nils-herrmann/clean_scirepeval huggingface dataset:
Principles of real-time computing with feedback applied to cortical microcircuit models

The network topology of neurons in the brain exhibits an abundance of feedback connections, but the computational function of these feedback connections is largely unknown. We present a computational theory that characterizes the gain in computational power achieved through feedback in dynamical systems with fading memory. It implies that many such systems acquire through feedback universal computational capabilities for analog computing with a non-fading memory. In particular, we show that feedback enables such systems to process time-varying input streams in diverse ways according to rules that are implemented through internal states of the dynamical system. In contrast to previous attractor-based computational models for neural networks, these flexible internal states are high-dimensional attractors of the circuit dynamics, that still allow the circuit state to absorb new information from online input streams. In this way one arrives at novel models for working memory, integration of evidence, and reward expectation in cortical circuits. We show that they are applicable to circuits of conductance-based Hodgkin-Huxley (HH) neurons with high levels of noise that reflect experimental data on in-vivo conditions.
From vision to multimodal communication: Incremental route descriptions

In the last few years, within cognitive science, there has been a growing interest in the connection between vision and natural language. The question of interest is: How can we discuss what we see. With this question in mind, we will look at the area ofincremental route descriptions. Here, a speaker step-by-step presents the relevant route information in a 3D-environment. The speaker must adjust his/her descriptions to the currently visible objects. Two major questions arise in this context: 1. How is visually obtained information used in natural language generation? and 2. How are these modalities coordinated? We will present a computational framework for the interaction of vision and natural language descriptions which integrates several processes and representations. Specifically discussed is the interaction between the spatial representation and the presentation representation used for natural language descriptions. We have implemented a prototypical version of the proposed model, called MOSES.
Computational models for generic cortical microcircuits

The human nervous system processes a continuous stream of multi-modal input from a rapidly changing environment. A key challenge for neural modeling is to explain how the neural microcircuits (columns, minicolumns, etc.) in the cerebral cortex whose anatomical and physiological structure is quite similar in many brain areas and species achieve this enormous computational task. We propose a computational model that could explain the potentially universal computational capabilities and does not require a task-dependent construction of neural circuits. Instead it is based on principles of high dimensional dynamical systems in combination with statistical learning theory, and can be implemented on generic evolved or found recurrent circuitry. This new approach towards understanding neural computation on the micro-level also suggests new ways of modeling cognitive processing in larger neural systems. In particular it questions traditional ways of thinking about neural coding.
On-Line Learning of Rectangles and Unions of Rectangles

AbstractWe design efficient algorithms for on-line learning of axis-parallel rectangles (and for the union of two such rectangles) in the common model for on-line learning with equivalence queries. With regard to the learning of rectangles in arbitrary dimensions d we solve the following open problem:Is there an algorithm for on-line learning of rectangles 
$$\prod\nolimits_{i = 1}^d {\{ a_i ,a_i + 1, \ldots ,b_i \} }$$
 over a discrete domain {1, 
$$\ldots $$
, n}d whose error bound is polylogarithmic in the size nd of the domain (i.e. polynomial in d and log n)?We give a positive solution by introducing a new design technique that appears to be of some interest on its own. The new learning algorithm for rectangles consists of 2d separate search strategies that search for the parameters a1, b1, 
$$\ldots $$
, ad, bd of the target rectangle. A learning algorithm with this type of modular design tends to fail because of the well known “credit assignment problem”: Which of the 2d local search strategies should be “blamed” when the global algorithm makes an error? We propose here a rather radical solution to this problem: each local search strategy that is possibly involved in an error of the global algorithm will be blamed. With this radical solution it is unavoidable that frequently local search strategies will be blamed incorrectly. We overcome this difficulty by employing local search strategies (“error tolerant binary search”) that are able to tolerate such incorrect credit assignments. The structure of this learning algorithm is reminiscent of “finite injury priority constructions” in recursive function theory.Section 4 contains another application of this design technique: an algorithm for learning the union of two rectangles in the plane.
Movement Generation and Control with Generic Neural Microcircuits

Simple linear readouts from generic neural microcircuit models can be trained to generate and control basic movements, e.g., reaching with an arm to various target points. After suitable training of these readouts on a small number of target points; reaching movements to nearby points can also be generated. Sensory or proprioceptive feedback turns out to improve the performance of the neural microcircuit model, if it arrives with a significant delay of 25 to 100 ms. Furthermore, additional feedbacks of “prediction of sensory variables” are shown to improve the performance significantly. Existing control methods in robotics that take the particular dynamics of sensors and actuators into account(“embodiment of robot control”) are taken one step further with this approach which provides methods for also using the “embodiment of computation”, i.e. the inherent dynamics and spatial structure of neural circuits, for the design of robot movement controllers.
Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits

What makes a neural microcircuit computationally powerful? Or more precisely, which measurable quantities could explain why one microcircuit C is better suited for a particular family of computational tasks than another microcircuit C′? We propose in this article quantitative measures for evaluating the computational power and generalization capability of a neural microcircuit, and apply them to generic neural microcircuit models drawn from different distributions. We validate the proposed measures by comparing their prediction with direct evaluations of the computational performance of these microcircuit models. This procedure is applied first to microcircuit models that differ with regard to the spatial range of synaptic connections and with regard to the scale of synaptic efficacies in the circuit, and then to microcircuit models that differ with regard to the level of background input currents and the level of noise on the membrane potential of neurons. In this case the proposed method allows us to quantify differences in the computational power and generalization capability of circuits in different dynamic regimes (UP- and DOWN-states) that have been demonstrated through intracellular recordings in vivo.
Theory of the Computational Function of Microcircuit Dynamics

This chapter discusses models for computation in cortical microcircuits in the light of general computational theories and biological data. We first review some basic concepts from computation theory. We then discuss existing models for computation in cortical microcircuits within this precise conceptual framework. We argue that models for online computing in dynamical systems require particular attention in this context, since they provide the best fit to the types of computational tasks that are solved by the brain, as well as the best fit to biological data about the anatomy and physiology of the underlying
Lower Bound Methods and Separation Results for On-Line Learning Models

AbstractWe consider the complexity of concept learning in various common models for on-line learning, focusing on methods for proving lower bounds to the learning complexity of a concept class. Among others, we consider the model for learning with equivalence and membership queries. For this model we give lower bounds on the number of queries that are needed to learn a concept class 
$$\mathcal{C}$$
 in terms of the Vapnik-Chervonenkis dimension of 
$$\mathcal{C}$$
, and in terms of the complexity of learning 
$$\mathcal{C}$$
 with arbitrary equivalence queries. Furthermore, we survey other known lower bound methods and we exhibit all known relationships between learning complexities in the models considered and some relevant combinatorial parameters. As it turns out, the picture is almost complete. This paper has been written so that it can be read without previous knowledge of Computational Learning Theory.
Information Dynamics and Emergent Computation in Recurrent Circuits of Spiking Neurons

We employ an efficient method using Bayesian and linear classifiers for analyzing the dynamics of information in high-dimensional states of generic cortical microcircuit models. It is shown that such recurrent circuits of spiking neurons have an inherent capability to carry out rapid computations on complex spike patterns, merging information contained in the order of spike arrival with previously acquired context information.
Input Prediction and Autonomous Movement Analysis in Recurrent Circuits of Spikitfg Neurons

Temporal integration of information and prediction of future sensory inputs are assumed to be important computational tasks of generic cortical microcircuits. It has remained open how cortical microcircuits could possibly achieve this, especially since they consist--in contrast to most neural network models--of neurons and synapses with heterogeneous dynamic responses. It turns out, however, that the diversity of computational units increases the capability of microcircuit models for temporal integration. Furthermore the prediction of future input may be rather easy for such circuits since it suffices to train the readouts from such microcircuits. In this article we show that very simple readouts from a generic recurrently connected circuit of integrate-and-fire neurons with diverse dynamic synapses can be trained in an unsupervised manner to predict movements of different objects, that move within an unlimited number of combinations of speed, angle, and offset over a simulated sensory field. The autonomously trained microcircuit model is also able to compute the direction of motion, which is a computationally difficult problem ('aperture problem') since it requires disambiguation of local sensory readings through the context of other sensory readings at the current and preceding moments. Furthermore the same circuit can be trained simultaneously in a supervised manner to also report the shape and velocity of the moving object. Finally it is shown that the trained neural circuit supports novelty detection and the generation of 'imagined movements'. Altogether the results of this article suggest that it is not necessary to construct specific and biologically unrealistic neural circuit models for specific sensory processing tasks, since 'found' generic cortical microcircuit models in combination with very simple perceptron-like readouts can easily be trained to solve such computational tasks.
Temporal integration in recurrent microcircuits

An essential feature of neural computation in behaving organisms is the capability to produce at any time t a response ) (t y that depends on sensory and internal inputs ) (s u that the system received at various times t s < back in the past. Such any-time intelligent response to temporally integrated input, which does not have to wait until some algorithm has completed its computation, is also desirable for artificial computing machinery. It would therefore be helpful to understand how neural systems solve this task. Because of the stereotypical features of cortical microcircuits (see [De Felipe, 1997], [Douglas et al., 1998], [Shepherd, 1988], [Thomson et al., 2002], [von Melchner et al., 2000]) one may hypothesize that there exists a common principle by which neural microcircuits in different cortical areas and species solve the task of adaptive real-time response to temporally integrated information. We will survey in this article the primary models that have been proposed for temporal integration in neural microcircuits of the cortex.
Perspectives of the high-dimensional dynamics of neural microcircuits from the point of view of low-dimensional readouts

