Here we derive measures quantifying the information loss of a synaptic signal due to the presence of neuronal noise sources, as it electrotonically propagates along a weakly-active dendrite. We model the dendrite as a...
Here we derive measures quantifying the information loss of a synaptic signal due to the presence of neuronal noise sources, as it electrotonically propagates along a weakly-active dendrite. We model the dendrite as an infinite linear cable, with noise sources distributed along its length. The noise sources we consider are thermal noise, channel noise arising from the stochastic nature of voltage-dependent ionic channels (K+ and Na+) and synaptic noise due to spontaneous background activity. We assess the efficacy of information transfer using a signal detection paradigm where the objective is to detect the presence/absence of a presynaptic spike from the post-synaptic membrane voltage. This allows us to analytically assess the role of each of these noise sources in information transfer. For our choice of parameters, we find that the synaptic noise is the dominant noise source which limits the maximum length over which information be reliably transmitted.
The complexity of analog VLSI systems is often limited by the number of pins on a chip rather than by the die area. Currently, many analog parameters and biases are stored off chip. Moving parameter storage on chip co...
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The complexity of analog VLSI systems is often limited by the number of pins on a chip rather than by the die area. Currently, many analog parameters and biases are stored off chip. Moving parameter storage on chip could save pins and allow us to create complex programmable analog systems. In this paper, we present a design for an on-chip non-volatile analog memory cell that can be configured in addressable arrays and programmed easily. We use floating-gate MOS transistors to store charge, and we use the processes of tunneling and hot-electron injection to program values. We achieve greater than 13-bit precision with no crosstalk between memory cells.
In this paper we describe an analog VLSI circuit, fabricated using a standard 2 mu m, n-well, BiCMOS process, which utilizes floating-gate structures for non-volatile, on-chip, analog parameter storage. This circuit i...
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In this paper we describe an analog VLSI circuit, fabricated using a standard 2 mu m, n-well, BiCMOS process, which utilizes floating-gate structures for non-volatile, on-chip, analog parameter storage. This circuit is designed to operate in the context of a hardware model of the primate oculomotor system and performs visually-guided, saccadic adaptation. The chip contains a one-dimensional array of photoreceptors and floating-gate circuits which are used to map retinal positions to motor output commands. The system's functionality is demonstrated by training the chip with several different mapping functions using a supervised-learning technique.
Neurons and their networks underlie our perceptions, actions and memories. The latest work on information processing and storage at the single-cell level reveals previously unimagined complexity and dynamism.
Neurons and their networks underlie our perceptions, actions and memories. The latest work on information processing and storage at the single-cell level reveals previously unimagined complexity and dynamism.
Shunting inhibition, a conductance increase with a reversal potential close to the resting potential of the cell, has been shown to have a divisive effect on subthreshold excitatory postsynaptic potential amplitudes. ...
Shunting inhibition, a conductance increase with a reversal potential close to the resting potential of the cell, has been shown to have a divisive effect on subthreshold excitatory postsynaptic potential amplitudes. It has therefore been assumed to have the same divisive effect on firing rates. We show that shunting inhibition actually has a subtractive effect on the firing rate in most circumstances. Averaged over several interspike intervals, the spiking mechanism effectively clamps the somatic membrane potential to a value significantly above the resting potential, so that the current through the shunting conductance is approximately independent of the firing rate. This leads to a subtractive rather than a divisive effect. In addition, at distal synapses, shunting inhibition will also have an approximately subtractive effect if the excitatory conductance is not small compared to the inhibitory conductance. Therefore regulating a cell's passive membrane conductance-for instance, via massive feedback-is not an adequate mechanism for normalizing or scaling its output.
A hint is any piece of side information about the target function to be learned. We consider the monotonicity hint, which states that the function to be learned is monotonic in some or all of the input variables. The ...
