We present a simulation framework, called NETMORPH, for the developmental generation of 3D large-scale neuronal networks with realistic neuron morphologies. In NETMORPH, neuronal morphogenesis is simulated from the perspective of the individual growth cone. For each growth cone in a growing axonal or dendritic tree, its actions of elongation, branching and turning are described in a stochastic, phenomenological manner. In this way, neurons with realistic axonal and dendritic morphologies, including neurite curvature, can be generated. Synapses are formed as neurons grow out and axonal and dendritic branches come in close proximity of each other. NETMORPH is a flexible tool that can be applied to a wide variety of research questions regarding morphology and connectivity. Research applications include studying the complex relationship between neuronal morphology and global patterns of synaptic connectivity. Possible future developments of NETMORPH are discussed.

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Acetylcholine may regulate working memory for novel stimuli by activating intrinsic mechanisms for sustained spiking in entorhinal cortical neurons, which have been demonstrated in slice preparations of the entorhinal cortex. Computational modeling demonstrates that loss of the cholinergic activation of intrinsic mechanisms for sustained activity could selectively impair working memory for novel stimuli, whereas working memory for familiar stimuli could be maintained because of previously modified synapses. Blockade of muscarinic cholinergic receptors and selective cholinergic lesions has been shown to impair encoding in delayed matching tasks. However, previous studies have not compared explicitly the role of cholinergic modulation in working memory for novel versus familiar stimuli. Here, we show that lesions of the cholinergic innervation of the entorhinal cortex selectively impair delayed nonmatch to sample performance for novel odors, whereas delayed nonmatch to sample for familiar odors is spared. This indicates an important role for cholinergic innervation of the entorhinal cortex in working memory for novel stimuli.

Persistent neuronal firing has been modeled in relation to observed brain rhythms, especially to theta oscillations recorded in behaving animals. Models of short-term memory that are based on such persistent firing properties of specific neurons can meet the requirements of spike-timing-dependent potentiation of synaptic strengths during the encoding of a temporal sequence of spike patterns. We show that such a spiking buffer can be simulated with integrate-and-fire neurons that include a leak current even when different numbers of spikes represent successive items. We propose a mechanism that successfully replaces items in the buffer in first-in-first-out (FIFO) order when the distribution of spike density in a theta cycle is asymmetric, as found in experimental data. We predict effects on the function and capacity of the buffer model caused by changes in modeled theta cycle duration, the timing of input to the buffer, the strength of recurrent inhibition, and the strength and timing of after-hyperpolarization and after-depolarization (ADP). Shifts of input timing or changes in ADP parameters can enable the reverse-order buffering of items, with FIFO replacement in a full buffer. As noise increases, the simulated buffer provides robust output that may underlie episodic encoding.

The orbital frontal cortex appears to be involved in learning the rules of goal-directed behavior necessary to perform the correct actions based on perception to accomplish different tasks. The activity of orbitofrontal neurons changes dependent upon the specific task or goal involved, but the functional role of this activity in performance of specific tasks has not been fully determined.Here we present a model of prefrontal cortex function using networks of integrate-and-fire neurons arranged in minicolumns. This network model forms associations between representations of sensory input and motor actions, and uses these associations to guide goal-directed behavior. The selection of goal-directed actions involves convergence of the spread of activity from the goal representation with the spread of activity from the current state. This spiking network model provides a biological implementation of the action selection process used in reinforcement learning theory. The spiking activity shows properties similar to recordings of orbitofrontal neurons during task performance.

We propose a mechanism to explain both retrospective and prospective recall activity found in experimental data from hippocampal regions CA3 and CA1. Our model of temporal context dependent episodic memory replicates reverse recall in CA1, as recently recorded and published [Foster, D., & Wilson, M. (2006). Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature, 440, 680-683], as well as the prospective and retrospective activity recorded in region CA3 during spatial tasks [Johnson, A., & Redish, A. (2006). Neural ensembles in ca3 transiently encode paths forward of the animal at a decision point: a possible mechanism for the consideration of alternatives. In 2006 neuroscience meeting planner. Atlanta, GA: Society for Neuroscience. (Program no. 574.2)]. We suppose that CA3 encodes episodic memory of both forward and reversed sequences of perforant path spikes representing place input. Using a persistent firing buffer mechanism in layer II of entorhinal cortex, simulated episodic learning involves dentate gyrus, layer III of entorhinal cortex, and hippocampal regions CA3 and CA1. Associations are formed between buffered episodic cues, unique temporal context specific representations in dentate gyrus, and episodic memory in the CA3 recurrent network.

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A variety of approaches are available for using computational models to help understand neural processes over many levels of description, from sub-cellular processes to behavior. Alongside purely deductive bottom-up or top-down modeling, a systems design strategy has the advantage of providing a clear goal for the behavior of a complex model. The order in which biological details are added is dictated by functional requirements in terms of the tasks that the model should perform. Ideas from engineering can be mixed with those from biology to build systems in which some constituents are modeled in detail using biologically-realistic components, while others are implemented directly in software. This allows the areas of most interest to be studied within the context of a behaving system in which each component is constrained both by the biology it is intended to represent as well as the task it is required to perform within the system. The Catacomb2 modeling package has been developed to allow rapid and flexible design and study of complex multi-level systems ranging in scale from ion channels to whole animal behavior. The methodology, internal architecture, and capabilities of the system are described. Its use is illustrated by a modeling case study in which hypotheses about how parahippocampal and hippocampal structures may be involved in spatial navigation tasks are implemented in a model of a virtual rat navigating through a virtual environment in search of a food reward. The model incorporates theta oscillations to separate encoding from retrieval and yields testable predictions about the phase relations of spiking activity to theta oscillations in different parts of the hippocampal formation at various stages of the behavioral task.