firing threshold, and second, to increase the variability of the output spike train at all rates. Supported by: The Howard Hughes Medical Institute.
Neurocomputational models of working memory

During working memory tasks, the firing rates of single neurons recorded in behaving monkeys remain elevated without external cues. Modeling studies have explored different mechanisms that could underlie this selective persistent activity, including recurrent excitation within cell assemblies, synfire chains and single-cell bistability. The models show how sustained activity can be stable in the presence of noise and distractors, how different synaptic and voltage-gated conductances contribute to persistent activity, how neuromodulation could influence its robustness, how completely novel items could be maintained, and how continuous attractor states might be achieved. More work is needed to address the full repertoire of neural dynamics observed during working memory tasks.
Abstract View ENTRAINMENT BY SYNCHRONIZED INHIBITION BOOSTS INFORMATION TRANSFER IN NEOCORTICAL NEURONS

Synchronized electrical activity due to the firing of action potentials by neurons in synaptically connected networks is ubiquitous in the nervous system. We investigate, theoretically and experimentally, the ability of a single pyramidal neuron to transduce the information content of a synchronized inhibitory synaptic drive, generated by an interneuron network, into an information-rich output of neuron spike times. The neuron is entrained to the periodic network drive when the jitter in the input spike times is sufficiently small (i.e. high precision), and the number of presynaptic spikes during one drive cycle is sufficiently large. The Shannon entropy of the output of spike times is reduced sharply during entrainment. Surprisingly, however, the amount of transduced information as measured by the mutual information is significantly increased during entrainment. This increase is due to the reduced contribution of the internal dynamics to the output variability. This has consequences for the regulation of cortical information processing by neuromodulators. Supported by: Northeastern CIRCS fund, Sloan Foundation and the Howard Hughes Medical Institute.
FLEXIBLE FUNCTIONAL CONNECTIVITY IN WORKING MEMORY NETWORKS WITH NON-MONOTONIC NEURAL RESPONSE FUNCTIONS

In most neural models the output firing rate of a single neuron is assumed to be a monotonic function (e.g. linear or logarithmic) of the rate of its inputs. However, there are circumstances where the output of a cortical neuron can be a non-monotonic function of its inputs (e.g. gaussian). We demonstrate here three different biophysical mechanisms that individually or in combination could produce strongly non-monotonic input/output-functions: 1) Synaptic depression and facilitation; 2) Strong voltage-gated conductances in dendritic spines or small dendritic arbors; and 3) Local interactions between co-activated pyramidal cells and interneurons. For example, high local densities of fast Na+/K+ channels might amplify low-frequency/high-amplitude synaptic inputs, but currents with slower time courses (L-type Ca2+, IA) might favor high-frequency/low amplitude inputs. These mechanisms could allow a neuron to respond best to a narrow range in input activity levels, where the optimal range might be different for different synaptic connections. The consequences of non-monotonic response tunings were explored in a network of leaky-integrate-and-fire neurons. In such a network, one set of presynaptic neurons might activate different assemblies of postsynaptic neurons depending on its state of firing. Assuming that the 4-8 synaptic connections between pairs of pyramidal cells (e.g., Markram et al. 1997) have different frequency tunings, several different sets of functional connectivities can be stored in the same network such that different sets of synaptic connections will be activated depending on the firing rates of the neurons. This might explain how different spatial maps are stored in the same hippocampal network, how multi-stability arises in neural recordings during parametric working memory tasks, or how top down influences of context shape the transfer properties of a network. Supported by: a grant from the Deutsche Forschungsgemeinschaft.
Abstract View DYNAMICS OF EVENT-RELATED COHERENCE BETWEEN INDEPENDENT COMPONENTS OF THE HUMAN EEG

