Network and neuronal membrane properties in hybrid networks reciprocally regulate selectivity to rapid thalamocortical inputs.

Rapidly changing environments require rapid processing from sensory inputs. Varying deflection velocities of a rodent's primary facial vibrissa cause varying temporal neuronal activity profiles within the ventral posteromedial thalamic nucleus. Local neuron populations in a single somatosensory layer 4 barrel transform sparsely coded input into a spike count based on the input's temporal profile. We investigate this transformation by creating a barrel-like hybrid network with whole cell recordings of in vitro neurons from a cortical slice preparation, embedding the biological neuron in the simulated network by presenting virtual synaptic conductances via a conductance clamp. Utilizing the hybrid network, we examine the reciprocal network properties (local excitatory and inhibitory synaptic convergence) and neuronal membrane properties (input resistance) by altering the barrel population response to diverse thalamic input. In the presence of local network input, neurons are more selective to thalamic input timing; this arises from strong feedforward inhibition. Strongly inhibitory (damping) network regimes are more selective to timing and less selective to the magnitude of input but require stronger initial input. Input selectivity relies heavily on the different membrane properties of excitatory and inhibitory neurons. When inhibitory and excitatory neurons had identical membrane properties, the sensitivity of in vitro neurons to temporal vs. magnitude features of input was substantially reduced. Increasing the mean leak conductance of the inhibitory cells decreased the network's temporal sensitivity, whereas increasing excitatory leak conductance enhanced magnitude sensitivity. Local network synapses are essential in shaping thalamic input, and differing membrane properties of functional classes reciprocally modulate this effect.

Response sensitivity of barrel neuron subpopulations to simulated thalamic input.

Our goal is to examine the relationship between neuron- and network-level processing in the context of a well-studied cortical function, the processing of thalamic input by whisker-barrel circuits in rodent neocortex. Here we focus on neuron-level processing and investigate the responses of excitatory and inhibitory barrel neurons to simulated thalamic inputs applied using the dynamic clamp method in brain slices. Simulated inputs are modeled after real thalamic inputs recorded in vivo in response to brief whisker deflections. Our results suggest that inhibitory neurons require more input to reach firing threshold, but then fire earlier, with less variability, and respond to a broader range of inputs than do excitatory neurons. Differences in the responses of barrel neuron subtypes depend on their intrinsic membrane properties. Neurons with a low input resistance require more input to reach threshold but then fire earlier than neurons with a higher input resistance, regardless of the neuron's classification. Our results also suggest that the response properties of excitatory versus inhibitory barrel neurons are consistent with the response sensitivities of the ensemble barrel network. The short response latency of inhibitory neurons may serve to suppress ensemble barrel responses to asynchronous thalamic input. Correspondingly, whereas neurons acting as part of the barrel circuit in vivo are highly selective for temporally correlated thalamic input, excitatory barrel neurons acting alone in vitro are less so. These data suggest that network-level processing of thalamic input in barrel cortex depends on neuron-level processing of the same input by excitatory and inhibitory barrel neurons.

Response properties of single units in the dorsal nucleus of the lateral lemniscus of decerebrate cats.

The dorsal nucleus of the lateral lemniscus (DNLL) receives afferent inputs from many brain stem nuclei and, in turn, is a major source of inhibitory inputs to the inferior colliculus (IC). The goal of this study was to characterize the monaural and binaural response properties of neurons in the DNLL of unanesthetized decerebrate cat. Monaural responses were classified according to the patterns of excitation and inhibition observed in contralateral and ipsilateral frequency response maps. Binaural classification was based on unit sensitivity to interaural level differences. The results show that units in the DNLL can be grouped into three distinct types. Type v units produce contralateral response maps that show a wide V-shaped excitatory area and no inhibition. These units receive ipsilateral excitation and exhibit binaural facilitation. The contralateral maps of type i units show a more restricted I-shaped region of excitation that is flanked by inhibition. Type o maps display an O-shaped island of excitation at low stimulus levels that is bounded by inhibition at higher levels. Both type i and type o units receive ipsilateral inhibition and exhibit binaural inhibition. Units that produce type v maps have a low best frequency (BF), whereas type i and type o units have high BFs. Type v and type i units give monotonic rate-level responses for both BF tones and broadband noise. Type o units are inhibited by tones at high levels, but are excited by high-level noise. These results show that the DNLL can exert strong, differential effects in the IC.

Transfer of learned perception of sensorimotor simultaneity.

Synchronizing a motor response to a predictable sensory stimulus, like a periodic flash or click, relies on feedback (somesthetic, auditory, visual, or other) from the motor response. Practically, this results in a small (<50 ms) asynchrony in which the motor response leads the sensory event. Here we show that the perceived simultaneity in a coincidence-anticipation task (line crossing) is affected by changing the perceived simultaneity in a different task (pacing). In the pace task, human subjects were instructed to press a key in perfect synchrony with a red square flashed every second. In training sessions, feedback was provided by flashing a blue square with each key press, below the red square. There were two types of training pace sessions: one in which the feedback was provided with no delay, the other (adapting), in which the feedback was progressively delayed (up to 100 ms). Subjects' asynchrony was unchanged in the first case, but it was significantly increased in the pace task with delay. In the coincidence-anticipation task, a horizontally moving vertical bar crossed a vertical line in the middle of a screen. Subjects were instructed to press a key exactly when the bar crossed the line. They were given no feedback on their performance. Asynchrony on the line-crossing task was tested after the training pace task with feedback. We found that this asynchrony to be significantly increased even though there never was any feedback on the coincidence-anticipation task itself. Subjects were not aware that their sensorimotor asynchrony had been lengthened (sometimes doubled). We conclude that perception of simultaneity in a sensorimotor task is learned. If this perception is caused by coincidence of signals in the brain, the timing of these signals depends on something-acquired by experience-more adaptable than physiological latencies.