Background-The neocortex is the most common target of sub-dural electrotherapy and noninvasive brain stimulation modalities including transcranial magnetic stimulation (TMS) and transcranial current simulation (TCS). Specific neuronal elements targeted by cortical stimulation are considered to underlie therapeutic effects, but the exact cell-type(s) affected by these methods remains poorly understood.

Previous experimental studies of both cortical barrel and thalamic barreloid neuron responses in rodent somatosensory cortex have indicated an active role for barrel circuitry in processing thalamic signals. Previous modeling studies of the same system have suggested that a major function of the barrel circuit is to render the response magnitude of barrel neurons particularly sensitive to the temporal distribution of thalamic input. Specifically, thalamic inputs that are initially synchronous strongly engage recurrent excitatory connections in the barrel and generate a response that briefly withstands the strong damping effects of inhibitory circuitry. To test this experimentally, we recorded responses from 40 cortical barrel neurons and 63 thalamic barreloid neurons evoked by whisker deflections varying in velocity and amplitude. This stimulus evoked thalamic response profiles that varied in terms of both their magnitude and timing. The magnitude of the thalamic population response, measured as the average number of evoked spikes per stimulus, increased with both deflection velocity and amplitude. On the other hand, the degree of initial synchrony, measured from population peristimulus time histograms, was highly correlated with the velocity of whisker deflection, deflection amplitude having little or no effect on thalamic synchrony. Consistent with the predictions of the model, the cortical population response was determined largely by whisker velocity and was highly correlated with the degree of initial synchrony among thalamic neurons (R(2) = 0.91), as compared with the average number of evoked thalamic spikes (R(2) = 0.38). Individually, the response of nearly all cortical cells displayed a positive correlation with deflection velocity; this homogeneity is consistent with the dependence of the cortical response on local circuit interactions as proposed by the model. By contrast, the response of individual thalamic neurons varied widely. These findings validate the predictions of the modeling studies and, more importantly, demonstrate that the mechanism by which the cortex processes an afferent signal is inextricably linked with, and in fact determines, the saliency of neural codes embedded in the thalamic response.

An important role for the neural extracellular matrix in modulating cortical activity-dependent synaptic plasticity has been established by a number of recent studies. However, identification of the critical molecular components of the neural matrix that mediate these processes is far from complete. Of particular interest is the perineuronal net (PN), an extracellular matrix component found surrounding the cell body and proximal neurites of a subset of neurons. Because of the apposition of the PN to synapses and expression of this structure coincident with the close of the critical period, it has been hypothesized that nets could play uniquely important roles in synapse stabilization and maturation. Interestingly, previous work has also shown that expression of PNs is dependent on appropriate sensory stimulation in the visual system. Here, we investigated whether PNs in the mouse barrel cortex are expressed in an activity-dependent manner by manipulating sensory input through whisker trimming. Importantly, this manipulation did not lead to a global loss of PNs but instead led to a specific decrease in PNs, detected with the antibody Cat-315, in layer IV of the barrel cortex. In addition, we identified a key activity-regulated component of PNs is the proteoglycan aggrecan. We also demonstrate that these Cat-315-positive neurons virtually all also express parvalbumin. Together, these data are in support of an important role for aggrecan in the activity-dependent formation of PNs on parvalbumin-expressing cells and suggest a role for expression of these nets in regulating the close of the critical period.

Neocortical neurons in awake, behaving animals can generate high-frequency (>300 Hz) bursts of action potentials, either in single bursts or in a repetitive manner. Intracellular recordings of layer II/III pyramidal neurons were obtained from adult ferret visual cortical slices maintained in vitro to investigate the ionic mechanisms by which a subgroup of these cells generates repetitive, high-frequency burst discharges, a pattern referred to as "chattering." The generation of each but the first action potential in a burst was dependent on the critical interplay between the afterhyperpolarizations (AHPs) and afterdepolarizations (ADPs) that followed each action potential. The spike-afterdepolarization and the generation of action potential bursts were dependent on Na(+), but not Ca(2+), currents. Neither blocking of the transmembrane flow of Ca(2+) nor the intracellular chelation of free Ca(2+) with BAPTA inhibited the generation of intrinsic bursts. In contrast, decreasing the extracellular Na(+) concentration or pharmacologically blocking Na(+) currents with tetrodotoxin, QX-314, or phenytoin inhibited bursting before inhibiting action potential generation. Additionally, a subset of layer II/III pyramidal neurons could be induced to switch from repetitive single spiking to a burst-firing mode by constant depolarizing current injection, by raising extracellular K(+) concentrations, or by potentiation of the persistent Na(+) current with the Na(+) channel toxin ATX II. These results indicate that cortical neurons may dynamically regulate their pattern of action potential generation through control of Na(+) and K(+) currents. The generation of high-frequency burst discharges may strongly influence the response of postsynaptic neurons and the operation of local cortical networks.

In the whisker-barrel system, layer IV excitatory neurons respond preferentially to high-velocity deflections of their principal whisker, and these responses are inhibited by deflections of adjacent whiskers. Thalamic input neurons are amplitude and velocity sensitive and have larger excitatory and weaker inhibitory receptive fields than cortical neurons. Computational models based on known features of barrel circuitry capture these and other differences between thalamic and cortical neuron response properties. The models' responses are highly sensitive to thalamic firing synchrony, a finding subsequently confirmed in real barrels by in vivo experiments. Here, we use dynamic systems analysis to examine how barrel circuitry attains its sensitivity to input timing, and how this sensitivity explains the transformation of receptive fields between thalamus and cortex. We find that strong inhibition renders the net effect of intracortical connections suppressive or damping, distinguishing it from previous amplifying models of cortical microcircuits. In damping circuits, recurrent excitation enhances response tuning not by amplifying responses to preferred inputs, but by enabling them to better withstand strong inhibitory influences. Dense interconnections among barrel neurons result in considerable response homogeneity. Neurons outside the barrel layer respond more heterogeneously, possibly reflecting diverse networks and multiple transformations within the cortical output layers. What Do Barrels Do?Thalamocortical Response Transformation Layer IV of rodent somatosensory cortex contains anatomically distinct neuronal aggregates, called barrels, that correspond in one-to-one fashion with individual whiskers on the rat's face (Woolsey and Van der Loos, 1970;Welker 1971). Barrels contain at least two principal neuronal populations, excitator y spiny neurons and inhibitory smooth neurons. The two populations are synaptically connected reciprocally to each other and recurrently to themselves. Both receive afferent input from thalamocortical neurons (White, 1989;Keller, 1995). Neurons within a barrel are also related functionally in that each responds