Neural computation
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Many decoding algorithms for brain machine interfaces' (BMIs) estimate hand movement from binned spike rates, which do not fully exploit the resolution contained in spike timing and may exclude rich neural dynamics from the modeling. More recently, an adaptive filtering method based on a Bayesian approach to reconstruct the neural state from the observed spike times has been proposed. However, it assumes and propagates a gaussian distributed state posterior density, which in general is too restrictive. ⋯ However, this added complexity is translated into higher performance with real data. To deal with the intrinsic spike randomness in online modeling, several synthetic spike trains are generated from the intensity function estimated from the neurons and utilized as extra model inputs in an attempt to decrease the variance in the kinematic predictions. The performance of the sequential Monte Carlo estimation methodology augmented with this synthetic spike input provides improved reconstruction, which raises interesting questions and helps explain the overall modeling requirements better.
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Previous theoretical work has shown that a single-layer neural network can implement the optimal decision process for simple, two-alternative forced-choice (2AFC) tasks. However, it is likely that the mammalian brain comprises multilayer networks, raising the question of whether and how optimal performance can be approximated in such an architecture. Here, we present theoretical work suggesting that the noradrenergic nucleus locus coeruleus (LC) may help optimize 2AFC decision making in the brain. ⋯ Here, we find that gain transients modeling the LC phasic response yield an improvement in reward rate of 12% to 24%. Furthermore, we show that the timing characteristics of these gain transients agree with observations concerning LC firing patterns reported in recent experimental studies. This provides converging evidence for the hypothesis that the LC optimizes processes underlying 2AFC decision making in multilayer networks.
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To perform automatic, unconscious inference, the human brain must solve the binding problem by correctly grouping properties with objects. Temporal binding models like SHRUTI already suggest much of how this might be done in a connectionist and localist way by using temporal synchrony. ⋯ These extensions model the human ability to both perform unification and handle multiple instantiations of logical terms. To verify our model's feasibility, we simulate it with a computer system modeling simple, neuron-like computations.
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Letter Comparative Study
Bayesian model comparison in nonlinear BOLD fMRI hemodynamics.
Nonlinear hemodynamic models express the BOLD (blood oxygenation level dependent) signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for both the neural activity and the hemodynamics. ⋯ Using a split-half resampling procedure (Strother, Anderson, & Hansen, 2002), we compare the generalization abilities of the models as well as their reproducibility, for both synthetic and real data, recorded from two different visual stimulation paradigms. The results show that the simple model is the better one for these data.
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Accumulating evidence suggests that auditory cortical neurons exhibit widespread-onset responses and restricted sustained responses to sound stimuli. When a sound stimulus is presented to a subject, the auditory cortex first responds with transient discharges across a relatively large population of neurons, showing widespread-onset responses. As time passes, the activation becomes restricted to a small population of neurons that are preferentially driven by the stimulus, showing restricted sustained responses. ⋯ The widespread-onset responses contributed to the accelerating reaction time of neurons to sensory stimulation. Lateral interaction among dynamic cell assemblies was essential for maintaining ongoing membrane potentials near thresholds for action potential generation, thereby accelerating reaction time to subsequent sensory input as well. We suggest that the widespread-onset neuronal responses and the ongoing subthreshold cortical state, for which the coordination of lateral synaptic interaction among dissimilar cell assemblies is essential, may work together in order for the auditory cortex to quickly detect the sudden occurrence of sounds from the external environment.