Robust versus optimal strategies for two-alternative forced choice tasks.

It has been proposed that animals and humans might choose a speed-accuracy tradeoff that maximizes reward rate. For this utility function the simple drift-diffusion model of two-alternative forced-choice tasks predicts a parameter-free optimal performance curve that relates normalized decision times to error rates under varying task conditions. However, behavioral data indicate that only ≈ 30% of subjects achieve optimality, and here we investigate the possibility that, in allowing for uncertainties, subjects might exercise robust strategies instead of optimal ones. We consider two strategies in which robustness is achieved by relinquishing performance: maximin and robust-satisficing. The former supposes maximization of guaranteed performance under a presumed level of uncertainty; the latter assumes that subjects require a critical performance level and maximize the level of uncertainty under which it can be guaranteed. These strategies respectively yield performance curves parameterized by presumed uncertainty level and required performance. Maximin performance curves for uncertainties in response-to-stimulus interval match data for the lower-scoring 70% of subjects well, and are more likely to explain it than robust-satisficing or alternative optimal performance curves that emphasize accuracy. For uncertainties in signal-to-noise ratio, neither maximin nor robust-satisficing performance curves adequately describe the data. We discuss implications for decisions under uncertainties, and suggest further behavioral assays.

Do we know how to set decision thresholds for diabetes?

The diagnosis of diabetes, based on measured fasting plasma glucose level, depends on choosing a threshold level for which the probability of failing to detect disease (missed diagnosis), as well as the probability of falsely diagnosing disease (false alarm), are both small. The Bayesian risk provides a tool for aggregating and evaluating the risks of missed diagnosis and false alarm. However, the underlying probability distributions are uncertain, which makes the choice of the decision threshold difficult. We discuss an hypothesis for choosing the threshold that can robustly achieve acceptable risk. Our analysis is based on info-gap decision theory, which is a non-probabilistic methodology for modelling and managing uncertainty. Our hypothesis is that the non-probabilistic method of info-gap robust decision making is able to select decision thresholds according to their probability of success. This hypothesis is motivated by the relationship between info-gap robustness and the probability of success, which has been observed in other disciplines (biology and economics). If true, it provides a valuable clinical tool, enabling the clinician to make reliable diagnostic decisions in the absence of extensive probabilistic information. Specifically, the hypothesis asserts that the physician is able to choose a diagnostic threshold that maximizes the probability of acceptably small Bayesian risk, without requiring accurate knowledge of the underlying probability distributions. The actual value of the Bayesian risk remains uncertain.

Oscillatory neural networks for robotic yo-yo control.

Different networks of coupled oscillators were developed for open-loop control of periodic motion. However, some tasks, like yo-yo playing, are open-loop unstable and require proper phase locking to stabilize. Given the phase-locking property of coupled oscillators, we investigate their application to closed-loop control of open-loop unstable systems, concentrating on the challenging task of yo-yo control. In particular, we focus on pulse-coupling, where the yo-yo sends a feedback upon reaching the bottom of the string and the onset of the oscillatory cycle is used to trigger the movement. Four networks involving either a stand-alone or a circuit level oscillator with either excitatory or inhibitory couplings are considered. Working curve analysis indicates that three of the networks cannot stabilize the yo-yo. The fourth network, which is based on a circuit-level oscillator, is analyzed using the return map and the region of stability is determined and verified by simulations. The resulting pulse-coupled oscillatory control provides a model-free control strategy that operates with an easy-to-measure low-rate feedback.

Sensitivity of basic oscillatory mechanisms for pattern generation and detection.

Intrinsic oscillators are the basic building blocks of central pattern generators, which model the neural circuits underlying pattern generation. Coupled intrinsic oscillators have been shown to synchronize their oscillatory frequencies and to maintain a characteristic pattern of phase relationships. Recently, oscillatory neurons have also been identified in sensory systems that are involved in decoding phase information. It has been hypothesized that the neural oscillators are part of neural circuits that implement phase-locked loops (PLLs), which are well-known electrical circuits for temporal decoding. Thus, there is evidence that intrinsic neural oscillators participate in both temporal pattern generation and temporal pattern decoding. The present paper investigates the dynamics underlying forced oscillators and forced PLLs, using a single framework, and compares both their stability and sensitivity characteristics. In particular, a method for assessing whether an oscillatory neuron is forced directly or indirectly, as part of a PLL, is developed and applied to published data.

Single-neuron modeling of LSO unit responses.

