temporal relations requires the joint evaluation of responses from more than one neuron, only experiments that permit simultaneous measurements of responses 60528 Frankfurt from multiple units are considered. These include multi-Federal Republic of Germany electrode recordings from multiple individual cells, but also measurements of local field potentials (LFPs) and electroencephalographic (EEG) or magnetoencephalo-Most of our knowledge about the functional organization of neuronal systems is based on the analysis of the firing patterns of individual neurons that have been recorded one by one in succession. This approach permits as-sessment of event-related variations in discharge rate, but it precludes detection of any covariations in the amplitude or timing of distributed responses if these graphic (MEG) recordings. The signals of these latter

Several formulations of correlation-based Hebbian learning are reviewed. On the presynaptic side, activity is described either by a firing rate or by presynaptic spike arrival. The state of the postsynaptic neuron can be described by its membrane potential, its firing rate, or the timing of backpropagating action potentials (BPAPs). It is shown that all of the above formulations can be derived from the point of view of an expansion. In the absence of BPAPs potentials, it is natural to correlate presynaptic spikes with the postsynaptic membrane potential. Time windows of spike time dependent plasticity arise naturally, if the timing of postsynaptic spikes is available at the site of the synapse as it is the case in the presence of BPAPs. With an appropriate choice of parameters, Hebbian synaptic plasticity has intrinsic normalization properties that stabilizes postsynaptic firing rates and leads to subtractive weight normalization.

Triggered by recent experimental results, temporally asymmetric Hebbian (TAH) plasticity is considered as a candidate model for the biological implementation of competitive synaptic learning, a key concept for the experience-based development of cortical circuitry. However, because of the well known positive feedback instability of correlation-based plasticity, the stability of the resulting learning process has remained a central problem. Plagued by either a runaway of the synaptic efficacies or a greatly reduced sensitivity to input correlations, the learning performance of current models is limited. Here we introduce a novel generalized nonlinear TAH learning rule that allows a balance between stability and sensitivity of learning. Using this rule, we study the capacity of the system to learn patterns of correlations between afferent spike trains. Specifically, we address the question of under which conditions learning induces spontaneous symmetry breaking and leads to inhomogeneous synaptic distributions that capture the structure of the input correlations. To study the efficiency of learning temporal relationships between afferent spike trains through TAH plasticity, we introduce a novel sensitivity measure that quantifies the amount of information about the correlation structure in the input, a learning rule capable of storing in the synaptic weights. We demonstrate that by adjusting the weight dependence of the synaptic changes in TAH plasticity, it is possible to enhance the synaptic representation of temporal input correlations while maintaining the system in a stable learning regime. Indeed, for a given distribution of inputs, the learning efficiency can be optimized. Key words: Hebbian learning; spike-timing-dependent plasticity; synaptic updating; symmetry breaking; unsupervised learning; infomax; activity-dependent development

prising and potentially detrimental to brain function. But another possibility is that synaptic unreliability is used by the brain for the purposes of learning (Minsky, 1954; Hinton, 1989), in analogy to the way in which unreliable genetic replication is used for evolution. Here I propose a specific implementation of this idea. According to the proposal, synapses are “hedonistic,” responding to a global reward signal by increasing their probabilities of release or failure, depending on which action immediately preceded reward. Remarkably, if each synapse in a network behaves hedonistically, selfishly seeking reward, then the network as a whole be-haves hedonistically, learning to increase its average reward by generating appropriate collective actions. This statement can be formulated and justified mathematically