We demonstrate that event-related coherence in the human EEG, computed using a short-time Fourier wavelet decomposition, shows multiple synchronization frequencies with abrupt event-related jumps in coherence frequency and, less frequently, in synchronization lag. Previous studies of EEG coherence have suffered from confounds arising from the large projection areas for all but the most superficial brain generator configurations. Since projections from different generators overlap widely, single scalp electrode channels typically pick up and sum activity from a relatively large number of discrete EEG sources. Changes in coherence between two scalp electrode channels thus may reflect any of several types of changes in the summed mixtures of activities they record. Computing coherence between independent components (ICs) of the EEG, derived using the infomax ICA algorithm, increases the functional specificity of the derived results. Study of event-related coherence between ICs suggests that cooperativity between brain networks may be accomplished through joint oscillatory processes operating at multiple discrete (theta to gamma) frequencies, only some of which may appear as peaks in the EEG power spectra, and these oscillatory linkages may be transiently created, broken or transformed by experimental events. Supported by: The Office of Naval Research, Howard Hughes Medical Institute, National Institutes of Health and the Swartz Foundation.
Abstract View MODEL OF SLOW-WAVE SLEEP AND ITS TRANSITION TO ACTIVATED STATES IN THALAMOCORTICAL NETWORK

In non-anesthetized cats slow-wave sleep (SWS) is composed of sequences of silent (down) and active (up) states; the down states are absent during REM sleep and wake. SWS oscillations ( < 1 Hz ) and their transformation to the active awake state were investigated with computational models of the thalamocortical system. The summation of the miniature EPSPs during depth-positive (silent) phase of an oscillation could activate the persistent sodium current and depolarize the membrane of cortical pyramidal (PY) cells sufficiently for spike generation. Progressive depression of the excitatory interconnections and activation of Ca2+ dependent K+ current led to termination of the 20-25 Hz activity after 500-1000 ms. Including thalamocortical (TC) and thalamic reticular (RE) neurons to the model slightly increased the frequency of SWS like oscillation and introduced waning spindle sequences. To model the effects of ACh increase, which lead to activated states, the K+ leak current in PY and TC cells and PY-PY and TC-RE-TC synaptic conductances were reduced. These changes eliminated the hyperpolarizing phases and transformed the cortical network to the tonic firing at frequency 15-20 Hz. TC cells displayed IPSPs originating from single spikes in RE neurons and discharged spontaneously at very low rates. Afferent volleys randomly applied to TC neurons evoked firing that was reflected in the cortex. Thus, this model describes the essential features of SWS and activated states of thalamocortical system as well as the transition between them. Supported by: Howard Hughes Medical Institute, Sloan Foundation, MRC of Canada and Human Frontier Science Program.
Abstract View CAN THE VISUAL SYSTEM ACCOUNT FOR LATENCY DIFFERENCES?

In a previous study (Eagleman & Sejnowski, Science, 2000), we demonstrated that visual perception seems to access incoming information from 80 ms after an event before attributing a perceptual decision to the time of the event. This postdictive framework stands in contrast to the latency difference (LD) hypothesis, which suggests that the leading edges of signals race through the brain to perceptual end points. We here extend our examination of the LD hypothesis with several psychophysical experiments. Even at the early stages of the visual system, physiological latencies vary by about 80 ms between highand low-luminace signals. Thus, using luminance as a parameter, we introduce several visual displays whose appearance should be predicted by LD. In one experiment, a moving square is defined by sequential frames of randomly-placed flashes. The flashes appear in a luminance gradient, from bright on one side of the square to dim (below threshold) on the other. The LD hypothesis predicts that since bright flashes are processed more quickly, the moving square should appear to expand when moving in one direction (and contract in the other direction). Surprisingly, no such illusion is seen by participants. To examine this further, we noted that many illusions seem to be well explained by LD: for example, two flashes, differing in luminance, can yield a sensation of apparent movement from bright to dim. We show that adding additional flashes, over a larger area, can reduce or abolish the illusion. Apparently, the abolition can come from the addition of contextual or 'higher level' information, which can only be explained by perceptual delay and postdiction, wherein the system waits for the slowest information to arrive before committing to a perceptual decision. These results suggest that the visual system normally allows the context of a scene to correct for latency differences. These experiments help us to address the underlying brain circuitry that could allow widely varying stimulus-evoked latencies to be reconciled with precise psychophysical temporal detection.
Abstract View CORTICAL IH PLAYS A ROLE IN THE GENERATION OF PAROXYSMAL ACTIVITIES

Slow neocortical paroxysmal activities are characterized by increased amplitude of depolarizing and hyperpolarizing envelopes in some neocortical neurons, compared to slow sleep oscillations (Steriade et al., 1998). Sudden depolarization leading to the next paroxysmal cycle starts when the majority of cortical neurons are hyperpolarized. At least two factors play a critical role in the generation of hyperpolarizing potentials. These are the Ca2+-dependent K+ current and the disfacilitation that result from the temporal absence of synaptic activities in the network. Here, in experiments and computational models we investigated the possible role of hyperpolarization-activated depolarizing current (Ih) of cortical neurons in the generation of paroxysmal onsets. In vivo intracellular recordings with Cs+ filled pipettes suggest that K+ currents are significantly implicated in the generation of hyperpolarizing potentials during seizures. Earlier data showed an
A natural basis for efficient brain-actuated control.