We investigated, using a computational model, the biophysical correlates of measured discharge patterns of lateral superior olive (LSO) neuron responses to monaural and binaural stimuli. The model's geometry was based on morphological data, and static electric properties of the model agree with available intracellular responses to hyperpolarizing current pulses. Inhibitory synapses were located on the soma and excitatory ones on the dendrites, which were modeled as passive cables. The active properties of the model were adjusted to agree with statistical measures derived from extracellular recordings. Calcium-dependent potassium channels supplemented the usual Hodgkin-Huxley characterization for the soma to produce observed serial interspike interval dependence characteristics. Intracellular calcium concentration is controlled by voltage- and calcium-dependent potassium channels and by calcium diffusion and homeostatic mechanisms. By adjusting the density of the calcium-dependent potassium channels, we could span the observed range of transient response patterns found in different LSO neurons. Inputs from the two ears were modeled as Poisson processes to describe the responses to tone-burst stimuli. Transient and sustained responses to monaural and binaural tone-burst stimuli over a wide range of stimulus conditions could be well described by varying only the model's inputs. As found in recordings, model responses having similar discharge rates but different binaural stimulus combinations exhibited differences in interval statistics.

Decoding temporally encoded sensory input by cortical oscillations and thalamic phase comparators.

The temporally encoded information obtained by vibrissal touch could be decoded "passively," involving only input-driven elements, or "actively," utilizing intrinsically driven oscillators. A previous study suggested that the trigeminal somatosensory system of rats does not obey the bottom-up order of activation predicted by passive decoding. Thus, we have tested whether this system obeys the predictions of active decoding. We have studied cortical single units in the somatosensory cortices of anesthetized rats and guinea pigs and found that about a quarter of them exhibit clear spontaneous oscillations, many of them around whisking frequencies ( approximately 10 Hz). The frequencies of these oscillations could be controlled locally by glutamate. These oscillations could be forced to track the frequency of induced rhythmic whisker movements at a stable, frequency-dependent, phase difference. During these stimulations, the response intensities of multiunits at the thalamic recipient layers of the cortex decreased, and their latencies increased, with increasing input frequency. These observations are consistent with thalamocortical loops implementing phase-locked loops, circuits that are most efficient in decoding temporally encoded information like that obtained by active vibrissal touch. According to this model, and consistent with our results, populations of thalamic "relay" neurons function as phase "comparators" that compare cortical timing expectations with the actual input timing and represent the difference by their population output rate.

Transient effects during the chopping response of LSO neurons.

The initial transient chopping response of LSO neuron discharges to both monaural and binaural tone-burst stimuli in the context of a previously developed point process model of the later sustained response is analyzed and modeled. The analysis reveals the nature of the initial transient response to stimulus onset: The model's stimulus-dependent parameters vary with poststimulus-onset time while the neuron's intrinsic recovery characteristics remain constant throughout the response. By applying maximum-likelihood estimation techniques to determine the time course of the stimulus-dependent parameters, it was found that the initial excitatory and inhibitory effects decay exponentially, with their ratio determining the instantaneous rate of firing and their relative latency determining the extent of the initial chopping pattern. The "absolute" and apparent deadtime also vary exponentially during the transient portion of the response. It is concluded that the recovery characteristics of LSO neurons and, the exponential nature of the transient effects give rise to a tightly distributed latency period and a regular chopping response pattern that could encode azimuthal information.

Excitation effects on LSO unit sustained responses: point process characterization.

LSO units recover from a spike discharge in a characteristic way, modeled by an intrinsic recovery function that is stimulus invariant up to a scaling factor and a shifting constant. Data analysis shows that the effect of increasing excitatory stimulus level can be described by amplifying the intrinsic recovery function and by shifting it toward shorter intervals. The shifting process secondarily interacts with the absolute deadtime to produce the response characteristics of the three LSO unit types. Decreased excitation is clearly distinguished from inhibition, which affects the scaling, but not the time origin, of the recovery. We conclude that both excitatory and inhibitory stimulus levels are encoded in the timing of LSO unit discharges.

Excitatory/inhibitory interaction in the LSO revealed by point process modeling.

We studied lateral superior olivary (LSO) unit responses to binaural tone-bursts using a general point process approach. We show that inhibition of the ipsilaterally elicited response by contralateral stimulation cannot be modeled simply as a reduction of the ipsilateral input. Statistical analyses reveal that inhibition operates by scaling the intensity of the point process describing the ipsilateral response. In some cases the scaling process has secondary effects: Binaurally elicited discharges produce bimodal interspike interval histograms from units that produce unimodal interval histograms under monaural stimulation. We present a specific point process model that describes the scaling process and successfully replicates the observed responses to monaural and binaural stimulation of the three types of LSO units: slow choppers, fast choppers, and bimodal units. We interpret scaling as a shunting inhibitory process in these LSO neurons. By relating scaling magnitude to interaural level difference, we demonstrate the spatial sensitivity of LSO units.