Experimental evidence from a number of different preparations indicates that repeated pairing of pre- and postsynaptic action potentials can lead to long-term Brandeis University changes in synaptic efficacy, the sign and amplitude of Waltham, Massachusetts 02454-9110 which depend on relative spike timing (Levy and Steward,

this article, we explore the hypothesis that recurrent excitation in neocortical circuits subserves the function of prediction and generation of temporal sequences (for related ideas, see Jordan, 1986; Elman, 1990; Minai & Levy, 1993; Montague & Sejonowski, 1994; Abbott & Blum, 1996; Rao & Ballard, 1997; Barlow, 1998; Westerman, Northmore, & Elias, 1999). In particular, we show that a temporal-difference-based learning rule for prediction (Sutton, 1988), when applied to backpropagating action potentials in dendrites, reproduces the temporally asymmetric window of Hebbian plasticity obtained in physiological experiments (see section 3). We examine the stability of the learning rule in section 4 and discuss possible biophysical mechanisms for implementing this rule in section 5. We also provide a simple example demonstrating how such a learning mechanism may allow cortical networks to learn to predict their inputs using recurrent excitation. The model predicts that cortical neurons may employ different temporal windows of plasticity at different dendritic locations to allow them to capture correlations between pre- and postsynaptic activity at different timescales (see section 6). A preliminary report of this work appeared as Rao and Sejnowski (2000)

Abstract not found

In this article we compare methods for temporal sequence learning (TSL) across the disciplines machine-control, classical conditioning, neuronal models for TSL as well as spiketiming dependent plasticity. This review will briefly introduce the most influential models and focus on two questions: 1) To what degree are reward-based (e.g. TD-learning) and correlation based (hebbian) learning related? and 2) How do the different models correspond to possibly underlying biological mechanisms of synaptic plasticity? We will first compare the different models in an open-loop condition, where behavioral feedback does not alter the learning. Here we observe, that reward-based and correlation based learning are indeed very similar. Machine-control is then used to introduce the problem of closed-loop control (e.g. “actor-critic architectures”). Here the problem of evaluative (“rewards”) versus nonevaluative (“correlations”) feedback from the environment will be discussed showing that both learning approaches are fundamentally different in the closed-loop condition. In trying to answer the second question we will compare neuronal versions of the different learning architectures to the anatomy of the involved brain structures (basal-ganglia, thalamus and

Synaptic plasticity was recently shown to depend on the relative timing of the pre- and postsynaptic spikes. This article analytically derives a spike-dependent learning rule based on the principle of information maximization for a single neuron with spiking inputs. This rule is then transformed into a biologically feasible rule, which is compared to the experimentally observed plasticity. This comparison reveals that the biological rule increases information to a near-optimal level and provides insights into the structure of biological plasticity. It shows that the time dependency of synaptic potentiation should be determined by the synaptic transfer function and membrane leak. Potentiation consists of weightdependent and weight-independent components whose weights are of the same order of magnitude. It further suggests that synaptic depression should be triggered by rare and relevant inputs but at the same time serves to unlearn the baseline statistics of the network’s inputs. The optimal depression curve is uniformly extended in time, but biological constraints that cause the cell to forget past events may lead to a different shape, which is not specified by our current model. The structure of the optimal rule thus suggests a computational account for several temporal characteristics of the biological spike-timing-dependent rules.

Synaptic plasticity was recently shown to depend on the relative timing of the pre and post synaptic spikes. The current paper analytically derives a spike dependent learning rule based on the principle of information maximization for a single neuron with spiking inputs. This rule is then transformed into a biologically feasible rule, which is compared to the experimentally observed plasticity. This comparison reveals that the biological rule increases information to a near optimal level, and provides insights into the structure of biological plasticity: It shows that time dependency of synaptic potentiation should be determined by the synaptic transfer function and membrane leak. Potentiation consists of weight dependent and weight independent components whose weights are of the same order of magnitude. It further suggests that synaptic depression should be triggered by rare and relevant inputs but at the same time serves to unlearn the baseline statistics of the network’s inputs. The optimal depression curve is uniformly extended in time, but biological constraints that cause the cell to forget past events may lead to a different shape, which is not specified by our current model. The structure of the optimal rule thus suggests a computational account for several temporal characteristics of the biological spike timing dependent rules. 1 1