Hebbian models of development and learning require both activity-dependent synaptic plasticity and a mechanism that induces competition between different synapses. Recent experiments have characterized a form of long-term synaptic plasticity that depends on the relative timing of pre- and postsynaptic action potentials. In modeling studies, this form of synaptic modification, which we call spike-timing-dependent plasticity (STDP), automatically adjusts synaptic strengths so that the postsynaptic neuron becomes more sensitive to presynaptic spike timing. It has been argued that neurons in vivo operate in such a balanced or irregular ring mode, and STDP may thus explain how the required level of excitation arises and is maintained. Despite being synapse specific, STDP generates competition between different synapses because they compete for control of the timing of postsynaptic action potentials. By combining synaptic modification and competition, STPD can serve as a mechanism for competitiv...

such as the orientation of a line in the visual field or the location of Two main goals for reconstruction are approached in this the body in space are coded as activity levels in populations of neurons. Reconstruction or decoding is an inverse problem in which paper. The first goal is technical and is exemplified by the the physical variables are estimated from observed neural activity. population vector method applied to motor cortical activities Reconstruction is useful first in quantifying how much information during various reaching tasks (Georgopoulos et al. 1986, 1989; about the physical variables is present in the population and, second, Schwartz 1994) and the template matching method applied to in providing insight into how the brain might use distributed represen- disparity selective cells in the visual cortex (Lehky and Sejnowtations in solving related computational problems such as visual ob- ski 1990) and hippocampal place cells during rapid learning of ject recognition and spatial navigation. Two classes of reconstruction place fields in a novel environment (Wilson and McNaughton methods, namely, probabilistic or Bayesian methods and basis func- 1993). In these examples, reconstruction extracts information tion methods, are discussed. They include important existing methods from noisy neuronal population activity and transforms it to a

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.

Using experimental facts about long-term potentiation (LTP) and hippocampal place cells, we model how a spatial map of the environment can be created in the rat hippocampus. Sequential firing of place cells during exploration induces, in the model, a pattern of LTP between place cells that shifts the location coded by their ensemble activity away from the actual location of the animal. These shifts provide a navigational map that, in a simulation of the Morris maze, can guide the animal toward its goal. The model demonstrates how behaviorally generated modifications of synaptic strengths can be read out to affect subsequent behavior. Our results also suggest a way that navigational maps can be constructed from experimental recordings of hippocampal place cells. *Current address: Dept. of Brain and Cognitive Sciences, MIT E25-236, 45 Carlton St., Cambridge, MA 02139. Blockade of long term potentiation (LTP) and hippocampal lesions drastically impair

We analyze a model of navigational map formation based on correlation-based, temporally asymmetric potentiation and depression of synapses between hippocampal place cells. We show that synaptic modification during random exploration of an environment shifts the location encoded by place cell activity in such a way that it indicates the direction from any location to a fixed target avoiding walls and other obstacles. Multiple maps to different targets can be simultaneously stored if we introduce target-dependent modulation of place cell activity. Once maps to a number of target locations in a given environment have been stored, novel maps to previously unknown target locations are automatically constructed by interpolation between existing maps.

Recent experimental data indicate that the strengthening or weakening of synaptic connections between neurons depends on the relative timing of pre- and postsynaptic action potentials. AHebbian synaptic modification rule based on these data leads to a stable state in which the excitatory and inhibitory inputs to a neuron are balanced, producing an irregular pattern of firing. It has been proposed that neurons in vivo operate in such a mode. 1 Introduction Hebbian modification of network interconnections plays a central role in the study of learning in neural networks (Rumelhart and McClelland, 1986; Hertz et al., 1991). Most work on Hebbian learning involves network models in which the activities of the individual units are represented by continuous variables. A Hebbian learning rule, in this context, is specified by describing how network weights change as a function of the activities of the units that transmit and receive signals across a given network connection. While analyses of ...

We study analytically a model of long-term synaptic plasticity where synaptic changes are triggered by presynaptic spikes, postsynaptic spikes, and the time dierences between presynaptic and postsynaptic spikes. The changes due to correlated input and output spikes are quanti- ed by means of a learning window. We show that plasticity can lead to an intrinsic stabilization of the mean ring rate of the postsynaptic neuron. Subtractive normalization of the synaptic weights (summed over all presynaptic inputs converging on a postsynaptic neuron) follows if, in addition, the mean input rates and the mean input correlations are identical at all synapses. If the integral over the learning window is positive, ring-rate stabilization requires a non-Hebbian component, whereas such a component is not needed, if the integral of the learning window is negative. A negative integral corresponds to `anti-Hebbian' learning in a model with slowly varying ring rates. For spike-based learning, a strict distinction between Hebbian and `anti-Hebbian' rules is questionable since learning is driven by correlations on the time scale of the learning window. The correlations between presynaptic and postsynaptic ring are evaluated for a piecewise-linear Poisson model and for a noisy spiking neuron model with refractoriness. While a negative integral over the learning window leads to intrinsic rate stabilization, the positive part of the learning window picks up spatial and temporal correlations in the input.

Introduction Biological neural networks are large systems of complex elements interacting through a complex array of connections. Individual neurons express a large number of active conductances (Connors et al., 1982; Adams & Gavin, 1986; Llin'as, 1988; McCormick, 1990; Hille, 1992) and exhibit a wide variety of dynamic behaviors on time scales ranging from milliseconds to many minutes (Llin'as, 1988; Harris-Warrick & Marder, 1991; Churchland & Sejnowski, 1992; Turrigiano et al., 1994). Neurons in cortical circuits are typically coupled to thousands of other neurons (Stevens, 1989) and very little is known about the strengths of these synapses (although see Rosenmund et al., 1993; Hessler et al., 1993; Smetters & Nelson, 1993). The complex firing patterns of large neuronal populations are difficult to describe let alone understand. There is little point in accurately modeling each membrane potential in a large neural

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

markram.html 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. Instead of storing discrete frames of past visual inputs in a suitable datastructure and then applying a time-consuming algorithm to extract salient information about motion from these frames, we demonstrate through computer simulations that the following approach is in principle also feasible. Continuously timevarying visual inputs u (⋅) are directly injected into a sufficiently large and complex dynamical system (such as for example a cortical microcircuit). Simple readout devices can be trained to extract from the current state x(t) of such dynamical system at any time t in real-time salient information about the trajectory of recent inputs u ( s) for s < t. Since detailed knowledge about the precise structure of the dynamical system 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 dynamical system completely different information components.