12,499 research outputs found
Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control
It is widely accepted that the complex dynamics characteristic of recurrent
neural circuits contributes in a fundamental manner to brain function. Progress
has been slow in understanding and exploiting the computational power of
recurrent dynamics for two main reasons: nonlinear recurrent networks often
exhibit chaotic behavior and most known learning rules do not work in robust
fashion in recurrent networks. Here we address both these problems by
demonstrating how random recurrent networks (RRN) that initially exhibit
chaotic dynamics can be tuned through a supervised learning rule to generate
locally stable neural patterns of activity that are both complex and robust to
noise. The outcome is a novel neural network regime that exhibits both
transiently stable and chaotic trajectories. We further show that the recurrent
learning rule dramatically increases the ability of RRNs to generate complex
spatiotemporal motor patterns, and accounts for recent experimental data
showing a decrease in neural variability in response to stimulus onset
Area/latency optimized early output asynchronous full adders and relative-timed ripple carry adders
This article presents two area/latency optimized gate level asynchronous full
adder designs which correspond to early output logic. The proposed full adders
are constructed using the delay-insensitive dual-rail code and adhere to the
four-phase return-to-zero handshaking. For an asynchronous ripple carry adder
(RCA) constructed using the proposed early output full adders, the
relative-timing assumption becomes necessary and the inherent advantages of the
relative-timed RCA are: (1) computation with valid inputs, i.e., forward
latency is data-dependent, and (2) computation with spacer inputs involves a
bare minimum constant reverse latency of just one full adder delay, thus
resulting in the optimal cycle time. With respect to different 32-bit RCA
implementations, and in comparison with the optimized strong-indication,
weak-indication, and early output full adder designs, one of the proposed early
output full adders achieves respective reductions in latency by 67.8, 12.3 and
6.1 %, while the other proposed early output full adder achieves corresponding
reductions in area by 32.6, 24.6 and 6.9 %, with practically no power penalty.
Further, the proposed early output full adders based asynchronous RCAs enable
minimum reductions in cycle time by 83.4, 15, and 8.8 % when considering
carry-propagation over the entire RCA width of 32-bits, and maximum reductions
in cycle time by 97.5, 27.4, and 22.4 % for the consideration of a typical
carry chain length of 4 full adder stages, when compared to the least of the
cycle time estimates of various strong-indication, weak-indication, and early
output asynchronous RCAs of similar size. All the asynchronous full adders and
RCAs were realized using standard cells in a semi-custom design fashion based
on a 32/28 nm CMOS process technology
Direct Observation of Martensitic Phase-Transformation Dynamics in Iron by 4D Single-Pulse Electron Microscopy
The in situ martensitic phase transformation of iron, a complex solid-state transition involving collective atomic displacement and interface movement, is studied in real time by means of four-dimensional (4D) electron microscopy. The iron nanofilm specimen is heated at a maximum rate of ∼10^(11) K/s by a single heating pulse, and the evolution of the phase transformation from body-centered cubic to face-centered cubic crystal structure is followed by means of single-pulse, selected-area diffraction and real-space imaging. Two distinct components are revealed in the evolution of the crystal structure. The first, on the nanosecond time scale, is a direct martensitic transformation, which proceeds in regions heated into the temperature range of stability of the fcc phase, 1185−1667 K. The second, on the microsecond time scale, represents an indirect process for the hottest central zone of laser heating, where the temperature is initially above 1667 K and cooling is the rate-determining step. The mechanism of the direct transformation involves two steps, that of (barrier-crossing) nucleation on the reported nanosecond time scale, followed by a rapid grain growth typically in ∼100 ps for 10 nm crystallites
Linear and Nonlinear Encoding Properties of an Identified Mechanoreceptor on the Fly wing Measured with Mechanical Noise Stimuli
The wing blades of most flies contain a small set of distal campaniform sensilla, mechanoreceptors that respond to deformations of the cuticle. This paper describes a method of analysis based upon mechanical noise stimuli which is used to quantify the encoding properties of one of these sensilla (the d-HCV cell) on the wing of the blowfly Calliphora vomitoria (L.). The neurone is modelled as two components, a linear filter that accounts for the frequency response and phase characteristics of the cell, followed by a static nonlinearity that limits the spike discharge to a narrow portion of the stimulus cycle. The model is successful in predicting the response of campaniform neurones to arbitrary stimuli, and provides a convenient method for quantifying the encoding properties of the sensilla.