The prospect of noninvasive brain-actuated control of computerized screen displays or locomotive devices is of interest to many and of crucial importance to a few 'locked-in' subjects who experience near total motor paralysis while retaining sensory and mental faculties. Currently several groups are attempting to achieve brain-actuated control of screen displays using operant conditioning of particular features of the spontaneous scalp electroencephalogram (EEG) including central mu-rhythms (9-12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to be a foundation for new research in the design of systems for detection and operant control of endogenous EEG rhythms to achieve flexible EEG-based communication. ICA separates multichannel EEG data into spatially static and temporally independent components including separate components accounting for posterior alpha rhythms and central mu activities. We demonstrate using data from a visual selective attention task that ICA-derived mu-components can show much stronger spectral reactivity to motor events than activity measures for single scalp channels. ICA decompositions of spontaneous EEG would thus appear to form a natural basis for operant conditioning to achieve efficient and multidimensional brain-actuated control in motor-limited and locked-in subjects.
Models of basal ganglia and cerebellum for sensorimotor integration and predictive control

This paper presents a sensorimotor architecture integrating computational models of a cerebellum and a basal ganglia and operating on a microrobot. The computational models enable a microrobot to learn to track a moving object and anticipate future positions using a CCD camera. The architecture features pre-processing modules for coordinate transformation and instantaneous orientation extraction. Learning of motor control is implemented using predictive Hebbian reinforcement-learning algorithm in the basal ganglia model. Learning of sensory predictions makes use of a combination of long-term depression (LTD) and long-term potentiation (LTP) adaptation rules within the cerebellum model. The basal ganglia model uses the visual inputs to develop sensorimotor mapping for motor control, while the cerebellum module uses robot orientation and world- coordinate transformed inputs to predict the location of the moving object in a robot centered coordinate system. We propose several hypotheses about the functional role of cell populations in the cerebellum and argue that mossy fiber projections to the deep cerebellar nucleus (DCN) could play a coordinate transformation role and act as gain fields. We propose that such transformation could be learnt early in the brain development stages and could be guided by the activity of the climbing fibers. Proprioceptor mossy fibers projecting to the DCN and providing robot orientation with respect to a reference system could be involved in this case. Other mossy fibers carrying visual sensory input provide visual patterns to the granule cells. The combined activities of the granule and the Purkinje cells store spatial representations of the target patterns. The combinations of mossy and Purkinje projections to the DCN provide a prediction of the location of the moving target taking into consideration the robot orientation. Results of lesion simulations based on our model show degradations similar to those reported in cerebellar lesion studies on monkeys.
Dynamics of dendritic calcium transients evoked by quantal release at excitatory hippocampal synapses.

Synaptic N-methyl-D-aspartate (NMDA) receptors detect coincident pre- and postsynaptic activity and play a critical role in triggering changes in synaptic strength at central synapses. Despite intensive study of synaptic plasticity, relatively little is known about the magnitude and duration of calcium accumulation caused by unitary events at individual synapses. We used fluorescence imaging to detect NMDA receptor-mediated miniature synaptic calcium transients (MSCTs) caused by spontaneous release of synaptic vesicles in dendrites of cultured hippocampal neurons. MSCTs originated focally in dendritic regions <1 microm in length and decayed with a time constant of 0.35 +/- 0.03 s. Multiple occurrences of MSCTs recorded at single sites had fluctuating amplitudes, with a coefficient of variation of 0.34. From the reduction in the spatial spread of MSCTs with decreasing concentration of indicator dye, we estimated that the dominant endogenous calcium buffer in dendrites is relatively immobile (diffusion coefficient between 10 and 50 microm(2)/s). We conclude that calcium rise caused by spontaneous quantal synaptic NMDA receptor activation (i) is variable, (ii) lasts for a time period briefer than previous measurements indicate, and (iii) is confined by endogenous calcium buffers to local dendritic regions even when synapses are not on spines.
Constraining constructivism: Cortical and sub-cortical constraints on learning in development

