Simulating Cortical Feedback Modulation as Changes in Excitation and Inhibition in a Cortical Circuit Model.

Cortical feedback pathways are hypothesized to distribute context-dependent signals during flexible behavior. Recent experimental work has attempted to understand the mechanisms by which cortical feedback inputs modulate their target regions. Within the mouse whisker sensorimotor system, cortical feedback stimulation modulates spontaneous activity and sensory responsiveness, leading to enhanced sensory representations. However, the cellular mechanisms underlying these effects are currently unknown. In this study we use a simplified neural circuit model, which includes two recurrent excitatory populations and global inhibition, to simulate cortical modulation. First, we demonstrate how changes in the strengths of excitation and inhibition alter the input-output processing responses of our model. Second, we compare these responses with experimental findings from cortical feedback stimulation. Our analyses predict that enhanced inhibition underlies the changes in spontaneous and sensory evoked activity observed experimentally. More generally, these analyses provide a framework for relating cellular and synaptic properties to emergent circuit function and dynamic modulation

First impressions and perceived roles: Palestinian perceptions on foreign aid

This paper summarizes some results of a wider research on foreign aid that was conducted in the West Bank and Gaza Strip in 2010. It seeks to describe the impressions and feelings of Palestinian aid beneficiaries as well as the roles and functions they attached to foreign aid. To capture and measure local perceptions on Western assistance a series of individual in depth interviews and few focus group interviews were conducted in the Palestinian territories. The interview transcripts were processed by content analysis. As research results show — from the perspective of aid beneficiaries — foreign aid is more related to human dignity than to any economic development. All this implies that frustration with the ongoing Israeli-Palestinian conflict inevitably embraces the donor policies and practices too

Movement and Performance Explain Widespread Cortical Activity in a Visual Detection Task.

Recent studies in mice reveal widespread cortical signals during task performance; however, the various task-related and task-independent processes underlying this activity are incompletely understood. Here, we recorded wide-field neural activity, as revealed by GCaMP6s, from dorsal cortex while simultaneously monitoring orofacial movements, walking, and arousal (pupil diameter) of head-fixed mice performing a Go/NoGo visual detection task and examined the ability of task performance and spontaneous or task-related movements to predict cortical activity. A linear model was able to explain a significant fraction (33-55% of variance) of widefield dorsal cortical activity, with the largest factors being movements (facial, walk, eye), response choice (hit, miss, false alarm), and arousal and indicate that a significant fraction of trial-to-trial variability arises from both spontaneous and task-related changes in state (e.g., movements, arousal). Importantly, secondary motor cortex was highly correlated with lick rate, critical for optimal task performance (high d'), and was the first region to significantly predict the lick response on target trials. These findings suggest that secondary motor cortex is critically involved in the decision and performance of learned movements and indicate that a significant fraction of trial-to-trial variation in cortical activity results from spontaneous and task-related movements and variations in behavioral/arousal state

Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications

A computational architecture modeling the relation between perception and action is proposed. Basic brain processes representing synaptic plasticity are first abstracted through asynchronous communication protocols and implemented as virtual microcircuits. These are used in turn to build mesoscale circuits embodying parallel cognitive processes. Encoding these circuits into symbolic expressions gives finally rise to neuro-inspired programs that are compiled into pseudo-code to be interpreted by a virtual machine. Quantitative evaluation measures are given by the modification of synapse weights over time. This approach is illustrated by models of simple forms of behaviors exhibiting cognition up to the third level of animal awareness. As a potential benefit, symbolic models of emergent psychological mechanisms could lead to the discovery of the learning processes involved in the development of cognition. The executable specifications of an experimental platform allowing for the reproduction of simulated experiments are given in “Appendix”

A theory of how active behavior stabilises neural activity: neural gain modulation by closed-loop environmental feedback

During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence of neural fluctuations, across the brain, on closed-loop brain/body/environment interactions strongly supporting the idea that brain function cannot be fully understood through open-loop approaches alone

Incorporation of DPP6a and DPP6K Variants in Ternary Kv4 Channel Complex Reconstitutes Properties of A-type K Current in Rat Cerebellar Granule Cells

Dipeptidyl peptidase-like protein 6 (DPP6) proteins co-assemble with Kv4 channel α-subunits and Kv channel-interacting proteins (KChIPs) to form channel protein complexes underlying neuronal somatodendritic A-type potassium current (ISA). DPP6 proteins are expressed as N-terminal variants (DPP6a, DPP6K, DPP6S, DPP6L) that result from alternative mRNA initiation and exhibit overlapping expression patterns. Here, we study the role DPP6 variants play in shaping the functional properties of ISA found in cerebellar granule (CG) cells using quantitative RT-PCR and voltage-clamp recordings of whole-cell currents from reconstituted channel complexes and native ISA channels. Differential expression of DPP6 variants was detected in rat CG cells, with DPP6K (41±3%)>DPP6a (33±3%)>>DPP6S (18±2%)>DPP6L (8±3%). To better understand how DPP6 variants shape native neuronal ISA, we focused on studying interactions between the two dominant variants, DPP6K and DPP6a. Although previous studies did not identify unique functional effects of DPP6K, we find that the unique N-terminus of DPP6K modulates the effects of KChIP proteins, slowing recovery and producing a negative shift in the steady-state inactivation curve. By contrast, DPP6a uses its distinct N-terminus to directly confer rapid N-type inactivation independently of KChIP3a. When DPP6a and DPP6K are co-expressed in ratios similar to those found in CG cells, their distinct effects compete in modulating channel function. The more rapid inactivation from DPP6a dominates during strong depolarization; however, DPP6K produces a negative shift in the steady-state inactivation curve and introduces a slow phase of recovery from inactivation. A direct comparison to the native CG cell ISA shows that these mixed effects are present in the native channels. Our results support the hypothesis that the precise expression and co-assembly of different auxiliary subunit variants are important factors in shaping the ISA functional properties in specific neuronal populations

