Memristors are passive two-terminal circuit elements that combine resistance and memory. Although... more 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 showthe 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.
A 'memristor'is a passive two-terminal circuit element the electric resistance of which depends o... more 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.
Abstract: Reservoir Computing is a novel computing paradigm which uses a nonlinear recurrent dyna... more Abstract: Reservoir Computing is a novel computing paradigm which uses a nonlinear recurrent dynamical system to carry out information processing. Recent electronic and optoelectronic Reservoir Computers based on an architecture with a single nonlinear node and a delay loop have shown performance on standardized tasks comparable to state-of-the-art digital implementations. Here we report an all-optical implementation of a Reservoir Computer, made of off-the-shelf components for optical telecommunications.
Abstract: Reservoir computing is a new, powerful and flexible machine learning technique that is ... more Abstract: Reservoir computing is a new, powerful and flexible machine learning technique that is easily implemented in hardware. Recently, by using a time-multiplexed architecture, hardware reservoir computers have reached performance comparable to digital implementations. Operating speeds allowing for real time information operation have been reached using optoelectronic systems. At present the main performance bottleneck is the readout layer which uses slow, digital postprocessing.
Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processi... more Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an optoelectronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.
An important endeavor in modern materials science is the synthesis of adaptive assemblies with in... more An important endeavor in modern materials science is the synthesis of adaptive assemblies with information processing capabilities similar to those of biological neural systems. Recent developments concern materials functionally similar to the memristor, a notional electrical circuit whose conductivity is dependent on past activity. This feature is analogous to synaptic plasticity: the ability of neurons to modify their synaptic connections as a result of accumulated experience—the basis of learning and the formation of memory. In this paper, we present the first evidence that memristive device-based organic materials show adaptive behavior similar to biological cognitive systems, using learning in the feeding neural network of the pond snail, Lymnaea stagnalis, as a specific biological reference. The synthetic reproduction of synaptic plasticity reported here can create new paradigms for novel comput- ing systems and give impetus to the search for bio-inspired nanoscale molecular architectures capable of learning and decision making.
In this paper, we examine decision making in a spiking neuronal network and show that longer time... more 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 neuron's membrane potential is close to the firing threshold.
The organic memristor is an element which varies its conductance according to its previous involv... more The organic memristor is an element which varies its conductance according to its previous involvement in the signal transfer processes, i.e. it combines conductance with memory properties. The first part of the work is dedicated to the consideration of its basic principles and fundamental properties. After this, we present the architecture of the organization of model networks, demonstrating the capabilities of supervised and unsupervised learning. Finally, we discuss the possible ways, alternative to the existing lithography-based technologies, that would result in the fabrication of statistically organized networks of such elements, mimicking learning in biological systems.
Memristors are passive two-terminal circuit elements that combine resistance and memory. Although... more 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 showthe 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.
A 'memristor'is a passive two-terminal circuit element the electric resistance of which depends o... more 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.
Abstract: Reservoir Computing is a novel computing paradigm which uses a nonlinear recurrent dyna... more Abstract: Reservoir Computing is a novel computing paradigm which uses a nonlinear recurrent dynamical system to carry out information processing. Recent electronic and optoelectronic Reservoir Computers based on an architecture with a single nonlinear node and a delay loop have shown performance on standardized tasks comparable to state-of-the-art digital implementations. Here we report an all-optical implementation of a Reservoir Computer, made of off-the-shelf components for optical telecommunications.
Abstract: Reservoir computing is a new, powerful and flexible machine learning technique that is ... more Abstract: Reservoir computing is a new, powerful and flexible machine learning technique that is easily implemented in hardware. Recently, by using a time-multiplexed architecture, hardware reservoir computers have reached performance comparable to digital implementations. Operating speeds allowing for real time information operation have been reached using optoelectronic systems. At present the main performance bottleneck is the readout layer which uses slow, digital postprocessing.
Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processi... more Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an optoelectronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.
An important endeavor in modern materials science is the synthesis of adaptive assemblies with in... more An important endeavor in modern materials science is the synthesis of adaptive assemblies with information processing capabilities similar to those of biological neural systems. Recent developments concern materials functionally similar to the memristor, a notional electrical circuit whose conductivity is dependent on past activity. This feature is analogous to synaptic plasticity: the ability of neurons to modify their synaptic connections as a result of accumulated experience—the basis of learning and the formation of memory. In this paper, we present the first evidence that memristive device-based organic materials show adaptive behavior similar to biological cognitive systems, using learning in the feeding neural network of the pond snail, Lymnaea stagnalis, as a specific biological reference. The synthetic reproduction of synaptic plasticity reported here can create new paradigms for novel comput- ing systems and give impetus to the search for bio-inspired nanoscale molecular architectures capable of learning and decision making.
In this paper, we examine decision making in a spiking neuronal network and show that longer time... more 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 neuron's membrane potential is close to the firing threshold.
The organic memristor is an element which varies its conductance according to its previous involv... more The organic memristor is an element which varies its conductance according to its previous involvement in the signal transfer processes, i.e. it combines conductance with memory properties. The first part of the work is dedicated to the consideration of its basic principles and fundamental properties. After this, we present the architecture of the organization of model networks, demonstrating the capabilities of supervised and unsupervised learning. Finally, we discuss the possible ways, alternative to the existing lithography-based technologies, that would result in the fabrication of statistically organized networks of such elements, mimicking learning in biological systems.
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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.
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.