No abstract

Almost all of the processing that occurs in the various lower auditory nuclei converges upon a common target in the central nucleus of the inferior colliculus (ICc) thus making the ICc the nexus of the auditory system. A variety of new response properties are formed in the ICc through the interactions among the excitatory and inhibitory inputs that converge upon it. Here we review studies that illustrate the dominant role inhibition plays in the ICc. We begin by reviewing studies of tuning curves and show how inhibition shapes the variety of tuning curves in the ICc through sideband inhibition. We then show how inhibition shapes selective response properties for complex signals, focusing on selectivity for the sweep direction of frequency modulations (FM). In the final section we consider results from in vivo whole-cell recordings that show how parameters of the incoming excitation and inhibition interact to shape directional selectivity. We show that post-synaptic potentials (PSPs) evoked by different signals can be similar but evoke markedly different spike-counts. In these cases, spike threshold acts as a non-linear amplifier that converts small differences in PSPs into large differences in spike output. Such differences between the inputs to a cell compared to the outputs from the same cell suggest that highly selective discharge properties can be created by only minor adjustments in the synaptic strengths evoked by one or both signals. These findings also suggest that plasticity of response features may be achieved with far less modifications in circuitry than previously supposed.

Here we study the neural computations performed by neurons in the auditory system to be selective for the direction and velocity of signals sweeping upward or downward in frequency, termed spectral motion. We show that neurons in the auditory midbrain of Mexican free-tailed bats encode multiple spectrotemporal features of natural communication sounds. These features to which each neuron is tuned are nonlinearly combined to produce selectivity for spectral motion cues present in their conspecific calls, such as direction and velocity. We find that the neural computations resulting in selectivity for spectral motion are analogous to models of motion-selectivity studied in vision. Our analysis revealed that auditory neurons in the inferior colliculus (IC) are avoiding spectrotemporal modulations that are redundant across different bat communication signals and are specifically tuned for modulations that distinguish each call from another by their frequency-modulated direction and velocity, suggesting that spectral motion is the neural computation through which IC neurons are encoding specific features of conspecific vocalizations.

Cortical spontaneous activity reflects an animal’s behavioral state and affects neural responses to sensory stimuli. The correlation between excitatory and inhibitory synaptic input to single neurons is a key parameter in models of cortical circuitry. Recent measurements demonstrated highly correlated synaptic excitation and inhibition during spontaneous “up-and-down” states, during which excitation accounted for approximately 80% of inhibitory variance (Shu et al., 2003; Haider et al., 2006). Here we report in vivo whole-cell estimates of the correlation between excitation and inhibition in the rat visual cortex under pentobarbital anesthesia, during which up-and-down states are absent. Excitation and inhibition are weakly correlated, relative to the up-and-down state: excitation accounts for less than 40% of inhibitory variance. Although these correlations are lower than when the circuit cycles between up-and-down states, both behaviors may arise from the same circuitry. Our observations provide evidence that different correlational patterns of excitation and inhibition underlie different cortical states.

A single biological neuron is able to perform complex computations that are highly nonlinear in nature, adaptive, and superior to the perceptron model. A neuron is essentially a nonlinear dynamical system. Its state depends on the interactions among its previous states, its intrinsic properties, and the synaptic input it receives. These factors are included in Hodgkin-Huxley (HH) model, which describes the ionic mechanisms involved in the generation of an action potential. This paper proposes training of an artificial neural network to identify and model the physiological properties of a biological neuron, and mimic its input-output mapping. An HH simulator was implemented to generate the training data. The proposed model was able to mimic and predict the dynamic behavior of the HH simulator under novel stimulation conditions; hence, it can be used to extract the dynamics (in vivo or in vitro) of a neuron without any prior knowledge of its physiology. Such a model can in turn be used as a tool for controlling a neuron in order to study its dynamics for further analysis.