Neural control of brain state

How the brain takes in information, makes a decision, and acts on this decision is strongly influenced by the ongoing and constant fluctuations of state. Understanding the nature of these brain states and how they are controlled is critical to making sense of how the nervous system operates, both normally and abnormally. While broadly projecting neuromodulatory systems acting through metabotropic pathways have long been appreciated to be critical for determining brain state, more recent investigations have revealed a prominent role for fast acting neurotransmitter pathways for temporally and spatially precise control of neural processing. Corticocortical and thalamocortical glutamatergic projections can rapidly and precisely control brain state by changing both the nature of ongoing activity and by controlling the gain and precision of neural responses

Competing Neural Ensembles in Motor Cortex Gate Goal-Directed Motor Output

Unit recordings in behaving animals have revealed the transformation of sensory to motor representations in cortical neurons. However, we still lack basic insights into the mechanisms by which neurons interact to generate such transformations. Here, we study cortical circuits related to behavioral control in mice engaged in a sensory detection task. We recorded neural activity using extracellular and intracellular techniques and analyzed the task-related neural dynamics to reveal underlying circuit processes. Within motor cortex, we find two populations of neurons that have opposing spiking patterns in anticipation of movement. From correlation analyses and circuit modeling, we suggest that these dynamics reflect neural ensembles engaged in a competition. Furthermore, we demonstrate how this competitive circuit may convert a transient, sensory stimulus into a motor command. Together, these data reveal cellular and circuit processes underlying behavioral control and establish an essential framework for future studies linking cellular activity to behavior

Segmented Operations for Sparse Matrix Computation on Vector Multiprocessors

In this paper we present a new technique for sparse matrix multiplication on vector multiprocessors based on the efficient implementation of a segmented sum operation. We describe how the segmented sum can be implemented on vector multiprocessors such that it both fully vectorizes within each processor and parallelizes across processors. Because of our method's insensitivity to relative row size, it is better suited than the Ellpack/Itpack or the Jagged Diagonal algorithms for matrices which have a varying number of non-zero elements in each row. Furthermore, our approach requires less preprocessing (no more time than a single sparse matrix-vector multiplication), less auxiliary storage, and uses a more convenient data representation (an augmented form of the standard compressed sparse row format). We have implemented our algorithm (SEGMV) on the Cray Y-MP C90, and have compared its performance with other methods on a variety of sparse matrices from the Harwell-Boeing collection and in..

Synaptic Mechanisms of Tight Spike Synchrony at Gamma Frequency in Cerebral Cortex

During the generation of higher-frequency (e.g., gamma) oscillations, cortical neurons can exhibit pairwise tight (<10 ms) spike synchrony. To understand how synaptic currents contribute to rhythmic activity and spike synchrony, we performed dual whole-cell recordings in mouse entorhinal cortical slices generating periodic activity (the slow oscillation). This preparation exhibited a significant amount of gamma-coherent spike synchrony during the active phase of the slow oscillation (Up state), particularly among fast-spiking inhibitory interneurons. IPSCs arriving in pairs of either pyramidal or fast-spiking neurons during the Up state were highly synchronized and exhibited significant coherence at frequencies from 10 to 100 Hz, peaking at ∼40 Hz, suggesting both synchronous discharge of, and synaptic divergence from, nearby inhibitory neurons. By inferring synaptic currents related to spike generation in simultaneously recorded pyramidal or fast-spiking neurons, we detected a decay of inhibition ∼20 ms before spiking. In fast-spiking interneurons, this was followed by an even larger excitatory input immediately before spike generation. Consistent with an important role for phasic excitation in driving spiking, we found that the correlation of excitatory inputs was highly predictive of spike synchrony in pairs of fast-spiking interneurons. Interestingly, spike synchrony in fast-spiking interneurons was not related to the strength of gap junctional coupling, and was still prevalent in connexin 36 knock-out animals. Our results support the pyramidal-interneuron gamma model of fast rhythmic oscillation in the cerebral cortex and suggest that spike synchrony and phase preference arises from the precise interaction of excitatory–inhibitory postsynaptic currents. SIGNIFICANCE STATEMENT We dissected the cellular and synaptic basis of spike synchrony occurring at gamma frequency (30–80 Hz). We used simultaneous targeted whole-cell recordings in an active slice preparation and analyzed the relationships between synaptic inputs and spike generation. We found that both pyramidal and fast-spiking neurons receive large, coherent inhibitory synaptic inputs at gamma frequency. In addition, we found that fast-spiking interneurons receive large, phasic excitatory synaptic inputs immediately before spike generation followed shortly by synaptic inhibition. These data support the principal-interneuron gamma generation model, and reveal how the synaptic connectivity between excitatory and inhibitory neurons supports the generation of gamma oscillations and spike synchrony