Scene analysis in the mammalian visual system, conceived as a distributed and parallel process, faces the so-called binding problem. As a possible solution, the temporal correlation hypothesis has been suggested and implemented in phase-coding models.

Here we develop and investigate a computational model of a network of cortical neurons on the base of biophysically well constrained and tested two-compartmental neurons developed by Pinsky and Rinzel [Pinsky and Rinzel, 1994]. To study associative memory we connect a pool of cells by a structured connectivity matrix. The connection weights are shaped by simple Hebbian coincidence learning using a set of spatially sparse patterns. We study the neuronal activity processes following an external stimulation of a stored memory. In two series of simulation experiments we explore the effect of different classes of external input, tonic and flashed stimulation: With tonic stimulation the addressed memory is attractor of the network dynamics. The memory is displayed rhythmically, coded by phase locked bursts or regular spikes. The participating neurons have rhythmic activity in the gamma-frequency range (30-80 Hz). If the input is switched from one memory to another, the network act...

Although experimental evidence for distributed cell assemblies is growing, theories of cell assemblies are still marginalized in theoretical neuroscience. We argue that this has to do with shortcomings of the currently best understood assembly theories, the ones based on formal associative memory models. These only insufficiently reflect anatomical and physiological properties of nervous tissue and their functionality is too restricted to provide a framework for cognitive modeling. We describe cell assembly models that integrate more neurobiological constraints and review results from simulations of a simple nonlocal associative network formed by a reciprocal topographic projection. Impacts of nonlocal associative projections in the brain are discussed

ABSTRACT: It has been proposed that the hippocampal theta rhythm (4–7 Hz) can contribute to memory formation by separating encoding (storage) and retrieval of memories into different functional half-cycles (Hasselmo et al. (2002) Neural Comput 14:793–817). We investigate, via computer simulations, the biophysical mechanisms by which storage and recall of spatio-temporal input patterns are achieved by the CA1 microcircuitry. A model of the CA1 microcircuit is presented that uses biophysical representations of the major cell types, including pyramidal (P) cells and four types of inhibitory interneurons: basket (B) cells, axo-axonic (AA) cells, bistratified (BS) cells and oriens lacunosum-moleculare (OLM) cells. Inputs to the network come from the entorhinal cortex (EC), the CA3 Schaffer collaterals and medial septum. The EC input provides the sensory information, whereas all other inputs provide context and timing information. Septal input provides timing information for phasing storage and recall. Storage is accomplished via a local STDP mediated hetero-association of the EC input pattern and the incoming CA3 input pattern on the CA1 pyramidal cell target synapses. The model simulates the timing of firing of different hippocampal cell types relative to the theta rhythm in anesthetized animals and proposes experimentally confirmed functional roles for the different classes of inhibitory interneurons in the storage and recall cycles (Klausberger et al., (2003, 2004) Nature 421:844–848, Nat Neurosci 7:41–47). Measures of recall performance of new and previously stored input patterns in the presence or absence of various inhibitory interneurons are employed to quantitatively test the performance of our model. Finally, the mean recall quality of the CA1 microcircuit is tested as the number of stored patterns is increased. VC 2009 Wiley-Liss, Inc. KEY WORDS: CA1 microcircuit model; storage and recall; pyramidal cell; basket cell; bistratified cell; OLM cell; axo-axonic cell; STDP