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).

In the intact brain neurons are constantly exposed to intense synaptic activity. This heavy barrage of excitatory and inhibitory inputs was recreated in vitro by injecting a noisy current, generated as an Ornstein–Uhlenbeck process, into the soma of rat neocortical pyramidal cells. The response to such in vivo–like currents was studied systematically by analyzing the time development of the instantaneous spike frequency, and when possible, the stationary mean spike frequency as a function of both the mean and the variance of the input current. All cells responded with an in vivo–like action potential activity with stationary statistics that could be sustained throughout long stimulation intervals (tens of seconds), provided the frequencies were not too high. The temporal evolution of the response revealed the presence of mechanisms of fast and slow spike frequency adaptation, and a medium duration mechanism of facilitation. For strong input currents, the slow adaptation mechanism made the spike frequency response nonstationary. The minimal frequencies that caused strong slow adaptation (a decrease in the spike rate by more than 1 Hz/s), were in the range 30–80 Hz and depended on the pipette solution used. The stationary response function has been fitted by two simple models of integrate-and-fire neurons endowed with a frequency-dependent modification of the input current. This accounts for all the fast and slow mechanisms of adaptation and facilitation that determine the stationary response, and proved necessary to fit the model to the experimental data. The coefficient of variability of the interspike interval was also in part captured by the model neurons, by tuning the parameters of the model to match the mean spike frequencies only. We conclude that the integrate-and-fire model with spike-frequency–dependent adaptation/facilitation is an adequate model reduction of cortical cells when the mean spike-frequency response to in vivo–like currents with stationary statistics is considered.