The Role of Incentives in the Organizational Structure: A model of Performance Maximization

To optimize firm performance, both individual employee and organizational performance must be taken into account. In this paper, we combine several theoretical models of individual and organizational behavior to propose the Organizational Performance Maximization Model (OPMM), in which individual and corporate performance maximization are combined

Functional Clustering Drives Encoding Improvement in a Developing Brain Network during Awake Visual Learning

Sensory experience drives dramatic structural and functional plasticity in developing neurons. However, for single-neuron plasticity to optimally improve whole-network encoding of sensory information, changes must be coordinated between neurons to ensure a full range of stimuli is efficiently represented. Using two-photon calcium imaging to monitor evoked activity in over 100 neurons simultaneously, we investigate network-level changes in the developing Xenopus laevis tectum during visual training with motion stimuli. Training causes stimulus-specific changes in neuronal responses and interactions, resulting in improved population encoding. This plasticity is spatially structured, increasing tuning curve similarity and interactions among nearby neurons, and decreasing interactions among distant neurons. Training does not improve encoding by single clusters of similarly responding neurons, but improves encoding across clusters, indicating coordinated plasticity across the network. NMDA receptor blockade prevents coordinated plasticity, reduces clustering, and abolishes whole-network encoding improvement. We conclude that NMDA receptors support experience-dependent network self-organization, allowing efficient population coding of a diverse range of stimuli.Canadian Institutes of Health Researc

Sensory experience driven network plasticity in the awake developing brain

During embryonic activity‐dependent brain circuit refinement, neurons receiving the same natural sensory input may undergo either long‐term potentiation (LTP) or depression (LTD). While the origin of variable plasticity in vivo is unknown, the type of plasticity induced plays a key role in shaping dynamic neural circuit synaptogenesis and growth. Here, we investigate the effects of natural visual stimuli on functional neuronal firing within the intact and awake developing brain using calcium imaging of 100s of central neurons in the Xenopus retinotectal system. We find that specific patterns of visual stimuli shift population responses towards either potentiation or depression in an N‐methyl‐D‐aspartate receptor (NMDAR)‐dependent manner. In agreement with the Bienenstock‐Cooper‐Munro (BCM) theory, our results show that functional potentiation or depression in individual neurons can be predicted by their specific receptive field properties and endogenous firing rates prior to plasticity induction. Enhancing pre‐training activity shifts plasticity outcomes as predicted by BCM, and this induced metaplasticity is also NMDAR dependent. Furthermore, network analysis reveals an increase in correlated firing of neurons that undergo potentiation. These findings implicate metaplasticity as a natural property governing experience‐dependent refinement of nascent embryonic brain circuits.Medicine, Faculty ofGraduat

Metaplasticity Governs Natural Experience-Driven Plasticity of Nascent Embryonic Brain Circuits

SummaryDuring embryogenesis, brain neurons receiving the same sensory input may undergo potentiation or depression. While the origin of variable plasticity in vivo is unknown, it plays a key role in shaping dynamic neural circuit refinement. Here, we investigate effects of natural visual stimuli on neuronal firing within the intact, awake, developing brain using calcium imaging of 100 s of central neurons in the Xenopus retinotectal system. We find that specific patterns of visual stimuli shift population responses toward either potentiation or depression in an N-methyl-D-aspartate receptor (NMDA-R)-dependent manner. In agreement with Bienenstock-Cooper-Munro metaplasticity, our results show that functional potentiation or depression can be predicted by individual neurons' specific receptive field properties and historic firing rates. Interestingly, this activity-dependent metaplasticity is itself NMDA-R dependent. Furthermore, network analysis reveals increased correlated firing of neurons that undergo potentiation. These findings implicate metaplasticity as a natural property regulating experience-dependent refinement of nascent embryonic brain circuits

Neural mechanisms of credit card spending

Credit cards have often been blamed for consumer overspending and for the growth in household debt. Indeed, laboratory studies of purchase behavior have shown that credit cards can facilitate spending in ways that are difficult to justify on purely financial grounds. However, the psychological mechanisms behind this spending facilitation effect remain conjectural. A leading hypothesis is that credit cards reduce the pain of payment and so ‘release the brakes’ that hold expenditures in check. Alternatively, credit cards could provide a ‘step on the gas,’ increasing motivation to spend. Here we present the first evidence of differences in brain activation in the presence of real credit and cash purchase opportunities. In an fMRI shopping task, participants purchased items tailored to their interests, either by using a personal credit card or their own cash. Credit card purchases were associated with strong activation in the striatum, which coincided with onset of the credit card cue and was not related to product price. In contrast, reward network activation weakly predicted cash purchases, and only among relatively cheaper items. The presence of reward network activation differences highlights the potential neural impact of novel payment instruments in stimulating spending—these fundamental reward mechanisms could be exploited by new payment methods as we transition to a purely cashless society

Training induces NMDAR-dependent improvement of whole-network encoding.

<p>(a) Time course of noise-correlation–based (red) and independent (blue) decoding performance. Light curves, improvement is blocked by MK-801. Bars denote early and late epochs. Decoding improvement is the decrease in decoding error relative to the independent decoder at the first timepoint. Both decoders improved from early to late epochs in control, but not MK-801–treated tadpoles (paired <i>t</i>-tests). (b) Decoding error of control (left, blue) and MK-801–treated (right, red) tadpoles over first hour of stimulation. Lighter shades denote decoding using the optimal independent decoder, darker shades mark noise correlation-based decoding. (c) Improvement, relative to the early epoch, of decoders trained on data from early (left two panels) or late (right two panels) epochs, used to decode early or late neuronal firing patterns. Performance decreased when decoding the epoch on which the decoder was not trained (center two panels; ANOVA). Asterisks in rightmost panel denote significant difference from corresponding value in leftmost panel. Error bars denote SEM. *<i>p</i><0.05; **<i>p</i><0.01.</p

NMDAR-dependent coordination between clusters supports network encoding improvement.

