Spike Timing Dependent Plasticity Learning Research Articles

Modulating STDP Balance Impacts the Dendritic Mosaic.

The ability for cortical neurons to adapt their input/output characteristics and information processing capabilities ultimately relies on the interplay between synaptic plasticity, synapse location, and the nonlinear properties of the dendrite. Collectively, they shape both the strengths and spatial arrangements of convergent afferent inputs to neuronal dendrites. Recent experimental and theoretical studies support a clustered plasticity model, a view that synaptic plasticity promotes the formation of clusters or hotspots of synapses sharing similar properties. We have previously shown that spike timing-dependent plasticity (STDP) can lead to synaptic efficacies being arranged into spatially segregated clusters. This effectively partitions the dendritic tree into a tessellated imprint which we have called a dendritic mosaic. Here, using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and STDP learning, we investigated the impact of altered STDP balance on forming such a spatial organization. We show that cluster formation and extend depend on several factors, including the balance between potentiation and depression, the afferents' mean firing rate and crucially on the dendritic morphology. We find that STDP balance has an important role to play for this emergent mode of spatial organization since any imbalances lead to severe degradation- and in some case even destruction- of the mosaic. Our model suggests that, over a broad range of of STDP parameters, synaptic plasticity shapes the spatial arrangement of synapses, favoring the formation of clustered efficacy engrams.

Fast unsupervised learning for visual pattern recognition using spike timing dependent plasticity

Real-time learning needs algorithms operating in a fast speed comparable to human or animal, however this is a huge challenge in processing visual inputs. Research shows a biological brain can process complicated real-life recognition scenarios at milliseconds scale. Inspired by biological system, in this paper, we proposed a novel real-time learning method by combing the spike timing-based feed-forward spiking neural network (SNN) and the fast unsupervised spike timing dependent plasticity learning method with dynamic post-synaptic thresholds. Fast cross-validated experiments using MNIST database showed the high efficiency of the proposed method at an acceptable accuracy.

ASP: Learning to Forget With Adaptive Synaptic Plasticity in Spiking Neural Networks

A fundamental feature of learning in animals is the "ability to forget" that allows an organism to perceive, model and make decisions from disparate streams of information and adapt to changing environments. Against this backdrop, we present a novel unsupervised learning mechanism ASP (Adaptive Synaptic Plasticity) for improved recognition with Spiking Neural Networks (SNNs) for real time on-line learning in a dynamic environment. We incorporate an adaptive weight decay mechanism with the traditional Spike Timing Dependent Plasticity (STDP) learning to model adaptivity in SNNs. The leak rate of the synaptic weights is modulated based on the temporal correlation between the spiking patterns of the pre- and post-synaptic neurons. This mechanism helps in gradual forgetting of insignificant data while retaining significant, yet old, information. ASP, thus, maintains a balance between forgetting and immediate learning to construct a stable-plastic self-adaptive SNN for continuously changing inputs. We demonstrate that the proposed learning methodology addresses catastrophic forgetting while yielding significantly improved accuracy over the conventional STDP learning method for digit recognition applications. Additionally, we observe that the proposed learning model automatically encodes selective attention towards relevant features in the input data while eliminating the influence of background noise (or denoising) further improving the robustness of the ASP learning.

(Invited) Analog HfO2-RRAM Switches for Neural Networks

Resistive random access memories (RRAM) are one of the main constituents of the class of memristive technologies that are today considered very promising in semiconductor industry because of their high potential for several applications ranging from non-volatile memories to neuromorphic hardware. The latter application is particularly interesting, since bio-inspired electronic systems have the ability to treat ill-posed problems with higher efficiency than conventional computing paradigms. In this work, we focus on HfO2-based RRAM devices and we analyse their switching dynamics in order to reach neuromorphic requirements. We present analogue memristive behaviour in HfO2 RRAM, which allows realizing a simple version of spike timing dependent plasticity learning rule. Finally, the experimental data are used to simulate an unsupervised spiking neuromorphic network for pattern recognition suitable for real-time applications.

Memristor emulator with spike-timing-dependent-plasticity

A novel fully floating memristor circuit that accounts for the Spike Timing-Dependent Plasticity (STDP) mechanism is presented in this paper. This proposed circuit does not need any multiplier or extra circuit blocks to provide non-linear characteristics and transistors, which are operated in subthreshold region. We show that memristor circuit exhibits pinched-hysteresis loop in the V-I plane when driven by any sinusoidal voltage source. We demonstrate that the conductance change of the proposed floating memristor exhibits STDP behavior after the application of a pulse-pair train. Finally we present that the time constant of the STDP learning window can be controlled using only one parameter. Proposed memristor emulator circuit that accounts for STDP characteristics is compatible with VLSI systems.

(Invited) Analog Hfox-Rram Switches for Neural Networks

Memristive systems represent today a disruptive technology for the semiconductor industry with high potential for a wide range of applications ranging from non-volatile memories and non-volatile logic, to analog circuits, biomimetic devices, and neuromorphic computing paradigms. Among the general class of memristive systems, our work focus on oxide filamentary resistive random access memories (oxide RRAM) that base their operation on redox reactions and electrochemical phenomena that allow the formation and dissolution of conductive filaments, shorting the two electrodes of a metal/oxide/metal stack [1]. RRAMs have already demonstrated their great potential for non-volatile memory applications because of low power consumption, fast switching times, scalability down to nm scale [2] or atomic level, and CMOS compatibility. In parallel, significant efforts to redirect the device engineering towards neuromorphic computing [3] and neural networks have been devoted by a large community in the last years. Indeed, the RRAM technology can localize memory and computation in one single device, thus allowing a conceptual realization of non-Von Neumann paradigms that dramatically reduce the power-cost in computing architectures and data communication. This research finds its main application in the development of intelligent and adaptive systems towards the ‘Internet of Things’ concept. It is a highly interdisciplinary research field which merges, on one hand, materials development, nanodevice fabrication, modelling, electrical testing aimed at building artificial synapses with memristive devices. On the other hand, it studies the implementation of neuron functions into VLSI electronics that has to match memristive behavior and electrical features for a functional and physical integration of artificial neuronal and synaptic units. In our work we are focusing on switching dynamics in relation to material properties of HfOx-based RRAM devices [4-7] in order to reach the switching characteristics that fit biologically plausible behaviors. Here we present analog memristive behavior for use in two different approaches, named (i) 1 RRAM-1 synapse and (ii) 1T-1 RRAM–synapse for subthreshold CMOS circuits. In approach (i), a single RRAM device plays the role of a synapse by itself. The analog behavior allows realizing a Spike Timing Dependent-Plasticity (STDP) learning rule (Fig1a), which translates the timing of two superimposing spikes with specific shapes sent to the two device terminals into a causality relation among events [6-9]. The experimental STDP learning rule has been employed in a simulated neuromorphic network able to recognize vowels after an unsupervised training (Fig.1b). While conceptually simple and allowing for a highly compact design, the previous approach is very challenging in practice. The 1T-1 RRAM synapse for subthreshold CMOS circuits, despite being less compact, is more feasible and allows some biological plausibility even within a network utilizing short simply shaped spikes. The use of subthreshold transistor in the analog regime refers back to the original definition of neuromorphic system as given by Carver Mead [9] and is used to generate the biorealistic temporal dynamics of post synaptic spikes [10]. We will demonstrate that RRAM devices can be interfaced with such synaptic circuits including subthreshold transistors to obtain a non-volatile control of synaptic weights. [1] Brivio, G. Tallarida, E. Cianci and S. Spiga, Formation and disruption of conductive filaments in a HfO2/TiN structure, Nanotechnology 25, 385705 (2014) [2] J. Frascaroli , S. Brivio, F. F. Lupi , G. Seguini , L. Boarino , M. Perego, and S. Spiga , Resistive Switching in High-Density Nanodevices Fabricated by Block Copolymer Self-Assembly, ACS Nano 9, 2518 (2015) [3] G. Indiveri, S.-C. Liu, Memory and information processing in neuromorphic systems, In Proceedings of the IEEE, IEEE, volume 103, 2015. [4] J. Frascaroli, F. G. Volpe, S. Brivio, and S. Spiga, Effect of Al doping on the retention behavior of HfO2 resistive switching memories, Microelectronic Engineering 147, 104 (2015) [5] S. Brivio, J. Frascaroli, and S. Spiga, Role of metal-oxide interfaces in the multiple resistance switching regimes of Pt/HfO2/TiN devices, Applied Physics Letters 107, 023504 (2015) [6]E. Covi, S. Brivio, M. Fanciulli, and S. Spiga, Synaptic potentiation and depression in Al:HfO2-based memristor , Microelectronic Engineering 147, 41-44 (2015) [7] S. Brivio, E. Covi, A. Serb, T. Prodromakis, M. Fanciulli, and S. Spiga, Gradual set dynamics in HfO2-based memristor driven by sub-threshold voltage pulses, in Memristive Syst. MEMRISYS 2015 IEEE Int. Conf. On (2015), pp. 1–2. [8] E. Covi, S. Brivio, A. Serb, T. Prodromakis, M. Fanciulli, and S. Spiga, HfO2-based Memristors for Neuromorphic Applications, Proceedings of 2016 IEEE International Symposium on Circuits and Systems (ISCAS) [9] C. Mead, Analog VLSI and neural systems. Reading, MA: Addison-Wesley (1989) [10] Bartolozzi et al., Neural Computation 19 (2007) 2581. Figure 1

Modified STDP Triplet Rule Significantly Increases Neuron Training Stability in the Learning of Spatial Patterns

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules which are based firmly on biological evidence. STDP learning is capable of detecting spatiotemporal patterns highly obscured by noise. This feature appears attractive from the point of view of machine learning. In this paper three different additive STDP models of spike interactions were compared in respect to training performance when the neuron is exposed to a recurrent spatial pattern injected into Poisson noise. The STDP models compared were all-to-all interaction, nearest-neighbor interaction, and the nearest-neighbor triplet interaction. The parameters of the neuron model and STDP training rules were optimized for a range of spatial patterns of different sizes by the means of heuristic algorithm. The size of the pattern, that is, the number of synapses containing the pattern, was gradually decreased from what amounted to a relatively easy task down to a single synapse. Optimization was performed for each size of the pattern. The parameters were allowed to evolve freely. The triplet rule, in most cases, performed better by far than the other two rules, while the evolutionary algorithm immediately switched the polarity of the triplet update. The all-to-all rule achieved moderate results.

Slow feature analysis with spiking neurons and its application to audio stimuli.

Extracting invariant features in an unsupervised manner is crucial to perform complex computation such as object recognition, analyzing music or understanding speech. While various algorithms have been proposed to perform such a task, Slow Feature Analysis (SFA) uses time as a means of detecting those invariants, extracting the slowly time-varying components in the input signals. In this work, we address the question of how such an algorithm can be implemented by neurons, and apply it in the context of audio stimuli. We propose a projected gradient implementation of SFA that can be adapted to a Hebbian like learning rule dealing with biologically plausible neuron models. Furthermore, we show that a Spike-Timing Dependent Plasticity learning rule, shaped as a smoothed second derivative, implements SFA for spiking neurons. The theory is supported by numerical simulations, and to illustrate a simple use of SFA, we have applied it to auditory signals. We show that a single SFA neuron can learn to extract the tempo in sound recordings.

Modeling the interplay between structural plasticity and spike-timing-dependent plasticity

Structural Plasticity describes a form of long-term plasticity, in which the pruning and the creation of synapses lead to the formation of memories in the topology of a network of neurons. In contrast, classical learning rules such as spike-timing dependent plasticity (STDP) focus on changing the efficacy of synapses, for example by looking at the correlation of pre-and post-synaptic activity in spiking neural networks. Typically, prolonged correlated activity leads to a long-term potentiation of the synaptic weight, while anti-correlated activity depresses the weight. We propose a computational model that combines classical learning rules with structural changes in spiking neural network architectures that are based on observations on the morphological changes real biological synapses undergo during their live-cycle. Our model is based on the assumption that newly formed synapses are initially silent, due to their lack of AMPA receptors. In these synapses, only co-activation with other synapses can lead to postsynaptic potentials, and if this co-activation is not present for a critical period, the synapse degenerates again [1]. To study the interaction of structural plasticity and classical STDP learning rules, we simulated a highly recurrently connected spiking neural network and presented topological inputs to its neurons. We implemented the triplet STDP learning rule proposed by Pfister and Gerstner [2], and applied a structural plasticity rule where a critical period is opened whenever a synaptic weight is decreased below a certain threshold. If the weight does not manage to reach a set threshold by the end of the critical period, the synapse is pruned, and a new synapse is instantiated within the network; otherwise the synapse is maintained. This approach implies a homeostasis in the number of consolidated synapses in the network, while keeping the connectivity at a desired level of sparseness. We show in Figure ​Figure11 simulation results in which the input topology of the network is first learned using only STDP, and then, after activating structural plasticity, the structure of the connectivity matrix itself is adapted such that it reflects the input topology. Figure 1 Connectivity matrix from input to target neuron population with and without structural plasticity. Top: Randomly initialized connectivity, learned using triplet STDP. Bottom: Connectivity learned using structural plasticity and triplet-STDP. A major advantage of structural plasticity in artificial neural networks is given by the fact that it allows a drastic increase in performance given a finite number of synaptic resources. In addition to offering a promising approach for optimizing performance in software simulated networks, the model we propose optimizes the usage of resources in dedicated hardware neural network implementations that are faced with limited resources for emulating or simulating synaptic connections. This is particularly relevant for electronic implementations of spiking neural networks, ranging from GPU-based systems to mixed signal analog-digital neuromorphic VLSI devices.

Simple square pulses for implementing spike‐timing‐dependent plasticity in phase‐change memory

Phase‐change memory (PCM) is a promising candidate as an artificial synapse. A compact operation method to implement synaptic functions with low power consumption is critical for constructing large‐scale neuromorphic system. Here we propose a square spike strategy for implementing spike‐timing‐dependent plasticity (STDP) in PCM. Based on the heat accumulation effect in PCM, modulating the time intervals of pre‐ and post‐spikes results in different heat generation and dissipation conditions, which lead to various crystalline/ amorphous ratios in the phase change material layer in devices with diverse synaptic weights. Four forms of STDP learning rule are experimentally demonstrated. This study will further promote the development of PCM technology involved in neuromorphic systems. (© 2015 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)

Competitive STDP Learning of Overlapping Spatial Patterns.

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple distinct patterns. If patterns overlap significantly (i.e., patterns are mutually inclusive), however, competition would not preclude trained neuron's responding to a new pattern and adjusting synaptic weights accordingly. This letter presents a simple neural network that combines vertical inhibition and Euclidean distance-dependent synaptic strength factor. This approach helps to solve the problem of pattern size-dependent parameter optimality and significantly reduces the probability of a neuron's forgetting an already learned pattern. For demonstration purposes, the network was trained for the first ten letters of the Braille alphabet.

Inheritance of Hippocampal Place Fields Through Hebbian Learning: Effects of Theta Modulation and Phase Precession on Structure Formation.

A place cell is a neuron that fires whenever the animal traverses a particular location of the environment-the place field of the cell. Place cells are found in two regions of the rodent hippocampus: CA3 and CA1. Motivated by the anatomical connectivity between these two regions and by the evidence for synaptic plasticity at these connections, we study how a place field in CA1 can be inherited from an upstream region such as CA3 through a Hebbian learning rule, in particular, through spike-timing-dependent plasticity (STDP). To this end, we model a population of CA3 place cells projecting to a single CA1 cell, and we assume that the CA1 input synapses are plastic according to STDP. With both numerical and analytical methods, we show that in the case of overlapping CA3 input place fields, the STDP learning rule leads to the formation of a place field in CA1. We then investigate the roles of the hippocampal theta modulation and phase precession on the inheritance process. We find that theta modulation favors the inheritance and leads to faster place field formation whereas phase precession changes the drift of CA1 place fields over time.

Network-timing-dependent plasticity.

Bursts of activity in networks of neurons are thought to convey salient information and drive synaptic plasticity. Here we report that network bursts also exert a profound effect on Spike-Timing-Dependent Plasticity (STDP). In acute slices of juvenile rat somatosensory cortex we paired a network burst, which alone induced long-term depression (LTD), with STDP-induced long-term potentiation (LTP) and LTD. We observed that STDP-induced LTP was either unaffected, blocked or flipped into LTD by the network burst, and that STDP-induced LTD was either saturated or flipped into LTP, depending on the relative timing of the network burst with respect to spike coincidences of the STDP event. We hypothesized that network bursts flip STDP-induced LTP to LTD by depleting resources needed for LTP and therefore developed a resource-dependent STDP learning rule. In a model neural network under the influence of the proposed resource-dependent STDP rule, we found that excitatory synaptic coupling was homeostatically regulated to produce power law distributed burst amplitudes reflecting self-organized criticality, a state that ensures optimal information coding.

Mixed signal learning by spike correlation propagation in feedback inhibitory circuits.

The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP) is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory.

Simplified spiking neural network architecture and STDP learning algorithm applied to image classification

Spiking neural networks (SNN) have gained popularity in embedded applications such as robotics and computer vision. The main advantages of SNN are the temporal plasticity, ease of use in neural interface circuits and reduced computation complexity. SNN have been successfully used for image classification. They provide a model for the mammalian visual cortex, image segmentation and pattern recognition. Different spiking neuron mathematical models exist, but their computational complexity makes them ill-suited for hardware implementation. In this paper, a novel, simplified and computationally efficient model of spike response model (SRM) neuron with spike-time dependent plasticity (STDP) learning is presented. Frequency spike coding based on receptive fields is used for data representation; images are encoded by the network and processed in a similar manner as the primary layers in visual cortex. The network output can be used as a primary feature extractor for further refined recognition or as a simple object classifier. Results show that the model can successfully learn and classify black and white images with added noise or partially obscured samples with up to ×20 computing speed-up at an equivalent classification ratio when compared to classic SRM neuron membrane models. The proposed solution combines spike encoding, network topology, neuron membrane model and STDP learning.

Exploiting pallidal plasticity for stimulation in Parkinson’s disease

Objective. Continuous application of high-frequency deep brain stimulation (DBS) often effectively reduces motor symptoms of Parkinson’s disease patients. While there is a growing need for more effective and less traumatic stimulation, the exact mechanism of DBS is still unknown. Here, we present a methodology to exploit the plasticity of GABAergic synapses inside the external globus pallidus (GPe) for the optimization of DBS. Approach. Assuming the existence of spike-timing-dependent plasticity (STDP) at GABAergic GPe–GPe synapses, we simulate neural activity in a network model of the subthalamic nucleus and GPe. In particular, we test different DBS protocols in our model and quantify their influence on neural synchrony. Main results. In an exemplary set of biologically plausible model parameters, we show that STDP in the GPe has a direct influence on neural activity and especially the stability of firing patterns. STDP stabilizes both uncorrelated firing in the healthy state and correlated firing in the parkinsonian state. Alternative stimulation protocols such as coordinated reset stimulation can clearly profit from the stabilizing effect of STDP. These results are widely independent of the STDP learning rule. Significance. Once the model settings, e.g., connection architectures, have been described experimentally, our model can be adjusted and directly applied in the development of novel stimulation protocols. More efficient stimulation leads to both minimization of side effects and savings in battery power.

A Minimal Spiking Neural Network to Rapidly Train and Classify Handwritten Digits in Binary and 10-Digit Tasks

This paper reports the results of experiments to develop a minimal neural network for pattern classification. The network uses biologically plausible neural and learning mechanisms and is applied to a subset of the MNIST dataset of handwritten digits. The research goal is to assess the classification power of a very simple biologically motivated mechanism. The network architecture is primarily a feedforward spiking neural network (SNN) composed of Izhikevich regular spiking (RS) neurons and conductance-based synapses. The weights are trained with the spike timing-dependent plasticity (STDP) learning rule. The proposed SNN architecture contains three neuron layers which are connected by both static and adaptive synapses. Visual input signals are processed by the first layer to generate input spike trains. The second and third layers contribute to spike train segmentation and STDP learning, respectively. The network is evaluated by classification accuracy on the handwritten digit images from the MNIST dataset. The simulation results show that although the proposed SNN is trained quickly without error-feedbacks in a few number of iterations, it results in desirable performance (97.6%) in the binary classification (0 and 1). In addition, the proposed SNN gives acceptable recognition accuracy in 10-digit (0-9) classification in comparison with statistical methods such as support vector machine (SVM) and multi-perceptron neural network.

Spiking neuro-fuzzy clustering system and its memristor crossbar based implementation

This study proposes a spiking neuro-fuzzy clustering system based on a novel spike encoding scheme and a compatible learning algorithm. In this system, we utilize an analog to binary encoding scheme that properly maps the concept of “distance” in multi-dimensional analog spaces to the concept of “dissimilarity” of binary bits in the equivalent binary spaces. When this scheme is combined with a novel binary to spike encoding scheme and a proper learning algorithm is applied, a powerful clustering algorithm is produced. This algorithm creates flexible fuzzy clusters in its analog input space and modifies their shapes to different convex shapes during the learning process. This system has plausible biological support due to its spike-based learning mechanism, its Quasi Spike Time Dependent Plasticity learning policy and its brain-like fuzzy clustering performance. Moreover, this neuro-fuzzy system is fully implementable on the hybrid memristor-crossbar/CMOS platform. The resultant circuit was simulated on one clustering task carried out in the binary input space on the Simon Lucas handwritten dataset and another clustering task carried out in the analog input space on Fisher׳s Iris standard dataset. The results show that it attained a higher clustering rate in comparison with other algorithms such as the Self Organizing Map, K-mean and the Spiking Radial Basis Function. The circuit was also successfully simulated on an image segmentation task and some clustering tasks performed in noisy spaces with various cluster sizes. Furthermore, the circuit variability analysis shows that device and signal variations up to 20% had no significant impact on the circuit׳s clustering performance, so the system is sufficiently immune to different variations due to its fuzzy nature.

基于STDP 规则和忆阻桥突触的神经网络及图像处理

Memristor is a nonlinear circuit element which has property of memory and the similar synapse characteristic. Based on these properties, we presents a memristor bridge synaptic circuit based on the STDP (spike-time-dependent plasticity) learning rule which has an advantage that can be used as a synapse in the arti cial neural network. According to the advantage, we combine the new circuit with other circuits and networks and the bran-new circuits and networks are constructed. Firstly, we combine the memristor bridge synaptic circuit with three additional transistor components and the synaptic calculation of the neural network is realized, and then the complete memristor bridge synaptic neural network is constructed. Secondly, we combine the cellular neural network with it and can be used in image denoising, edge extraction, corner detection and Chinese character recognition. Finally, the feasibility of the proposed method is proved by the a series of the simulation experiments, and the memristor bridge synaptic neural network based on the STDP learning rule has more bionic feasibility, more integrated and more easy to replace the template are demonstrated, and it is hopeful to solve the real-time complex intelligent problems.

Computational capability of liquid state machines with spike-timing-dependent plasticity

Liquid state machine (LSM) is a recently developed computational model with a reservoir of recurrent spiking neural network (RSNN). This model has shown to be beneficial for performing computational tasks. In this paper, we present a novel type of LSM with self-organized RSNN instead of the traditional RSNN with random structure. Here, the spike-timing-dependent plasticity (STDP) which has been broadly observed in neurophysiological experiments is employed for the learning update of RSNN. Our computational results show that this model can carry out a class of biologically relevant real-time computational tasks with high accuracy. By evaluating the average mean squared error (MSE), we find that LSM with STDP learning is able to lead to a better performance than LSM with random reservoir, especially for the case of partial synaptic connections.