Heterogeneous orientation tuning in the primary visual cortex of mice diverges from Gabor-like receptive fields in primates.

A key feature of neurons in the primary visual cortex (V1) of primates is their orientation selectivity. Recent studies using deep neural network models showed that the most exciting input (MEI) for mouse V1 neurons exhibit complex spatial structures that predict non-uniform orientation selectivity across the receptive field (RF), in contrast to the classical Gabor filter model. Using local patches of drifting gratings, we identified heterogeneous orientation tuning in mouse V1 that varied up to 90° across sub-regions of the RF.

The Nested States Model: A Phenomenologically-Grounded Model of the Mind.

Background: Subjective experience is central to the nature of mental illness, yet it has not played a central role in most empirical approaches to psychopathology. While phenomenological perspectives in psychiatry have seen a recent resurgence, there remains a need for more detailed models of psychopathological processes based on explicit phenomenological and enactive foundations.

Summary: We present a framework derived from the Nested States Model (NSM) through which such phenomenologically-grounded models might be constructed.

The Nested States Model: An Empirical Framework for Integrating Brain and Mind.

Philosophy of mind has made substantial progress on biologically-rooted approaches to understanding the mind and subjectivity through the enactivist perspective, but research on subjectivity within neuroscience has not kept apace. Indeed, we possess no principled means of relating experiential phenomena to neurophysiological processes. Here, we present the Nested States Model as a framework to guide empirical investigation into the relationship between subjectivity and neurobiology.

Taking subjectivity seriously: towards a unification of phenomenology, psychiatry, and neuroscience.

Nearly all psychiatric diseases involve alterations in subjective, lived experience. The scientific study of the biological basis of mental illness has generally focused on objective measures and observable behaviors, limiting the potential for our understanding of brain mechanisms of disease states and possible treatments. However, applying methods designed principally to interpret objective behavioral measures to the measurement and extrapolation of subjective states presents a number of challenges.

Learning divisive normalization in primary visual cortex.

Divisive normalization (DN) is a prominent computational building block in the brain that has been proposed as a canonical cortical operation. Numerous experimental studies have verified its importance for capturing nonlinear neural response properties to simple, artificial stimuli, and computational studies suggest that DN is also an important component for processing natural stimuli. However, we lack quantitative models of DN that are directly informed by measurements of spiking responses in the brain and applicable to arbitrary stimuli.

Deep convolutional models improve predictions of macaque V1 responses to natural images.

Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recently, two approaches based on deep learning have emerged for modeling these nonlinear computations: transfer learning from artificial neural networks trained on object recognition and data-driven convolutional neural network models trained end-to-end on large populations of neurons. Here, we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys.

Attentional fluctuations induce shared variability in macaque primary visual cortex.

Variability in neuronal responses to identical stimuli is frequently correlated across a population. Attention is thought to reduce these correlations by suppressing noisy inputs shared by the population. However, even with precise control of the visual stimulus, the subject's attentional state varies across trials.

Response to Comment on "Principles of connectivity among morphologically defined cell types in adult neocortex".

The critique of Barth et al centers on three points: (i) the completeness of our study is overstated; (ii) the connectivity matrix we describe is biased by technical limitations of our brain-slicing and multipatching methods; and (iii) our cell classification scheme is arbitrary and we have simply renamed previously identified interneuron types. We address these criticisms in our Response.

Investigating the Limits of Neurovascular Coupling.

O'Herron et al. (2016) perform two-photon imaging of vascular and neural responses in cat and rodent primary visual cortex to investigate the limits of neurovascular coupling. Their results suggest important constraints on making inferences about neuronal responses from hemodynamic activity.

Spike sorting for large, dense electrode arrays.

Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes.

On the Structure of Neuronal Population Activity under Fluctuations in Attentional State.

Unlabelled: Attention is commonly thought to improve behavioral performance by increasing response gain and suppressing shared variability in neuronal populations. However, both the focus and the strength of attention are likely to vary from one experimental trial to the next, thereby inducing response variability unknown to the experimenter. Here we study analytically how fluctuations in attentional state affect the structure of population responses in a simple model of spatial and feature attention.

Pupil fluctuations track fast switching of cortical states during quiet wakefulness.

Neural responses are modulated by brain state, which varies with arousal, attention, and behavior. In mice, running and whisking desynchronize the cortex and enhance sensory responses, but the quiescent periods between bouts of exploratory behaviors have not been well studied. We found that these periods of "quiet wakefulness" were characterized by state fluctuations on a timescale of 1-2 s.

State dependence of noise correlations in macaque primary visual cortex.

Shared, trial-to-trial variability in neuronal populations has a strong impact on the accuracy of information processing in the brain. Estimates of the level of such noise correlations are diverse, ranging from 0.01 to 0.

Monetary favors and their influence on neural responses and revealed preference.

Favors from a sender to a receiver are known to bias decisions made by the recipient, especially when the decision relates to the sender, a feature of social exchange known as reciprocity. Using an art-viewing paradigm possessing no objectively correct answer for preferring one piece of art over another, we show that sponsorship of the experiment by a company endows the logo of the company with the capacity to bias revealed preference for art displayed next to the logo. Merely offering to sponsor the experiment similarly endowed the gesturing logo of the company with the capacity to bias revealed preferences.