Anteo Smerieri

Optimization of an organic memristor as an adaptive memory element

The combination of memory and signal handling characteristics of a memristor makes it a promising candidate for adaptive bioinspired information processing systems. This poses stringent requirements on the basic device, such as stability and reproducibility over a large number of training/learning cycles, and a large anisotropy in the fundamental control material parameter, in our case the electrical conductivity. In this work we report results on the improved performance of electrochemically controlled polymeric memristors, where optimization of a conducting polymer (polyaniline) in the active channel and better environmental control of fabrication methods led to a large increase both in the absolute values of the conductivity in the partially oxydized state of polyaniline and of the on-off conductivity ratio. These improvements are crucial for the application of the organic memristor to adaptive complex signal handling networks.

Polymeric electrochemical element for adaptive networks: Pulse mode

An electrochemically controlled polymeric heterojunction working as a memristor, i.e., having memory properties, was investigated in pulse mode, mimicking synaptic behavior of signal transmission in biological systems. Influence of parameters such as pulse duration, interval between pulses, and value of potential base level was analyzed. Learning capabilities were shown to be reversible and repeatable for both potentiation and inhibition of signal transmission. The adaptive behavior of the element was investigated and was shown to be more efficient than the dc mode.

Modeling and simulating the adaptive electrical properties of stochastic polymeric 3D networks

Memristors are passive two-terminal circuit elements that combine resistance and memory. Although in theory memristors are a very promising approach to fabricate hardware with adaptive properties, there are only very few implementations able to show their basic properties. We recently developed stochastic polymeric matrices with a functionality that evidences the formation of self-assembled three-dimensional (3D) networks of memristors. We demonstrated that those networks show the typical hysteretic behavior observed in the ‘one input-one output’ memristive configuration. Interestingly, using different protocols to electrically stimulate the networks, we also observed that their adaptive properties are similar to those present in the nervous system. Here, we model and simulate the electrical properties of these selfassembled polymeric networks of memristors, the topology of which is defined stochastically. First, we show that the model recreates the hysteretic behavior observed in the real experiments. Second, we demonstrate that the networks modeled indeed have a 3D instead of a planar functionality. Finally, we show that the adaptive properties of the networks depend on their connectivity pattern. Our model was able to replicate fundamental qualitative behavior of the real organic 3D memristor networks; yet, through the simulations, we also explored other interesting properties, such as the relation between connectivity patterns and adaptive properties. Our model and simulations represent an interesting tool to understand the very complex behavior of self-assembled memristor networks, which can finally help to predict and formulate hypotheses for future experiments.

Origin of current oscillations in a polymeric electrochemically controlled element

We present a model for describing electrical properties of an electrochemically controlled heterojunction between a conducting polymer (polyaniline) and a solid electrolyte (lithium salt doped polyethylene oxide). In particular, the difference in the kinetics of the conductivity variation for different polarities of the bias voltage and appearance of current oscillations at constant applied voltage are considered. The results of our simulation are in good agreement with published experimental data.

Decision time, slow inhibition, and theta rhythm.

In this paper, we examine decision making in a spiking neuronal network and show that longer time constants for the inhibitory neurons can decrease the reaction times and produce theta rhythm. We analyze the mechanism and find that the spontaneous firing rate before the decision cues are applied can drift, and thereby influence the speed of the reaction time when the decision cues are applied. The drift of the firing rate in the population that will win the competition is larger if the time constant of the inhibitory interneurons is increased from 10 to 33 ms, and even larger if there are two populations of inhibitory neurons with time constants of 10 and 100 ms. Of considerable interest is that the decision that will be made can be influenced by the noise-influenced drift of the spontaneous firing rate over many seconds before the decision cues are applied. The theta rhythm associated with the longer time constant networks mirrors the greater integration in the firing rate drift produced by the recurrent connections over long time periods in the networks with slow inhibition. The mechanism for the effect of slow waves in the theta and delta range on decision times is suggested to be increased neuronal spiking produced by depolarization of the membrane potential on the positive part of the slow waves when the neurons membrane potential is close to the firing threshold.

Weber’s law implies neural discharge more regular than a Poisson process

Weber’s law is one of the basic laws in psychophysics, but the link between this psychophysical behavior and the neuronal response has not yet been established. In this paper, we carried out an analysis on the spike train statistics when Weber’s law holds, and found that the efferent spike train of a single neuron is less variable than a Poisson process. For population neurons, Weber’s law is satisfied only when the population size is small (< 10 neurons). However, if the population neurons share a weak correlation in their discharges and individual neuronal spike train is more regular than a Poisson process, Weber’s law is true without any restriction on the population size. Biased competition attractor network also demonstrates that the coefficient of variation of interspike interval in the winning pool should be less than one for the validity of Weber’s law. Our work links Weber’s law with neural firing property quantitatively, shedding light on the relation between psychophysical behavior and neuronal responses.

Adaptive Properties of Stochastic Memristor Networks: A Computational Study

Abstract A ‘memristor’ is a passive two-terminal circuit element the electric resistance of which depends on the history of the charge that has passed through it. We implemented a platform to simulate adaptive properties of stochastic memristor networks. We showed that such networks follow a stable behavior that diverges from its initial state depending on the history of stimulation. Additionally, we observed that the connectivity patterns of the networks influence their adaptive properties. These results confirm the adaptive properties of statistical memristor networks and suggest that they can be potentially used as complex and self-assembled ‘learning machines’.