Cognitive processes depend on synchronization and propagation of electrical activity within and between neuronal assemblies. In vivo measurements show that the size of individual assemblies depends on their function and varies considerably, but the timescale of assembly activation is in the range of 0.1-0.2 s and is primarily independent of assembly size. Here we use an in vitro experimental model of cortical assemblies to characterize the process underlying the timescale of synchronization, its relationship to the effective topology of connectivity within an assembly, and its impact on propagation of activity within and between assemblies. We show that the basic mode of assembly activation, "network spike," is a threshold-governed, synchronized population event of 0.1-0.2 s duration and follows the logistics of neuronal recruitment in an effectively scale-free connected network. Accordingly, the sequence of neuronal activation within a network spike is nonrandom and hierarchical; a small subset of neurons is consistently recruited tens of milliseconds before others. Theory predicts that scale-free topology allows for synchronization time that does not increase markedly with network size; our experiments with networks of different densities support this prediction. The activity of early-to-fire neurons reliably forecasts an upcoming network spike and provides means for expedited propagation between assemblies. We demonstrate this capacity by observing the dynamics of two artificially coupled assemblies in vitro, using neuronal activity of one as a trigger for electrical stimulation of the other.

1. Introduction 631.1 Outline 631.2 Universals versus realizations in the study of learning and memory 642. Large random cortical networks developing ex vivo 652.1 Preparation 652.2 Measuring electrical activity 673. Spontaneous development 693.1 Activity 693.2 Connectivity 704. Consequences of spontaneous activity: pharmacological manipulations 724.1 Structural consequences 724.2 Functional consequences 735. Effects of stimulation 745.1 Response to focal stimulation 745.2 Stimulation-induced changes in connectivity 746. Embedding functionality in real neural networks 776.1 Facing the physiological definition of ‘reward’: two classes of theories 786.2 Closing the loop 797. Concluding remarks 848. Acknowledgments 859. References 85The phenomena of learning and memory are inherent to neural systems that differ from each other markedly. The differences, at the molecular, cellular and anatomical levels, reflect the wealth of possible instantiations of two neural learning and memory universals: (i) an extensive functional connectivity that enables a large repertoire of possible responses to stimuli; and (ii) sensitivity of the functional connectivity to activity, allowing for selection of adaptive responses. These universals can now be fully realized in ex-vivo developing neuronal networks due to advances in multi-electrode recording techniques and desktop computing. Applied to the study of ex-vivo networks of neurons, these approaches provide a unique view into learning and memory in networks, over a wide range of spatio-temporal scales. In this review, we summarize experimental data obtained from large random developing ex-vivo cortical networks. We describe how these networks are prepared, their structure, stages of functional development, and the forms of spontaneous activity they exhibit (Sections 2–4). In Section 5 we describe studies that seek to characterize the rules of activity-dependent changes in neural ensembles and their relation to monosynaptic rules. In Section 6, we demonstrate that it is possible to embed functionality into ex-vivo networks, that is, to teach them to perform desired firing patterns in both time and space. This requires ‘closing a loop’ between the network and the environment. Section 7 emphasizes the potential of ex-vivo developing cortical networks in the study of neural learning and memory universals. This may be achieved by combining closed loop experiments and ensemble-defined rules of activity-dependent change.

The results presented here demonstrate selective learning in a network of real cortical neurons. We focally stimulate the network at a low frequency (0.3-1 Hz) until a desired predefined response is observed 50 Ϯ 10 msec after a stimulus, at which point the stimulus is stopped for 5 min. Repeated cycles of this procedure ultimately lead to the desired response being directly elicited by the stimulus. By plotting the number of stimuli required to achieve the target response in each cycle, we are able to generate learning curves. Presumably, the repetitive stimulation is driving changes in the circuit, and we are selecting for changes consistent with the predefined desired response. To the best of our knowledge, this is the first time learning of arbitrarily chosen tasks, in networks composed of real cortical neurons, is demonstrated outside of the body.Key words: learning; multielectrode array; cultured neurons; neural network; reward; drive reduction Learning a new behavioral task is an exploration process that involves the formation and modulation of sets of associations between stimuli and responses. In an effort to understand the phenomenon of learning, two different questions are asked. (1) What are the neural mechanisms that underlie the formation and modulation of associations? (2) What are the principles that underlie the selection of "appropriate" associations over "inappropriate" ones? The nature of mechanisms underlying the formation and modulation of associations has been the topic of intense research. Although much is yet to be discovered, many mechanisms were described, at various levels of neural organization, that can support activity-dependent modification of associations between stimuli and responses. This study addresses the second question, the principles underlying the selection of an appropriate association during the learning process.Our learning experiments were performed in networks containing 10,000 -50,000 cortical neurons obtained from newborn rats (Baughman et al., 1991), under the assumption that the organizing principles operating at the level of neuronal populations are intrinsic to neurons and are therefore manifested ex vivo. Such cultured cortical networks were thoroughly studied by others (Ramakers et al., 1990;Murphy et al., 1992;Maeda et al., 1995;Canepari et al., 1997;Voigt et al., 1997;Turrigiano et al., 1998), and a substantial amount of data has been accumulated, showing that they are structurally rich, develop and adapt functionally and morphologically over a broad range of time scales, and are experimentally stable over weeks.In what follows, we show that the large random cortical networks developing ex vivo display general properties required from neural systems capable of learning: namely, numerous connections, stability of connections, and modifiability by external stimuli. We then describe closed-loop experiments in which these biological networks interact with a computer-controlled environment and demonstrate a simple procedure for learning and memorizing arbitrari...

Long term time-lapse imaging reveals that individual synapses undergo significant structural remodeling not only when driven by activity, but also when network activity is absent, raising questions about how reliably individual synapses maintain connections.

Although neuronal excitability is well understood and accurately modeled over timescales of up to hundreds of milliseconds, it is currently unclear whether extrapolating from this limited duration to longer behaviorally relevant timescales is appropriate. Here we used an extracellular recording and stimulation paradigm that extends the duration of single-neuron electrophysiological experiments, exposing the dynamics of excitability in individual cultured cortical neurons over timescales hitherto inaccessible. We show that the long-term neuronal excitability dynamics is unstable and dominated by critical fluctuations, intermittency, scale-invariant rate statistics, and long memory. These intrinsic dynamics bound the firing rate over extended timescales, contrasting observed short-term neuronal response to stimulation onset. Furthermore, the activity of a neuron over extended timescales shows transitions between quasi-stable modes, each characterized by a typical response pattern. Like in the case of rate statistics, the short-term onset response pattern that often serves to functionally define a given neuron is not indicative of its long-term ongoing response. These observations question the validity of describing neuronal excitability based on temporally restricted electrophysiological data, calling for in-depth exploration of activity over wider temporal scales. Such extended experiments will probably entail a different kind of neuronal models, accounting for the unbounded range, from milliseconds up.