Neurons important for cognitive function are often classified by their morphology and integrative properties. However, it is unclear if within a single class of neuron these properties tune synaptic responses to the salient features of the information that each neuron represents. We demonstrate that for stellate neurons in layer II of the medial entorhinal cortex, the waveform of postsynaptic potentials, the time window for detection of coincident inputs, and responsiveness to gamma frequency inputs follow a dorsal-ventral gradient similar to the topographical organization of grid-like spatial firing fields of neurons in this area. We provide evidence that these differences are due to a membrane conductance gradient mediated by HCN and leak potassium channels. These findings suggest key roles for synaptic integration in computations carried out within the medial entorhinal cortex and imply that tuning of neural information processing by membrane ion channels is important for normal cognitive function.

Neuronal activity is mediated through changes in the probability of stochastic transitions between open and closed states of ion channels. While differences in morphology define neuronal cell types and may underlie neurological disorders, very little is known about influences of stochastic ion channel gating in neurons with complex morphology. We introduce and validate new computational tools that enable efficient generation and simulation of models containing stochastic ion channels distributed across dendritic and axonal membranes. Comparison of five morphologically distinct neuronal cell types reveals that when all simulated neurons contain identical densities of stochastic ion channels, the amplitude of stochastic membrane potential fluctuations differs between cell types and depends on sub-cellular location. For typical neurons, the amplitude of membrane potential fluctuations depends on channel kinetics as well as open probability. Using a detailed model of a hippocampal CA1 pyramidal neuron, we show that when intrinsic ion channels gate stochastically, the probability of initiation of dendritic or somatic spikes by dendritic synaptic input varies continuously between zero and one, whereas when ion channels gate deterministically, the probability is either zero or one. At physiological firing rates, stochastic gating of dendritic ion channels almost completely accounts for probabilistic somatic and dendritic spikes generated by the fully stochastic model. These results suggest that the consequences of stochastic ion channel gating differ globally between neuronal cell-types and locally between neuronal compartments. Whereas dendritic neurons are often assumed to behave deterministically, our simulations suggest that a direct consequence of stochastic gating of intrinsic ion channels is that spike output may instead be a probabilistic function of patterns of synaptic input to dendrites.

Long-term synaptic plasticity requires postsynaptic influx of Ca 2ϩ and is accompanied by changes in dendritic spine size. Unless Ca 2ϩ influx mechanisms and spine volume scale proportionally, changes in spine size will modify spine Ca 2ϩ concentrations during subsequent synaptic activation. We show that the relationship between Ca 2ϩ influx and spine volume is a fundamental determinant of synaptic stability. If Ca 2ϩ influx is undercompensated for increases in spine size, then strong synapses are stabilized and synaptic strength distributions have a single peak. In contrast, overcompensation of Ca 2ϩ influx leads to binary, persistent synaptic strengths with double-peaked distributions. Biophysical simulations predict that CA1 pyramidal neuron spines are undercompensating. This unifies experimental findings that weak synapses are more plastic than strong synapses, that synaptic strengths are unimodally distributed, and that potentiation saturates for a given stimulus strength. We conclude that structural plasticity provides a simple, local, and general mechanism that allows dendritic spines to foster both rapid memory formation and persistent memory storage.

Summary Protein synthesis is crucial for both persistent synaptic plasticity and long-term memory. De novo protein expression can be restricted to specific neurons within a population, and to specific dendrites within a single neuron. Despite its ubiquity, the functional benefits of spatial protein regulation for learning are unknown. We used computational modeling to study this problem. We found that spatially patterned protein synthesis can enable selective consolidation of some memories but forgetting of others, even for simultaneous events that are represented by the same neural population. Key factors regulating selectivity include the functional clustering of synapses on dendrites, and the sparsity and overlap of neural activity patterns at the circuit level. Based on these findings we proposed a novel two-step model for selective memory generalization during REM and slow-wave sleep. The pattern-matching framework we propose may be broadly applicable to spatial protein signaling throughout cortex and hippocampus.