Michael R. DeWeese

Efficient Discrimination of Temporal Patterns by Motion-Sensitive Neurons in Primate Visual Cortex

Although motion-sensitive neurons in macaque middle temporal (MT) area are conventionally characterized using stimuli whose velocity remains constant for 1-3 s, many ecologically relevant stimuli change on a shorter time scale (30-300 ms). We compared neuronal responses to conventional (constant-velocity) and time-varying stimuli in alert primates. The responses to both stimulus ensembles were well described as rate-modulated Poisson processes but with very high precision (approximately 3 ms) modulation functions underlying the time-varying responses. Information-theoretic analysis revealed that the responses encoded only approximately 1 bit/s about constant-velocity stimuli but up to 29 bits/s about the time-varying stimuli. Analysis of local field potentials revealed that part of the residual response variability arose from noise sources extrinsic to the neuron. Our results demonstrate that extrastriate neurons in alert primates can encode the fine temporal structure of visual stimuli.

Asymmetric dynamics in optimal variance adaptation

It has long been recognized that sensory systems adapt to their inputs. Here we formulate the problem of optimal variance estimation for a broad class of nonstationary signals. We show that under weak assumptions, the Bayesian optimal causal variance estimate shows asymmetric dynamics: an abrupt increase in variance is more readily detectable than an abrupt decrease. By contrast, optimal adaptation to the mean displays symmetric dynamics when the variance is held fixed. After providing several empirical examples and a simple intuitive argument for our main result, we prove that optimal adaptation is asymmetrical in a broad class of model environments. This observation makes specific and falsifiable predictions about the time course of adaptation in neurons probed with certain stimulus ensembles.

Optimization principles for the neural code

Recent experiments show that the neural codes at work in a wide range of creatures share some common features. At first sight, these observations seem unrelated. However, we show that these features arise naturally in a linear filtered threshold crossing model when we set the threshold to maximize the transmitted information. This maximization process requires neural adaptation to not only the DC signal level, as in conventional light and dark adaptation, but also to the statistical structure of the signal and noise distributions. We also present a new approach for calculating the mutual information between a neurons output spike train and any aspect of its input signal which does not require reconstruction of the input signal. This formulation is valid provided the correlations in the spike train are small, and we provide a procedure for checking this assumption. This paper is based on joint work (DeWeese M 1995 Optimization principles for the neural code, Dissertation, Princeton University). Preliminary results from the linear filtered threshold crossing model appeared in a previous proceedings (DeWeese M and Bialek W 1995 Information flow in sensory neurons, Nuovo Cimento D 17 733-8), and the conclusions we reached at that time have been reaffirmed by further analysis of the model.

Reliability and Representational Bandwidth in the Auditory Cortex

It is unclear why there are so many more neurons in sensory cortex than in the sensory periphery. One possibility is that these extra neurons are used to overcome cortical noise and faithfully represent the acoustic stimulus. Another possibility is that even after overcoming cortical noise, there is excess representational bandwidth available and that this bandwidth is used to represent conjunctions of auditory and nonauditory information for computation. Here, we discuss recent data about neuronal reliability in auditory cortex showing that cortical noise may not be as high as was previously believed. Although at present, the data suggest that auditory cortex neurons can be more reliable than those in the visual cortex, we speculate that the principles governing cortical computation are universal and that visual and other cortical areas can also exploit strategies based on similarly high-fidelity activity.

Whole-cell recording in vivo

In vivo whole-cell patch-clamp recording provides a means for measuring membrane currents and potentials from individual cells in the intact animal. Patch-clamp methods have largely been developed in vitro. This body of work has contributed enormously to the understanding of many important phenomena in excitable cells--including synaptic plasticity in the mammalian central nervous system, and the behavior of individual protein channels. In recent years, an increasing number of groups have applied whole-cell recording techniques in the intact animal. Such in vivo studies offer the tantalizing possibility of uncovering the underlying principles and mechanisms of neural interactions within the natural context of fully intact biological networks. This unit focuses on strategies for overcoming the specific technical challenges posed by in vivo whole-cell recording. A straightforward procedure is described for obtaining whole-cell records from the cortex of the anesthetized rat; this procedure has also been applied successfully to awake animals and other rodent species with minor modifications.In vivo whole‐cell patch‐clamp recording provides a means for measuring membrane currents and potentials from individual cells in the intact animal. Patch‐clamp methods have largely been developed in vitro. This body of work has contributed enormously to the understanding of many important phenomena in excitable cells—including synaptic plasticity in the mammalian central nervous system, and the behavior of individual protein channels. In recent years, an increasing number of groups have applied whole‐cell recording techniques in the intact animal. Such in vivo studies offer the tantalizing possibility of uncovering the underlying principles and mechanisms of neural interactions within the natural context of fully intact biological networks. This unit focuses on strategies for overcoming the specific technical challenges posed by in vivo whole‐cell recording. A straightforward procedure is described for obtaining whole‐cell records from the cortex of the anesthetized rat; this procedure has also been applied successfully to awake animals and other rodent species with minor modifications.

Neurobiology: efficiency measures.

The nervous system translates sensory information into electrical impulses. The neural ‘code’ involved seems to represent natural sounds and images efficiently, using the smallest number of impulses.

Neural gallops across auditory streams.

We continually rely on our ability to segregate the myriad sounds in our environment--phones ringing, people talking--into separate auditory streams, each originating from a different source. In this issue of Neuron, Micheyl et al. provide the most direct evidence to date linking single-unit spiking responses from auditory cortex to the perception of distinct auditory streams.

An optimal preparation for studying optimization

Imagine a fly navigating through a forest at 2 m/s. In order to correct for the effects of the wind and other flight instabilities, the fly must continually estimate its heading direction if only to avoid running into a tree or inadvertently flying in circles. Given the striking prominence of eyes on a flys body, it is not surprising that vision plays a key role in many of its behaviors, including flight (Egelhaaf and Borst 1993). In fact, when a fly is suspended from a wire inside a rotating drum so that the visual scene in front of the fly moves to the right, the fly uses its wings to turn its body to the right, presumably to try to maintain what it perceives as its current heading direction (Reichardt and Poggio 1976). However, flies do not exhibit this behavior following lesions to subsets of the 50 or so identified neurons in the lobular plate that respond to wide-field visual motion (Hausen and Wehrhahn 1983). The fact that these neurons are involved in stabilizing heading direction, which is critical for chasing potential mating partners and avoiding obstacles, suggests that there has probably been strong evolutionary pressure on their performance. One of these neurons, H1, is particularly accessible experimentally, allowing stable extracellular recordings for hours and even days.