The future of North American trade policy: lessons from NAFTA

This repository item contains a single issue of the Pardee Center Task Force Reports, a publication series that began publishing in 2009 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future.This Task Force Report written by an international group of trade policy experts calls for significant reforms to address adverse economic, environmental, labor and societal impacts created by the 1994 North American Free Trade Agreement (NAFTA). The report is intended to contribute to the discussion and decisions stemming from ongoing reviews of proposed reforms to NAFTA as well as to help shape future trade agreements. It offers detailed proposals on topics including services, manufacturing, agriculture, investment, intellectual property, labor, environment, and migration. Fifteen years after NAFTA was enacted, there is widespread agreement that the trade treaty among the United States, Canada and Mexico has fallen short of its stated goals. While proponents credit the agreement with stimulating the flow of goods, services, and investment among the North American countries, critics in all three countries argue that this has not brought improvements in the standards of living of most people. Rather than triggering a convergence across the three nations, NAFTA has accentuated the economic and regulatory asymmetries that had existed among the three countries. [TRUNCATED

Timescales of spike-train correlation for neural oscillators with common drive

We examine the effect of the phase-resetting curve (PRC) on the transfer of correlated input signals into correlated output spikes in a class of neural models receiving noisy, super-threshold stimulation. We use linear response theory to approximate the spike correlation coefficient in terms of moments of the associated exit time problem, and contrast the results for Type I vs. Type II models and across the different timescales over which spike correlations can be assessed. We find that, on long timescales, Type I oscillators transfer correlations much more efficiently than Type II oscillators. On short timescales this trend reverses, with the relative efficiency switching at a timescale that depends on the mean and standard deviation of input currents. This switch occurs over timescales that could be exploited by downstream circuits

Motor Preparatory Activity in Posterior Parietal Cortex is Modulated by Subjective Absolute Value

For optimal response selection, the consequences associated with behavioral success or failure must be appraised. To determine how monetary consequences influence the neural representations of motor preparation, human brain activity was scanned with fMRI while subjects performed a complex spatial visuomotor task. At the beginning of each trial, reward context cues indicated the potential gain and loss imposed for correct or incorrect trial completion. FMRI-activity in canonical reward structures reflected the expected value related to the context. In contrast, motor preparatory activity in posterior parietal and premotor cortex peaked in high “absolute value” (high gain or loss) conditions: being highest for large gains in subjects who believed they performed well while being highest for large losses in those who believed they performed poorly. These results suggest that the neural activity preceding goal-directed actions incorporates the absolute value of that action, predicated upon subjective, rather than objective, estimates of one's performance

The Impact of International Legal Rules in Facilitating the Public's Access to Medicines in South Africa

This paper explores the role played by international legal treaties, conventions and agreements that are binding on South Africa, in promoting the public’s access to medicines. In greater detail the impact that the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) of 1994, the Convention on Biological Diversity of 1992, and the United States of America’s Bahy-Dole Act of 1980 have had in the development of South Africa intellectual property (IP) law is examined. In addition, a question regarding whether such international legal instruments have positively impacted the public’s access to medicines is considered. The paper concludes that compliance with international IP law rules is not a silver bullet that will solve South Africa’s challenges relating to access to medicines. The protection of the public’s right to access to medicines in South Africa is strongly dependent on the government’s political will of ensuring that IP law is implemented to serve public good and public and private pharmaceutical patent holders are held accountable regarding the socially-responsible utilization of their IP

Financing HIV Programming: How Much Should Low- And Middle-Income Countries and their Donors Pay?

Global HIV control funding falls short of need. To maximize health outcomes, it is critical that national governments sustain reasonable commitments, and that international donor assistance be distributed according to country needs and funding gaps. We develop a country classification framework in terms of actual versus expected national domestic funding, considering resource needs and donor financing. With UNAIDS and World Bank data, we examine domestic and donor HIV program funding in relation to need in 84 low- and middle-income countries. We estimate expected domestic contributions per person living with HIV (PLWH) as a function of per capita income, relative size of the health sector, and per capita foreign debt service. Countries are categorized according to levels of actual versus expected domestic contributions, and resource gap. Compared to national resource needs (UNAIDS Investment Framework), we identify imbalances among countries in actual versus expected domestic and donor contributions: 17 countries, with relatively high HIV prevalence and GNI per capita, have domestic funding below expected (median per PLWH $143 and$376, respectively), yet total available funding including from donors would exceed the need ($368 and$305, respectively) if domestic contribution equaled expected. Conversely, 27 countries have actual domestic funding above the expected (medians $294 and$149) but total (domestic+donor) funding does not meet estimated need ($685 and$1,173). Across the 84 countries, in 2009, estimated resource need totaled $10.3 billion, actual domestic contributions$5.1 billion and actual donor contributions $3.7 billion. If domestic contributions would increase to the expected level in countries where the actual was below expected, total domestic contributions would increase to$7.4 billion, turning a funding gap of $1.5 billion into a surplus of$0.8 billion. Even with imperfect funding and resource-need data, the proposed country classification could help improve coherence and efficiency in domestic and international allocations

