The cerebral cortex is organized so that an important component of feedback input from higher to lower cortical areas arrives at the distal apical tufts of pyramidal neurons. Yet, distal inputs are predicted to have much less impact on firing than proximal inputs. Here we show that even weak asynchronous dendritic input to the distal tuft region can significantly increase the gain of layer 5 pyramidal neurons and thereby the output of columns in the primary somatosensory cortex of the rat. Noisy currents injected in ramps at different dendritic locations showed that the initial slope of the frequency-current (f/I) relationship increases with the distance of the current injection from the soma. The increase was due to the interaction of dendritic depolarization with back-propagating APs which activated dendritic calcium conductances. Gain increases were accompanied by a change of firing mode from isolated spikes to bursting where the timing of bursts coded the presence of coincident somatic and dendritic inputs. We propose that this dendritic gain modulation and the timing of bursts may serve to associate top-down and bottom-up input on different time scales.

We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons).

Sequential neural activity patterns are as ubiquitous as the outputs they drive, which include motor gestures and sequential cognitive processes. Neural sequences are long, compared to the activation durations of participating neurons, and sequence coding is sparse. Numerous studies demonstrate that spike-time-dependent plasticity (STDP), the primary known mechanism for temporal order learning in neurons, cannot organize networks to generate long sequences, raising the question of how such networks are formed. We show that heterosynaptic competition within single neurons, when combined with STDP, organizes networks to generate long unary activity sequences even without sequential training inputs. The network produces a diversity of sequences with a power law length distribution and exponent -1, independent of cellular time constants. We show evidence for a similar distribution of sequence lengths in the recorded premotor song activity of songbirds. These results suggest that neural sequences may be shaped by synaptic constraints and network circuitry rather than cellular time constants.

The computational power of single neurons is greatly enhanced by active dendritic conductances that have a large influence on their spike activity. In cortical output neurons such as the large pyramidal cells of layer 5 (L5), activation of apical dendritic calcium channels leads to plateau potentials that increase the gain of the input/output function and switch the cell to burst-firing mode. The apical dendrites are innervated by local excitatory and inhibitory inputs as well as thalamic and corticocortical projections, which makes it a formidable task to predict how these inputs influence active dendritic properties in vivo. Here we investigate activity in populations of L5 pyramidal dendrites of the somatosensory cortex in awake and anaesthetized rats following sensory stimulation using a new fibre-optic method for recording dendritic calcium changes. We show that the strength of sensory stimulation is encoded in the combined dendritic calcium response of a local population of L5 pyramidal cells in a graded manner. The slope of the stimulus-response function was under the control of a particular subset of inhibitory neurons activated by synaptic inputs predominantly in L5. Recordings from single apical tuft dendrites in vitro showed that activity in L5 pyramidal neurons disynaptically coupled via interneurons directly blocks the initiation of dendritic calcium spikes in neighbouring pyramidal neurons. The results constitute a functional description of a cortical microcircuit in awake animals that relies on the active properties of L5 pyramidal dendrites and their very high sensitivity to inhibition. The microcircuit is organized so that local populations of apical dendrites can adaptively encode bottom-up sensory stimuli linearly across their full dynamic range.

Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic synapses where plasticity is modulated by these three factors. In functional terms, the rule seeks to minimize discrepancies between somatic firings and a local dendritic potential. Such prediction errors can arise in our model from stochastic fluctuations as well as from synaptic input, which directly targets the soma. Depending on the nature of this direct input, our plasticity rule subserves supervised or unsupervised learning. When a reward signal modulates the learning rate, reinforcement learning results. Hence a single plasticity rule supports diverse learning paradigms.