The hippocampal CA3 region plays a key role in learning and memory. Recurrent CA3-CA3 synapses are thought to be the subcellular substrate of pattern completion. However, the synaptic mechanisms of this network computation remain enigmatic. To investigate these mechanisms, we combined functional connectivity analysis with network modeling.Simultaneous recording from up to eight CA3 pyramidal neurons revealed that connectivity was sparse, spatially uniform, and highly enriched in disynaptic motifs (reciprocal, convergence, divergence, and chain motifs). Unitary connections were comprised of one or two synaptic contacts, suggesting efficient use of postsynaptic space. Real-size modeling indicated that CA3 networks with sparse connectivity, disynaptic motifs, and single-contact connections robustly generated pattern completion. Thus, macro-and microconnectivity contribute to efficient memory storage and retrieval in hippocampal networks. 3The hippocampal CA3 region plays a key role in learning and memory (1)(2)(3)(4)(5). A hallmark property of the network is its ability to retrieve patterns from partial or noisy cues, a process referred to as autoassociative recall, attractor dynamics, or pattern completion (3-7). However, the synaptic mechanisms underlying pattern completion have remained enigmatic. Previous neuronal network models suggested that recurrent CA3-CA3 pyramidal cell synapses play a key role in this process (8)(9)(10)(11)(12)(13)(14). In the storage phase, a stimulus pattern will activate an ensemble of interconnected neurons and induce synaptic potentiation in the corresponding recurrent synapses. In the recall phase, a partial pattern will initially activate only a fraction of the ensemble, but subsequently recruit the remaining cells via potentiated synapses. Successful pattern completion requires sufficient synaptic efficacy and network connectivity (12, 14). Whether the biological properties of the CA3 network are consistent with these assumptions remains unclear. Analysis of functional connectivity in the CA3 networkThe CA3 network is often envisaged as a network of highly interconnected neurons (3-5, 8, 11). To test this hypothesis, we analyzed functional connectivity by simultaneous recordings from up to eight CA3 pyramidal neurons in rat brain in vitro, followed by selective biocytin labeling ( 4 Macroconnectivity in the CA3 networkOur results suggested that connectivity in the CA3 cell network was surprisingly sparse, with a mean connection probability of 0.92%. Both experimental data and simulations using fully reconstructed CA3 neurons labeled in vivo indicated that connectivity was only moderately dependent on slice orientation (materials and methods; fig. S3). However, connectivity may decline with distance (17).Furthermore, connectivity might be non-random, with ensembles of highly connected cells embedded in a sparsely connected population (18, 19). To test these hypotheses, we first examined whether the connection probability was dependent on intersomatic distance (Fig. 2A). The ave...

CA3–CA3 recurrent excitatory synapses are thought to play a key role in memory storage and pattern completion. Whether the plasticity properties of these synapses are consistent with their proposed network functions remains unclear. Here, we examine the properties of spike timing-dependent plasticity (STDP) at CA3–CA3 synapses. Low-frequency pairing of excitatory postsynaptic potentials (EPSPs) and action potentials (APs) induces long-term potentiation (LTP), independent of temporal order. The STDP curve is symmetric and broad (half-width ∼150 ms). Consistent with these STDP induction properties, AP–EPSP sequences lead to supralinear summation of spine [Ca2+] transients. Furthermore, afterdepolarizations (ADPs) following APs efficiently propagate into dendrites of CA3 pyramidal neurons, and EPSPs summate with dendritic ADPs. In autoassociative network models, storage and recall are more robust with symmetric than with asymmetric STDP rules. Thus, a specialized STDP induction rule allows reliable storage and recall of information in the hippocampal CA3 network.

