Biomedical subjects

Bernd Porr

Publications and source records attributed to Bernd Porr.

4 recordsLinked to original sources

PubMed2004

How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model.

Spike-timing-dependent plasticity (STDP) is described by long-term potentiation (LTP), when a presynaptic event precedes a postsynaptic event, and by long-term depression (LTD), when the temporal order is reversed. In this article, we present a biophysical model of STDP based on a differential Hebbian learning rule (ISO learning). This rule correlates presynaptically the NMDA channel conductance with the derivative of the membrane potential at the synapse as the postsynaptic signal. The model is able to reproduce the generic STDP weight change characteristic. We find that (1) The actual shape of the weight change curve strongly depends on the NMDA channel characteristics and on the shape of the membrane potential at the synapse. (2) The typical antisymmetrical STDP curve (LTD and LTP) can become similar to a standard Hebbian characteristic (LTP only) without having to change the learning rule. This occurs if the membrane depolarization has a shallow onset and is long lasting. (3) It is known that the membrane potential varies along the dendrite as a result of the active or passive backpropagation of somatic spikes or because of local dendritic processes. As a consequence, our model predicts that learning properties will be different at different locations on the dendritic tree. In conclusion, such site-specific synaptic plasticity would provide a neuron with powerful learning capabilities.

Action Potentials↗

PubMed2003

Isotropic-sequence-order learning in a closed-loop behavioural system.

The simplest form of sensor-motor control is obtained with a reflex. In this case the reflex can be interpreted as part of a closed-loop control paradigm which measures a sensor input and generates a motor reaction as soon as the sensor signal deviates from its desired (resting) state. This is a typical case of feedback control. However, reflex reactions are tardy, because they occur always only after a (for example, unpleasant) reflex-eliciting sensor event. This defines an objective problem for an organism which can only be avoided if the corresponding motor reaction is generated earlier. The goal of this study is to design a closed-loop control situation where temporal-sequence learning supersedes a tardy reflex reaction with a proactive anticipatory action. We achieve this by employing a second, earlier-occurring and causally coupled sensor event. An appropriate motor reaction to this early event prevents triggering of the original, primary reflex. Such causally coupled sensor events are common for animals, for example when smell predicts taste or when heat radiation precedes pain. We show that trying to achieve anticipatory control is a fundamentally different goal from trying to model a classical conditioning paradigm, which is an open-loop condition. To this end, we use a novel learning rule for temporal-sequence learning called isotropic-sequence-order (ISO) learning, which performs a confounded correlation between the primary sensor signal associated to the reflex and a predictive, earlier-occurring sensor input: this way the system learns the relation between the primary reflex and the earlier sensor input in order to create an earlier-occurring motor reaction. As a consequence of learning, the primary reflex will not be triggered any more, thereby permanently remaining in its desired resting state. In a robot application, we demonstrate that ISO learning can successfully solve the classical obstacle-avoidance task by learning to correlate a built-in reflex behaviour (retraction after touching) with earlier arising signals from range finders (before touching). Finally, we show that avoidance and attraction tasks can be combined in the same agent.

Adaptation, Physiological↗

PubMed2003

Isotropic sequence order learning.

In this article, we present an isotropic unsupervised algorithm for temporal sequence learning. No special reward signal is used such that all inputs are completely isotropic. All input signals are bandpass filtered before converging onto a linear output neuron. All synaptic weights change according to the correlation of bandpass-filtered inputs with the derivative of the output. We investigate the algorithm in an open- and a closed-loop condition, the latter being defined by embedding the learning system into a behavioral feedback loop. In the open-loop condition, we find that the linear structure of the algorithm allows analytically calculating the shape of the weight change, which is strictly heterosynaptic and follows the shape of the weight change curves found in spike-time-dependent plasticity. Furthermore, we show that synaptic weights stabilize automatically when no more temporal differences exist between the inputs without additional normalizing measures. In the second part of this study, the algorithm is is placed in an environment that leads to closed sensor-motor loop. To this end, a robot is programmed with a prewired retraction reflex reaction in response to collisions. Through isotropic sequence order (ISO) learning, the robot achieves collision avoidance by learning the correlation between his early range-finder signals and the later occurring collision signal. Synaptic weights stabilize at the end of learning as theoretically predicted. Finally, we discuss the relation of ISO learning with other drive reinforcement models and with the commonly used temporal difference learning algorithm. This study is followed up by a mathematical analysis of the closed-loop situation in the companion article in this issue, "ISO Learning Approximates a Solution to the Inverse-Controller Problem in an Unsupervised Behavioral Paradigm" (pp. 865-884).

Algorithms↗

PubMed2003

ISO learning approximates a solution to the inverse-controller problem in an unsupervised behavioral paradigm.

In "Isotropic Sequence Order Learning" (pp. 831-864 in this issue), we introduced a novel algorithm for temporal sequence learning (ISO learning). Here, we embed this algorithm into a formal nonevaluating (teacher free) environment, which establishes a sensor-motor feedback. The system is initially guided by a fixed reflex reaction, which has the objective disadvantage that it can react only after a disturbance has occurred. ISO learning eliminates this disadvantage by replacing the reflex-loop reactions with earlier anticipatory actions. In this article, we analytically demonstrate that this process can be understood in terms of control theory, showing that the system learns the inverse controller of its own reflex. Thereby, this system is able to learn a simple form of feedforward motor control.

Algorithms↗