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 recurrent circuit is sufficiently large. At the same time such readout neurons can exploit the stability and speed of learning rules for linear classifiers, thereby overcoming the problems caused by local minima in the error function of nonlinear classifiers. In addition it is demonstrated that pairs of readout neurons can transform the complex trajectory of transient states of a large neural circuit into a simple and clearly structured two-dimensional trajectory. This two-dimensional projection of the high-dimensional trajectory can even exhibit convergence to virtual attractors that are not apparent in the high-dimensional trajectory.
A Very Short Story About Autonomous Robots

Machine learning for autonomous mobile robots is a very interesting but also very difficult problem. In this domain you need robust, fast and efficient learning algorithms. The combination of machine learning and mobile robots allows us to create machines whose performance surpasses both explicitly programmed robots and humans. Although humans can learn, their sensors and actuators are in several aspects inferior to those of robots. On the other hand a non-learning robot can only perform tasks where all the individual difficulties and complexities can be anticipated by the programmer of the robot. In this short article we discuss both a completed project, and a new one that has just started. The first project produced a robot that applied learning to improve his minigolf skills. The new project is the creation of the first Austrian team of autonomous robot soccer players that will compete in the robot soccer world championship RoboCup 1
Paradigms for Computing with Spiking Neurons

In this chapter we define for various neural coding schemes formal models of computation in networks of spiking neurons. The main results about the computational power of these models are surveyed. In particular, we compare their computational power with that of common models for artificial neural networks. Some rigorous theoretical results are presented which show that for temporal coding of inputs and outputs certain functions can be computed in a feedforward network of spiking neurons with fewer neurons than in any multi-layer perceptron (i.e., feedforward network of sigmoidal neurons). This chapter also presents a brief survey of the literature on computations in networks of spiking neurons.
The "Liquid Computer": A Novel Strategy for Real-Time Computing on Time Series

We will discuss in this survey article a new framework for analysing computations on time series and in particular on spike trains, introduced in (Maass et. al. 2002). In contrast to common computational models this new framework does not require that information can be stored in some stable states of a computational system. It has recently been shown that such models where all events are transient can be successfully applied to analyse computations in neural systems and (independently) that the basic ideas can also be used to solve engineering tasks such as the design of nonlinear controllers. Using an illustrative example we will develop the main ideas of the proposed model. This illustrative example is generalized and cast into a rigorous mathematical model: the Liquid State Machine. A mathematical analysis shows that there are in principle no computational limitations of liquid state machines in the domain of time series computing. Finally we discuss several successful applications of the framework in the area of computational neuroscience and in the field of artificial neural networks.
A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models

We propose an alternative paradigm for processing time-varying visual inputs, in particular for tasks involving temporal and spatial integration, which is inspired by hypotheses about the computational role of cortical microcircuits. Since detailed knowledge about the precise structure of the microcircuit is not needed for that, it can in principle also be implemented with partially unknown or faulty analog hardware. In addition, this approach supports parallel real-time processing of time-varying visual inputs for diverse tasks, since different readouts can be trained to extract concurrently from the same microcircuit completely different information components.
Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations

A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.
A Model for Real-Time Computation in Generic Neural Microcircuits

A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real-time. We propose a new computational model that is based on principles of high dimensional dynamical systems in combination with statistical learning theory. It can be implemented on generic evolved or found recurrent circuitry.
Computing the Optimally Fitted Spike Train for a Synapse

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 two computational 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 in a certain sense. With the help of these methods, one can compute, for example, the temporal pattern of a spike train (with a given number of spikes) that produces the largest sum of postsynaptic responses for a specific synapse. Several other applications are also discussed. 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. Hence, our analysis provides a possible functional explanation for the experimentally observed regularity in the combination of specific types of synapses with specific types of neurons in neural circuits.
Efficient temporal processing with biologically realistic dynamic synapses

Synapses play a central role in neural computation: the strengths of synaptic connections determine the function of a neural circuit. In conventional models of computation, synaptic strength is assumed to be a static quantity that changes only on the slow timescale of learning. In biological systems, however, synaptic strength undergoes dynamic modulation on rapid timescales through mechanisms such as short term facilitation and depression. Here we describe a general model of computation that exploits dynamic synapses, and use a backpropagation-like algorithm to adjust the synaptic parameters. We show that such gradient descent suffices to approximate a given quadratic filter by a rather small neural system with dynamic synapses. We also compare our network model to artificial neural networks designed for time series processing. Our numerical results are complemented by theoretical analyses which show that even with just a single hidden layer such networks can approximate a surprisingly large class of nonlinear filters: all filters that can be characterized by Volterra series. This result is robust with regard to various changes in the model for synaptic dynamics.