<p>(a) Preferred directions of example control (top) and MK801-treated (bottom) tadpoles during early (left) and late (right) epochs. Scale bar = 20 µm. (b) Receptive field diversity across the tectum during early and late epochs, in untreated (black) and MK801-treated (gray) tadpoles. Diversity decreased with training in MK-801–treated tadpoles (paired <i>t</i>-test, <i>p</i><0.05). (c) Mean decoding error of independent (blue) and correlation-based (red) decoding of single clusters during early and late epochs, in untreated and MK801-treated (lighter shades) tadpoles. (d) Mean decoding cooperation (decoding performance of two clusters taken together minus the maximum decoding performance of either taken alone) during early and late epochs, in untreated and MK801-treated tadpoles. Cooperation increased in control tadpoles and decreased in MK-801 treated tadpoles with training (paired <i>t</i>-tests). Number of clusters: untreated, <i>n</i> = 29; MK801, <i>n</i> = 25 clusters per epoch in seven tadpoles. Error bars denote SEM. Control, <i>n</i> = 7 tadpoles (277 neurons); MK801, <i>n</i> = 7 tadpoles (255 neurons). *<i>p</i><0.05; **<i>p</i><0.01.</p

Tectal noise correlations influence network decoding.

<p>(a) Recorded responses of two neurons (black and grey) in the same tadpole to eight consecutive presentations of the same stimulus. Responses vary in amplitude around their means (dotted lines). These neurons were noise correlated: variations in amplitude were shared. (b) Distribution of measured pairwise noise correlations (black dotted e) taken over a 1-h stimulation period, and values expected if neurons were independent (gray). Noise correlations were more positive (<i>p</i><10⁻⁵, <i>t</i>-test) and more variable (<i>p</i><10⁻⁸; X² variance test) than chance. (c) Scatterplot of pairwise linear noise correlations measured in two consecutive 30-min periods. Consecutive noise correlation measurements are correlated (<i>r</i> = 0.41, <i>p</i><10⁻⁸; linear regression). (d) Distribution of decoding errors under independent and noise correlation decoding of actual response patterns (left) and with responses shuffled for each stimulus type to remove noise correlations (right). Data from seven tadpoles, 277 neurons (b,d), 384 stimulus presentations (c), 192 stimulus presentations each 30 min. Error bars denote SEM. *<i>p</i><0.05; **<i>p</i><0.01.</p

Effects of visual training on single-neuron response properties.

<p>(a) Tuning curve dynamic range, the fraction by which a neuron's firing changes in response to different stimuli during early and late epochs. (b,c) Stimulus mutual information conveyed by single neuron (b) and neuron pair (c) firing patterns. Upper asterisks denote difference in the change with treatment. Lower asterisks denote significant change across epochs (paired <i>t</i>-test). (d) Evoked firing rates in control (black) and MK-801 treated (gray) tadpoles during first hour of stimulation. Each point corresponds to a single tadpole; error bars denote standard deviation across neurons within a given tadpole. MK-801 does not acutely affect evoked firing rates (<i>t</i>-test, <i>p</i> = 0.61). (e) Proportion of neurons showing direction (yellow), orientation (blue), both (green), or neither (red) selectivity in control (top) and MK-801–(bottom) treated tadpoles, in the first (left) and second (right) hour of stimulation. Asterisks denote significant change across epochs (paired <i>t</i>-test). (f,g) Mean normalized amplitude (f) and response reliability (g) over the course of visual training (black). Reliability increased with training (ANCOVA, <i>p</i><0.01). Neither measure was affected by MK-801 (gray) (ANCOVA, <i>p</i>>0.05). Reliability is the proportion of evoked responses with amplitude larger than the median spontaneous firing rate. Error bars denote SEM. *<i>p</i><0.05.</p

<i>In vivo</i> imaging of evoked network activity in the unanesthetized developing brain.

<p>(a) Experimental setup. Motion stimuli were presented to the left eye of awake, immobilized <i>Xenopus</i> tadpoles while imaging the right optic tectum. Neurons in the tectum (green circles) extend dendrites to receive visual input from retinal ganglion cells (red) of the contralateral eye. (b) Transmitted light image of a tadpole brain seen through the head. Green box, optic tectum. (c) Two-photon image of optical section corresponding to green box in (b). Tectum is loaded with OGB1-AM, a calcium-sensitive dye. Red box corresponds to the region of tectum monitored in our experiments. (d) Two-photon image of a patched neuron in awake tectum. (e) Simultaneous recording of somatic fluorescence (Δ<i>F</i>/<i>F</i>₀, top) and action potentials (green) in response to full field light stimuli of varying intensity, with actual (gray) and inferred (black) firing rates in the 5 s following each stimulus. (f) Expanded voltage trace for electrophysiological recording. Pink shading marks time of stimulus. The electrical transients bounding the stimulus period are clipped. Colored dots mark individual action potentials, which are magnified in the boxes at bottom.</p