Parvalbumin-positive (PV+) GABAergic interneurons in hippocampal microcircuits are thought to play a key role in several higher network functions, such as feedforward and feedback inhibition, network oscillations, and pattern separation. Fast lateral inhibition mediated by GABAergic interneurons may implement a winner-takes-all mechanism in the hippocampal input layer. However, it is not clear whether the functional connectivity rules of granule cells (GCs) and interneurons in the dentate gyrus are consistent with such a mechanism. Using simultaneous patch-clamp recordings from up to seven GCs and up to four PV+ interneurons in the dentate gyrus, we find that connectivity is structured in space, synapse-specific, and enriched in specific disynaptic motifs. In contrast to the neocortex, lateral inhibition in the dentate gyrus (in which a GC inhibits neighboring GCs via a PV+ interneuron) is ~ 10-times more abundant than recurrent inhibition (in which a GC inhibits itself). Thus, unique connectivity rules may enable the dentate gyrus to perform specific higher-order computations.

CA3 pyramidal neurons are important for memory formation and pattern completion in the hippocampal network. It is generally thought that proximal synapses from the mossy fibers activate these neurons most efficiently, whereas distal inputs from the perforant path have a weaker modulatory influence. We used confocally targeted patch-clamp recording from dendrites and axons to map the activation of rat CA3 pyramidal neurons at the subcellular level. Our results reveal two distinct dendritic domains. In the proximal domain, action potentials initiated in the axon backpropagate actively with large amplitude and fast time course. In the distal domain, Na + channel-mediated dendritic spikes are efficiently initiated by waveforms mimicking synaptic events. CA3 pyramidal neuron dendrites showed a high Na + -to-K + conductance density ratio, providing ideal conditions for active backpropagation and dendritic spike initiation. Dendritic spikes may enhance the computational power of CA3 pyramidal neurons in the hippocampal network.CA3 pyramidal neurons in the hippocampal network are critical for spatial information processing and memory 1-5 . These neurons receive three different glutamatergic inputs. Proximal mossy fiber synapses activate CA3 cells efficiently, acting as 'conditional detonators' 6,7 . Commissural/associational synapses between CA3 cells are thought to store memories by spike timing-dependent plasticity, but whether backpropagated action potentials efficiently invade the postsynaptic dendrites in CA3 pyramidal neurons is unclear [8][9][10] . Distal perforant path synapses from the entorhinal cortex may relay information about context 2 , but how synaptic signals are conducted to the soma via the long dendritic cable has not been resolved. Recent results have suggested that most entorhinal cortex layer 2 pyramidal neurons are grid cells 11 , indicating that perforant path inputs may signal precise spatiotemporal information. How this information is processed by CA3 pyramidal cell dendrites remains unclear.To understand both the induction rules of synaptic plasticity and the efficacy of distal inputs in CA3 pyramidal neurons, knowledge about the properties of the dendrites of these neurons is essential. Highly detailed information is available about the dendrites of layer 5 pyramidal cells in the neocortex and CA1 pyramidal neurons in the hippocampus 12-15 (reviewed by ref. 16). In contrast, both the difficulty of maintaining CA3 pyramidal cells in in vitro slice preparations and the small caliber of the dendritic processes of these cells have prevented a detailed analysis by direct recordings. Recent experiments using glutamate uncaging have

Intracellular electrophysiological recordings provide crucial insights into elementary neuronal signals such as action potentials and synaptic currents. Analyzing and interpreting these signals is essential for a quantitative understanding of neuronal information processing, and requires both fast data visualization and ready access to complex analysis routines. To achieve this goal, we have developed Stimfit, a free software package for cellular neurophysiology with a Python scripting interface and a built-in Python shell. The program supports most standard file formats for cellular neurophysiology and other biomedical signals through the Biosig library. To quantify and interpret the activity of single neurons and communication between neurons, the program includes algorithms to characterize the kinetics of presynaptic action potentials and postsynaptic currents, estimate latencies between pre- and postsynaptic events, and detect spontaneously occurring events. We validate and benchmark these algorithms, give estimation errors, and provide sample use cases, showing that Stimfit represents an efficient, accessible and extensible way to accurately analyze and interpret neuronal signals.