The d-HCV neurone is only broadly frequency tuned, but its maximal response near 150 Hz corresponds to the wingbeat frequency of Calliphora. In the range of frequencies likely to be encountered during flight, the d-HCV neurone fires a single phase-locked action potential for each stimulus cycle. The phase lag of the cell decreases linearly with increasing frequency such that the absolute delay between stimulus and response remains nearly constant. Thus, during flight the neurone is capable of firing one precisely timed action potential during each wingbeat, and might be used to modulate motor activity that requires afferent input on a cycle-by-cycle basis
Dopaminergic and Non-Dopaminergic Value Systems in Conditioning and Outcome-Specific Revaluation
Animals are motivated to choose environmental options that can best satisfy current needs. To explain such choices, this paper introduces the MOTIVATOR (Matching Objects To Internal Values Triggers Option Revaluations) neural model. MOTIVATOR describes cognitiveemotional interactions between higher-order sensory cortices and an evaluative neuraxis composed of the hypothalamus, amygdala, and orbitofrontal cortex. Given a conditioned stimulus (CS), the model amygdala and lateral hypothalamus interact to calculate the expected current value of the subjective outcome that the CS predicts, constrained by the current state of deprivation or satiation. The amygdala relays the expected value information to orbitofrontal cells that receive inputs from anterior inferotemporal cells, and medial orbitofrontal cells that receive inputs from rhinal cortex. The activations of these orbitofrontal cells code the subjective values of objects. These values guide behavioral choices. The model basal ganglia detect errors in CS-specific predictions of the value and timing of rewards. Excitatory inputs from the pedunculopontine nucleus interact with timed inhibitory inputs from model striosomes in the ventral striatum to regulate dopamine burst and dip responses from cells in the substantia nigra pars compacta and ventral tegmental area. Learning in cortical and striatal regions is strongly modulated by dopamine. The model is used to address tasks that examine food-specific satiety, Pavlovian conditioning, reinforcer devaluation, and simultaneous visual discrimination. Model simulations successfully reproduce discharge dynamics of known cell types, including signals that predict saccadic reaction times and CS-dependent changes in systolic blood pressure.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Institutes of Health (R29-DC02952, R01-DC007683); National Science Foundation (IIS-97-20333, SBE-0354378); Office of Naval Research (N00014-01-1-0624
Correlation entropy of synaptic input-output dynamics
The responses of synapses in the neocortex show highly stochastic and
nonlinear behavior. The microscopic dynamics underlying this behavior, and its
computational consequences during natural patterns of synaptic input, are not
explained by conventional macroscopic models of deterministic ensemble mean
dynamics. Here, we introduce the correlation entropy of the synaptic
input-output map as a measure of synaptic reliability which explicitly includes
the microscopic dynamics. Applying this to experimental data, we find that
cortical synapses show a low-dimensional chaos driven by the natural input
pattern.Comment: 7 pages, 6 Figures (7 figure files
Application of graphics processing units to search pipelines for gravitational waves from coalescing binaries of compact objects
We report a novel application of a graphics processing unit (GPU) for the purpose of accelerating the search pipelines for gravitational waves from coalescing binaries of compact objects. A speed-up of 16-fold in total has been achieved with an NVIDIA GeForce 8800 Ultra GPU card compared with one core of a 2.5 GHz Intel Q9300 central processing unit (CPU). We show that substantial improvements are possible and discuss the reduction in CPU count required for the detection of inspiral sources afforded by the use of GPUs
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