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ISBN:
(纸本)0262100657
A hint is any piece of side information about the target function to be learned. We consider the monotonicity hint, which states that the function to be learned is monotonic in some or all of the input variables. The application of monotonicity hints is demonstrated on two real-world problems- a credit card application task, and a problem in medical diagnosis. A measure of the monotonicity error of a candidate function is defined and an objective function for the enforcement of monotonicity is derived from Bayesian principles. We report experimental results which show that using monotonicity hints leads to a statistically significant improvement in performance on both problems.
Parametric feedback control of chaos relies on detailed knowledge of the locations of unstable periodic orbits. We show that unstable periodic orbits of dynamical systems with unknown locations but known periodicity ...
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Parametric feedback control of chaos relies on detailed knowledge of the locations of unstable periodic orbits. We show that unstable periodic orbits of dynamical systems with unknown locations but known periodicity τ can be stabilized by an oscillating feedback term proportional to ɛt (x→t−x→t−τ), where x→t is the location of the trajectory at time t and ɛt is periodic in time. Periodic feedback overcomes the limitations of Giona’s theorem [Nonlinearity 4, 911 (1991)], which states that constant feedback (i.e., a time-independent ɛ) can stabilize an unstable periodic orbit only if the stability matrix has no positive eigenvalues greater than unity. As an application of oscillating feedback, we use it to stabilize the memory patterns in an associative memory (Hopfield [Proc. Natl. Acad. Sci. USA 79, 2554 (1982); 81, 3088 (1984)]) network, thereby enhancing the total capacity of the memory device. We extend our method to high-dimensional systems described by differential equations; in this framework, it is possible to stabilize the spatiotemporal chaos generated by the Kuramoto-Sivashinsky equation [G. J. Sivashinsky and D. M. Michelson, Prog. Theor. Phys. 63, 2122 (1980)].
Flies are capable of rapidly detecting and integrating visual motion information in behaviorly-relevant ways. The first stage of visual motion processing in flies is a retinotopic array of functional units known as el...
Flies are capable of rapidly detecting and integrating visual motion information in behaviorly-relevant ways. The first stage of visual motion processing in flies is a retinotopic array of functional units known as elementary motion detectors (EMDs). Several decades ago, Reichardt and colleagues developed a correlation-based model of motion detection that described the behavior of these neural circuits. We have implemented a variant of this model in a 2.0-µm analog CMOS VLSI process. The result is a low-power, continuous-time analog circuit with integrated photoreceptors that responds to motion in real time. The responses of the circuit to drifting sinusoidal gratings qualitatively resemble the temporal frequency response, spatial frequency response, and direction selectivity of motion-sensitive neurons observed in insects. In addition to its possible engineering applications, the circuit could potentially be used as a building block for constructing hardware models of higher-level insect motion integration.
Here we analyze synaptic transmission from an information-theoretic perspective. We derive closed-form expressions for the lower-bounds on the capacity of a simple model of a cortical synapse under two explicit coding...
Here we analyze synaptic transmission from an information-theoretic perspective. We derive closed-form expressions for the lower-bounds on the capacity of a simple model of a cortical synapse under two explicit coding paradigms. Under the "signal estimation" paradigm, we assume the signal to be encoded in the mean firing rate of a Poisson neuron. The performance of an optimal linear estimator of the signal then provides a lower bound on the capacity for signal estimation. Under the "signal detection" paradigm, the presence or absence of the signal has to be detected. Performance of the optimal spike detector allows us to compute a lower bound on the capacity for signal detection. We find that single synapses (for empirically measured parameter values) transmit information poorly but significant improvement can be achieved with a small amount of redundancy.
Monotonicity is a constraint which arises in many application domains. We present a machine learning model, the monotonic network, for which monotonicity can be enforced exactly, i.e., by virtue of functional form. A ...
Monotonicity is a constraint which arises in many application domains. We present a machine learning model, the monotonic network, for which monotonicity can be enforced exactly, i.e., by virtue of functional form. A straightforward method for implementing and training a monotonic network is described. Monotonic networks are proven to be universal approximators of continuous, differentiable monotonic functions. We apply monotonic networks to a real-world task in corporate bond rating prediction and compare them to other approaches.
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