It is becoming increasingly clear that acquiring cognitive skills is feasible only with significant developmental constraints. However, recent research provides the strongest evidence to date for constructivist development. Here, we examine how these two apparently conflicting perspectives may be reconciled. Specifically, we suggest that subcortical and cortical structures possess divergent developmental strategies, with many subcortical structures satisfying Fodor's criteria for modularity. These structures constitute an early behavioral system that guides the construction of later emerging cortical structures, for which there is little evidence for modularity. Thus, we focus on how the dynamic time course of development itself implicitly constrains the emergence of cortical representations, reducing the requirement for built-in encodings of knowledge in cortical circuits, as on the traditional nativist conception.
Measuring facial expressions by computer image analysis.

Facial expressions provide an important behavioral measure for the study of emotion, cognitive processes, and social interaction. The Facial Action Coding System (Ekman & Friesen, 1978) is an objective method for quantifying facial movement in terms of component actions. We applied computer image analysis to the problem of automatically detecting facial actions in sequences of images. Three approaches were compared: holistic spatial analysis, explicit measurement of features such as wrinkles, and estimation of motion flow fields. The three methods were combined in a hybrid system that classified six upper facial actions with 91% accuracy. The hybrid system outperformed human nonexperts on this task and performed as well as highly trained experts. An automated system would make facial expression measurement more widely accessible as a research tool in behavioral science and investigations of the neural substrates of emotion.
The Book of Hebb Minireview and long-range lateral connections within cortical areas

in the cerebrum: " ... we know practically nothing about what goes on between the arrival of the excitation at a sensory projection area and its later departure from the It has been 50 years since Donald Hebb published the motor area of the cortex " (xvi). Hebb conjectured that Organization of Behavior: A Neuropsychological Theory cortical circuits admit self-sustaining activity that rever-in 1949. This book was written at a time when behavior-berated in what he called " cell assemblies. " This idea ism was dominant in North American psychology. The was inspired by evidence for recurrent connections be-approach that Hebb advocated, based on what was tween neighboring cells in the cortex. Although rever-then known about the brain, was out of favor among beratory activity lasting for up to half a second had psychologists who believed that only external sensory been observed by Lorente de Nó , Hebb went further stimuli and motor responses ought to be included in and suggested that such activity in one cortical circuit any explanation of behavior. Most neuroscientists have could through converging projections activate other ar-heard about the " Hebb synapse, " but few know why he eas of cortex and lead to a sequence of activations he postulated this learning rule. This is a good time to take called a " phase sequence. " Although these ideas remain a closer look at this book and let Hebb speak for himself. highly speculative, they reflect recent issues such as Hebb was on the faculty of the Psychology Depart-spike timing and spike synchrony that today are at the ment at McGill University. One of his research interests forefront of theoretical research on the cortical neural was the behavioral effects of brain lesions, and he had code (Abbott and Sejnowski, 1999). collaborated with Wilder Penfield, a colleague at McGill, Hebb needed a way to sustain persistent reverbera-but he was more broadly interested in the development tory activity (a " trace ") in cortical circuits. He proposed of behavior and learning, which he saw as intimately that patterns of connections between neurons could related. In 1949, much of what we now take for granted sustain reverberatory activity if their strengths could be about the organization of the nervous system and the adjusted by an activity-dependent mechanism for syn-properties of neurons was not yet discovered. Hodgkin aptic plasticity that he called a " Neurophysiological Pos-and Huxley's landmark series …
Neuronal Tuning: To Sharpen or Broaden?

Sensory and motor variables are typically represented by a population of broadly tuned neurons. A coarser representation with broader tuning can often improve coding accuracy, but sometimes the accuracy may also improve with sharper tuning. The theoretical analysis here shows that the relationship between tuning width and accuracy depends crucially on the dimension of the encoded variable. A general rule is derived for how the Fisher information scales with the tuning width, regardless of the exact shape of the tuning function, the probability distribution of spikes, and allowing some correlated noise between neurons. These results demonstrate a universal dimensionality effect in neural population coding.