2,249 research outputs found
Dopamine restores reward prediction errors in old age.
Senescence affects the ability to utilize information about the likelihood of rewards for optimal decision-making. Using functional magnetic resonance imaging in humans, we found that healthy older adults had an abnormal signature of expected value, resulting in an incomplete reward prediction error (RPE) signal in the nucleus accumbens, a brain region that receives rich input projections from substantia nigra/ventral tegmental area (SN/VTA) dopaminergic neurons. Structural connectivity between SN/VTA and striatum, measured by diffusion tensor imaging, was tightly coupled to inter-individual differences in the expression of this expected reward value signal. The dopamine precursor levodopa (L-DOPA) increased the task-based learning rate and task performance in some older adults to the level of young adults. This drug effect was linked to restoration of a canonical neural RPE. Our results identify a neurochemical signature underlying abnormal reward processing in older adults and indicate that this can be modulated by L-DOPA
Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility
Experimental data have revealed that neuronal connection efficacy exhibits
two forms of short-term plasticity, namely, short-term depression (STD) and
short-term facilitation (STF). They have time constants residing between fast
neural signaling and rapid learning, and may serve as substrates for neural
systems manipulating temporal information on relevant time scales. The present
study investigates the impact of STD and STF on the dynamics of continuous
attractor neural networks (CANNs) and their potential roles in neural
information processing. We find that STD endows the network with slow-decaying
plateau behaviors-the network that is initially being stimulated to an active
state decays to a silent state very slowly on the time scale of STD rather than
on the time scale of neural signaling. This provides a mechanism for neural
systems to hold sensory memory easily and shut off persistent activities
gracefully. With STF, we find that the network can hold a memory trace of
external inputs in the facilitated neuronal interactions, which provides a way
to stabilize the network response to noisy inputs, leading to improved accuracy
in population decoding. Furthermore, we find that STD increases the mobility of
the network states. The increased mobility enhances the tracking performance of
the network in response to time-varying stimuli, leading to anticipative neural
responses. In general, we find that STD and STP tend to have opposite effects
on network dynamics and complementary computational advantages, suggesting that
the brain may employ a strategy of weighting them differentially depending on
the computational purpose.Comment: 40 pages, 17 figure
Neuronal assembly dynamics in supervised and unsupervised learning scenarios
The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions
Event-driven simulations of a plastic, spiking neural network
We consider a fully-connected network of leaky integrate-and-fire neurons
with spike-timing-dependent plasticity. The plasticity is controlled by a
parameter representing the expected weight of a synapse between neurons that
are firing randomly with the same mean frequency. For low values of the
plasticity parameter, the activities of the system are dominated by noise,
while large values of the plasticity parameter lead to self-sustaining activity
in the network. We perform event-driven simulations on finite-size networks
with up to 128 neurons to find the stationary synaptic weight conformations for
different values of the plasticity parameter. In both the low and high activity
regimes, the synaptic weights are narrowly distributed around the plasticity
parameter value consistent with the predictions of mean-field theory. However,
the distribution broadens in the transition region between the two regimes,
representing emergent network structures. Using a pseudophysical approach for
visualization, we show that the emergent structures are of "path" or "hub"
type, observed at different values of the plasticity parameter in the
transition region.Comment: 9 pages, 6 figure
Effects of Noise in a Cortical Neural Model
Recently Segev et al. (Phys. Rev. E 64,2001, Phys.Rev.Let. 88, 2002) made
long-term observations of spontaneous activity of in-vitro cortical networks,
which differ from predictions of current models in many features. In this paper
we generalize the EI cortical model introduced in a previous paper (S.Scarpetta
et al. Neural Comput. 14, 2002), including intrinsic white noise and analyzing
effects of noise on the spontaneous activity of the nonlinear system, in order
to account for the experimental results of Segev et al.. Analytically we can
distinguish different regimes of activity, depending from the model parameters.
Using analytical results as a guide line, we perform simulations of the
nonlinear stochastic model in two different regimes, B and C. The Power
Spectrum Density (PSD) of the activity and the Inter-Event-Interval (IEI)
distributions are computed, and compared with experimental results. In regime B
the network shows stochastic resonance phenomena and noise induces aperiodic
collective synchronous oscillations that mimic experimental observations at 0.5
mM Ca concentration. In regime C the model shows spontaneous synchronous
periodic activity that mimic activity observed at 1 mM Ca concentration and the
PSD shows two peaks at the 1st and 2nd harmonics in agreement with experiments
at 1 mM Ca. Moreover (due to intrinsic noise and nonlinear activation function
effects) the PSD shows a broad band peak at low frequency. This feature,
observed experimentally, does not find explanation in the previous models.