An associative memory of Hodgkin-Huxley neuron networks with Willshaw-type synaptic couplings

An associative memory has been discussed of neural networks consisting of spiking N (=100) Hodgkin-Huxley (HH) neurons with time-delayed couplings, which memorize P patterns in their synaptic weights. In addition to excitatory synapses whose strengths are modified after the Willshaw-type learning rule with the 0/1 code for quiescent/active states, the network includes uniform inhibitory synapses which are introduced to reduce cross-talk noises. Our simulations of the HH neuron network for the noise-free state have shown to yield a fairly good performance with the storage capacity of $\alpha_c = P_{\rm max}/N \sim 0.4 - 2.4$ for the low neuron activity of $f \sim 0.04-0.10$. This storage capacity of our temporal-code network is comparable to that of the rate-code model with the Willshaw-type synapses. Our HH neuron network is realized not to be vulnerable to the distribution of time delays in couplings. The variability of interspace interval (ISI) of output spike trains in the process of retrieving stored patterns is also discussed.Comment: 15 pages, 3 figures, changed Titl

Stochastic Resonance of Ensemble Neurons for Transient Spike Trains: A Wavelet Analysis

By using the wavelet transformation (WT), we have analyzed the response of an ensemble of $N$ (=1, 10, 100 and 500) Hodgkin-Huxley (HH) neurons to {\it transient} $M$-pulse spike trains ($M=1-3$) with independent Gaussian noises. The cross-correlation between the input and output signals is expressed in terms of the WT expansion coefficients. The signal-to-noise ratio (SNR) is evaluated by using the {\it denoising} method within the WT, by which the noise contribution is extracted from output signals. Although the response of a single (N=1) neuron to sub-threshold transient signals with noises is quite unreliable, the transmission fidelity assessed by the cross-correlation and SNR is shown to be much improved by increasing the value of $N$: a population of neurons play an indispensable role in the stochastic resonance (SR) for transient spike inputs. It is also shown that in a large-scale ensemble, the transmission fidelity for supra-threshold transient spikes is not significantly degraded by a weak noise which is responsible to SR for sub-threshold inputs.Comment: 20 pages, 4 figure

Can Monkeys Choose Optimally When Faced with Noisy Stimuli and Unequal Rewards?

We review the leaky competing accumulator model for two-alternative forced-choice decisions with cued responses, and propose extensions to account for the influence of unequal rewards. Assuming that stimulus information is integrated until the cue to respond arrives and that firing rates of stimulus-selective neurons remain well within physiological bounds, the model reduces to an Ornstein-Uhlenbeck (OU) process that yields explicit expressions for the psychometric function that describes accuracy. From these we compute strategies that optimize the rewards expected over blocks of trials administered with mixed difficulty and reward contingencies. The psychometric function is characterized by two parameters: its midpoint slope, which quantifies a subject's ability to extract signal from noise, and its shift, which measures the bias applied to account for unequal rewards. We fit these to data from two monkeys performing the moving dots task with mixed coherences and reward schedules. We find that their behaviors averaged over multiple sessions are close to optimal, with shifts erring in the direction of smaller penalties. We propose two methods for biasing the OU process to produce such shifts

A New Perceptual Bias Reveals Suboptimal Population Decoding of Sensory Responses

Several studies have reported optimal population decoding of sensory responses in two-alternative visual discrimination tasks. Such decoding involves integrating noisy neural responses into a more reliable representation of the likelihood that the stimuli under consideration evoked the observed responses. Importantly, an ideal observer must be able to evaluate likelihood with high precision and only consider the likelihood of the two relevant stimuli involved in the discrimination task. We report a new perceptual bias suggesting that observers read out the likelihood representation with remarkably low precision when discriminating grating spatial frequencies. Using spectrally filtered noise, we induced an asymmetry in the likelihood function of spatial frequency. This manipulation mainly affects the likelihood of spatial frequencies that are irrelevant to the task at hand. Nevertheless, we find a significant shift in perceived grating frequency, indicating that observers evaluate likelihoods of a broad range of irrelevant frequencies and discard prior knowledge of stimulus alternatives when performing two-alternative discrimination

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA