Abstract. Prediction is an important task in robot motor control where it is used to gain feedback for a controller. With such a self-generated feedback, which is available before sensor readings from an environment can be processed, a controller can be stabilized and thus the performance of a moving robot in a real-world environment is improved. So far, only experiments with artificially generated data have shown good results. In a sequence of experiments we evaluate whether a liquid state machine in combination with a supervised learning algorithm can be used to predict ball trajectories with input data coming from a video camera mounted on a robot participating in the RoboCup. This pre-processed video data is fed into a recurrent spiking neural network. Connections to some output neurons are trained by linear regression to predict the position of a ball in various time steps ahead. Our results support the idea that learning with a liquid state machine can be applied not only to designed data but also to real, noisy data. 1

We investigate generic models for cortical microcircuits, i.e., recurrent circuits of integrate-and-fire neurons with dynamic synapses. These complex dynamic systems subserve the amazing information processing capabilities of the cortex, but are at the present time very little understood. We analyze the transient dynamics of models for neural microcircuits from the point of view of one or two readout neurons that collapse the high-dimensional transient dynamics of a neural circuit into a one- or two-dimensional output stream. This stream may for example represent the information that is projected from such circuit to some particular other brain area or actuators. It is shown that simple local learning rules enable a readout neuron to extract from the high-dimensional transient dynamics of a recurrent neural circuit quite different low-dimensional projections, which even may contain “virtual attractors ” that are not apparent in the high-dimensional dynamics of the circuit itself. Furthermore it is demonstrated that the information extraction capabilities of linear readout neurons are boosted by the computational operations of a sufficiently large preceding neural microcircuit. Hence a generic neural microcircuit may play a similar role for information processing as a kernel for support vector machines in machine learning. We demonstrate that the projection of time-varying inputs into a large recurrent neural circuit enables a linear readout neuron to classify the time-varying circuit inputs with the same power as complex nonlinear classifiers, such as a pool of perceptrons trained by the p-delta rule or a feedforward sigmoidal neural net trained by backprop, provided that the size of the

It is quite difficult to construct circuits of spiking neurons that can carry out complex computational tasks. On the other hand even randomly connected circuits of spiking neurons can in principle be used for complex computational tasks such as time-warp invariant speech recognition. This is possible because such circuits have an inherent tendency to integrate incoming information in such a way that simple linear readouts can be trained to transform the current circuit activity into the target output for a very large number of computational tasks. Consequently we propose to analyze circuits of spiking neurons in terms of their roles as analog fading memory and nonlinear kernels, rather than as im-plementations of specific computational operations and algorithms. This article is a sequel to [31], and contains new results about the performance of generic neural microcircuit models for the recognition of speech that is subject to linear

This review aims to summarize data obtained with different techniques to provide a functional map of the local circuit connections made by neocortical neurones, a reference for those interested in cortical circuitry and the numerical information required by those wishing to model the circuit. A brief description of the main techniques used to study circuitry is followed by outline descriptions of the major classes of neocortical excitatory and inhibitory neurones and the connections that each layer makes with other cortical and subcortical regions. Maps summarizing the projection patterns of each class of neurone within the local circuit and tables of the properties of these local circuit connections are provided. This review relies primarily on anatomical studies that have identified the classes of neurones and their local and long distance connections and on paired intracellular and whole-cell recordings which have documented the properties of the connections between them. A large number of different types of synaptic connections have been described, but for some there are only a few published examples and for others the details that can only be obtained with paired recordings and dye-filling are lacking. A further complication is provided by the range of species, technical approaches and age groups used in these studies. Wherever possible the range of available data are summarised and compared. To fill some of the more obvious gaps for the less well-documented cases, data obtained with other methods are also summarized.

prepared under our supervision by

Recent experimental results have revealed a large diversity of anatomic, synaptic and membrane physiology within the GABAergic system. By incorporating combinations of parametrically varied M-type, A-type, and calcium-dependent potassium (AHP) channels, we were able to replicate in vitro responses to 1-second current steps ranging from 50 to 350 pA. Our results show the need for subthreshold activating channels to capture the physiological properties of GABAergic cells. Presently underway is a collaboration among the authors to corroborate the proposed channel models using genetic profiles obtained from physiologically characterized interneurons. 1

Recent electrophysiological experiments have shown that modulation of pyramidal cells in prefrontal cortex by D1 (dopamine) receptors abolishes spike frequency adaptation and synaptic depression and enhances NMDA-transmission. Using a modified integrate-and-fire model of spiking neurons, we examine the effects of these changes on sustained (delay) activity in a reverberant network. We find that D1 modulation enables longer durations (? 1 s) and noise-resistant sustained activity. High frequencies of firing (40 Hz) at low stimulation amplitudes are possible. Together with the modulation of interneurons, these modulations affect the tuning of "memory fields" and yield more efficient distributed representations. The role of dopamine in influencing neuronal parameters on short time scales, (short-term adaptivity, [5]) is increasingly becoming elucidated. It has been shown that dopamine influences spike frequency adaptation (SFA), synaptic depression and NMDA-receptor activity thr...

Over the last several years of research, we have developed a large-scale biologically realistic neocortical neural network simulator. The simulator's effectiveness as a research tool has been limited due to accessibility and ease of use. The web portal for the neocortical simulator provides online access from anywhere in the world at any time. Its GUI interface allows users to build and simulate networks in a very short period of time. This portal was built using PHP, Mysql, and a back-end running Apache on a Red Hat Linux machine.

Experimental data have shown that synapses are heterogeneous: different synapses respond with different sequences of amplitudes of postsynaptic responses to the same spike train. Neither the role of synaptic dynamics itself nor the role of the heterogeneity of synaptic dynamics for computations in neural circuits is well understood. We present in this article methods that make it feasible to compute for a given synapse with known synaptic parameters the spike train that is optimally fitted to the synapse, for example in the sense that it produces the largest sum of postsynaptic responses. To our surprise we find that most of these optimally fitted spike trains match common firing patterns of specific types of neurons that are discussed in the literature.

In this paper, we present a network of silicon interneurons that synchronize in the gamma frequency range (20–80 Hz). The gamma rhythm strongly influences neuronal spike timing within many brain regions, potentially playing a crucial role in computation. Yet it has largely been ignored in neuromorphic systems, which use mixed analog and digital circuits to model neurobiology in silicon. Our neurons synchronize by using shunting inhibition (conductance based) with a synaptic rise time. Synaptic rise time promotes synchrony by delaying the effect of inhibition, providing an opportune period for interneurons to spike together. Shunting inhibition, through its voltage dependence, inhibits interneurons that spike out of phase more strongly (delaying the spike further), pushing them into phase (in the next cycle). We characterize the interneuron, which consists of soma (cell body) and synapse circuits, fabricated in a 0.25-µm