Besides we identify parametric changes (namely increase of noise or decreasing
of excitatory connections) that reproduces the fading of periodicity found
experimentally at long times, and we identify a way to discriminate between
those two possible effects measuring experimentally the low frequency PSD.Comment: 25 pages, 10 figures, to appear in Phys. Rev.
Dynamics of Neural Networks with Continuous Attractors
We investigate the dynamics of continuous attractor neural networks (CANNs).
Due to the translational invariance of their neuronal interactions, CANNs can
hold a continuous family of stationary states. We systematically explore how
their neutral stability facilitates the tracking performance of a CANN, which
is believed to have wide applications in brain functions. We develop a
perturbative approach that utilizes the dominant movement of the network
stationary states in the state space. We quantify the distortions of the bump
shape during tracking, and study their effects on the tracking performance.
Results are obtained on the maximum speed for a moving stimulus to be
trackable, and the reaction time to catch up an abrupt change in stimulus.Comment: 6 pages, 7 figures with 4 caption
Longitudinal change in autonomic symptoms predicts activities of daily living and depression in Parkinson’s disease
Purpose: The primary objective of this study was to examine the relationship of longitudinal changes in autonomic symptom burden and longitudinal changes in activities of daily living (ADLs); a secondary analysis examined the impact of depressive symptoms in this relationship. Methods: Data were retrieved from the Parkinson’s Progression Markers Initiative (PPMI), a dataset documenting the natural history of newly diagnosed Parkinson’s disease (PD). The analysis focused on data from baseline, visit 6 (24 months after enrollment), and visit 12 (60 months after enrollment). The impact of longitudinal changes in autonomic symptom burden on longitudinal changes in ADLs function was examined. A secondary mediation analysis was performed to investigate whether longitudinal changes in depressive symptoms mediate the relationship between longitudinal changes in autonomic symptom burden and ADLs function. Results: Changes in autonomic symptom burden, cognitive function, depressive symptoms, and motor function all correlated with ADLs. Only changes in ADLs and depression were found to be associated with changes in autonomic symptom burden. We found that longitudinal change in autonomic symptoms was a significant predictor of change in ADLs at 24 and 60 months after enrollment, with the cardiovascular subscore being a major driver of this association. Mediation analysis revealed that the association between autonomic symptoms and ADLs is partially mediated by depressive symptoms. Conclusions: Longitudinal changes in autonomic symptoms impact ADLs function in patients with early signs of PD, both directly and indirectly through their impact on depressive symptoms. Future investigation into the influence of treatment of these symptoms on outcomes in PD is warranted
Mobility and stochastic resonance in spatially inhomogeneous system
The mobility of an overdamped particle, in a periodic potential tilted by a
constant external field and moving in a medium with periodic friction
coefficient is examined. When the potential and the friction coefficient have
the same periodicity but have a phase difference, the mobility shows many
interesting features as a function of the applied force, the temperature, etc.
The mobility shows stochastic resonance even for constant applied force, an
issue of much recent interest. The mobility also exhibits a resonance like
phenomenon as a function of the field strength and noise induced slowing down
of the particle in an appropriate parameter regime.Comment: 14 pages, 12 figures. Submitted to Phys. Rev.
Backreaction on the luminosity-redshift relation from gauge invariant light-cone averaging
Using a recently proposed gauge invariant formulation of light-cone
averaging, together with adapted "geodesic light-cone" coordinates, we show how
an "induced backreaction" effect emerges, in general, from correlated
fluctuations in the luminosity distance and covariant integration measure.
Considering a realistic stochastic spectrum of inhomogeneities of primordial
(inflationary) origin we find that both the induced backreaction on the
luminosity-redshift relation and the dispersion are larger than naively
expected. On the other hand the former, at least to leading order and in the
linear perturbative regime, cannot account by itself for the observed effects
of dark energy at large-redshifts. A full second-order calculation, or even
better a reliable estimate of contributions from the non-linear regime, appears
to be necessary before firm conclusions on the correct interpretation of the
data can be drawn.Comment: 22 pages, 4 figures. Comments and references added, Fig. 1 modified.
Version accepted for publication in JCA
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