Endogenous fluctuations in cortical state selectively enhance different modes of sensory processing in human temporal lobe.

The degree of synchronized fluctuations in neocortical network activity can vary widely during alertness. One influential idea that has emerged over the past few decades is that perceptual decisions are more accurate when the state of population activity is desynchronized. This suggests that optimal task performance may occur during a particular cortical state - the desynchronized state. Here we show that, contrary to this view, cortical state can both facilitate and suppress perceptual performance in a task-dependent manner. We performed electrical recordings from surface-implanted grid electrodes in the temporal lobe while human subjects completed two perceptual tasks. We found that when local population activity is in a synchronized state, network and perceptual performance are enhanced in a detection task and impaired in a discrimination task, but these modulatory effects are reversed when population activity is desynchronized. These findings indicate that the brain has adapted to take advantage of endogenous fluctuations in the state of neural populations in temporal cortex to selectively enhance different modes of sensory processing during perception in a state-dependent manner.

Cortical response states for enhanced sensory discrimination.

Brain activity during wakefulness is characterized by rapid fluctuations in neuronal responses. Whether these fluctuations play any role in modulating the accuracy of behavioral responses is poorly understood. Here, we investigated whether and how trial changes in the population response impact sensory coding in monkey V1 and perceptual performance. Although the responses of individual neurons varied widely across trials, many cells tended to covary with the local population. When population activity was in a 'low' state, neurons had lower evoked responses and correlated variability, yet higher probability to predict perceptual accuracy. The impact of firing rate fluctuations on network and perceptual accuracy was strongest 200 ms before stimulus presentation, and it greatly diminished when the number of cells used to measure the state of the population was decreased. These findings indicate that enhanced perceptual discrimination occurs when population activity is in a 'silent' response mode in which neurons increase information extraction.

Fully integrated silicon probes for high-density recording of neural activity.

Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal-oxide-semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 × 20-µm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.

Mechanisms underlying a thalamocortical transformation during active tactile sensation.

During active somatosensation, neural signals expected from movement of the sensors are suppressed in the cortex, whereas information related to touch is enhanced. This tactile suppression underlies low-noise encoding of relevant tactile features and the brain's ability to make fine tactile discriminations. Layer (L) 4 excitatory neurons in the barrel cortex, the major target of the somatosensory thalamus (VPM), respond to touch, but have low spike rates and low sensitivity to the movement of whiskers. Most neurons in VPM respond to touch and also show an increase in spike rate with whisker movement. Therefore, signals related to self-movement are suppressed in L4. Fast-spiking (FS) interneurons in L4 show similar dynamics to VPM neurons. Stimulation of halorhodopsin in FS interneurons causes a reduction in FS neuron activity and an increase in L4 excitatory neuron activity. This decrease of activity of L4 FS neurons contradicts the "paradoxical effect" predicted in networks stabilized by inhibition and in strongly-coupled networks. To explain these observations, we constructed a model of the L4 circuit, with connectivity constrained by in vitro measurements. The model explores the various synaptic conductance strengths for which L4 FS neurons actively suppress baseline and movement-related activity in layer 4 excitatory neurons. Feedforward inhibition, in concert with recurrent intracortical circuitry, produces tactile suppression. Synaptic delays in feedforward inhibition allow transmission of temporally brief volleys of activity associated with touch. Our model provides a mechanistic explanation of a behavior-related computation implemented by the thalamocortical circuit.

Spontaneous Fluctuations in Visual Cortical Responses Influence Population Coding Accuracy.

Information processing in the cerebral cortex depends not only on the nature of incoming stimuli, but also on the state of neuronal networks at the time of stimulation. That is, the same stimulus will be processed differently depending on the neuronal context in which it is received. A major factor that could influence neuronal context is the background, or ongoing neuronal activity before stimulation. In visual cortex, ongoing activity is known to play a critical role in the development of local circuits, yet whether it influences the coding of visual features in adult cortex is unclear. Here, we investigate whether and how the information encoded by individual neurons and populations in primary visual cortex (V1) depends on the ongoing activity before stimulus presentation. We report that when individual neurons are in a "low" prestimulus state, they have a higher capacity to discriminate stimulus features, such as orientation, despite their reduction in evoked responses. By measuring the distribution of prestimulus activity across a population of neurons, we found that network discrimination accuracy is improved in the low prestimulus state. Thus, the distribution of ongoing activity states across the network creates an "internal context" that dynamically filters incoming stimuli to modulate the accuracy of sensory coding. The modulation of stimulus coding by ongoing activity state is consistent with recurrent network models in which ongoing activity dynamically controls the balanced background excitation and inhibition to individual neurons.

Layer 4 fast-spiking interneurons filter thalamocortical signals during active somatosensation.

We rely on movement to explore the environment, for example, by palpating an object. In somatosensory cortex, activity related to movement of digits or whiskers is suppressed, which could facilitate detection of touch. Movement-related suppression is generally assumed to involve corollary discharges. Here we uncovered a thalamocortical mechanism in which cortical fast-spiking interneurons, driven by sensory input, suppress movement-related activity in layer 4 (L4) excitatory neurons. In mice locating objects with their whiskers, neurons in the ventral posteromedial nucleus (VPM) fired in response to touch and whisker movement. Cortical L4 fast-spiking interneurons inherited these responses from VPM. In contrast, L4 excitatory neurons responded mainly to touch. Optogenetic experiments revealed that fast-spiking interneurons reduced movement-related spiking in excitatory neurons, enhancing selectivity for touch-related information during active tactile sensation. These observations suggest a fundamental computation performed by the thalamocortical circuit to accentuate salient tactile information.

Low-noise encoding of active touch by layer 4 in the somatosensory cortex.

Cortical spike trains often appear noisy, with the timing and number of spikes varying across repetitions of stimuli. Spiking variability can arise from internal (behavioral state, unreliable neurons, or chaotic dynamics in neural circuits) and external (uncontrolled behavior or sensory stimuli) sources. The amount of irreducible internal noise in spike trains, an important constraint on models of cortical networks, has been difficult to estimate, since behavior and brain state must be precisely controlled or tracked. We recorded from excitatory barrel cortex neurons in layer 4 during active behavior, where mice control tactile input through learned whisker movements. Touch was the dominant sensorimotor feature, with >70% spikes occurring in millisecond timescale epochs after touch onset. The variance of touch responses was smaller than expected from Poisson processes, often reaching the theoretical minimum. Layer 4 spike trains thus reflect the millisecond-timescale structure of tactile input with little noise.

Procedures for behavioral experiments in head-fixed mice.

The mouse is an increasingly prominent model for the analysis of mammalian neuronal circuits. Neural circuits ultimately have to be probed during behaviors that engage the circuits. Linking circuit dynamics to behavior requires precise control of sensory stimuli and measurement of body movements. Head-fixation has been used for behavioral research, particularly in non-human primates, to facilitate precise stimulus control, behavioral monitoring and neural recording. However, choice-based, perceptual decision tasks by head-fixed mice have only recently been introduced. Training mice relies on motivating mice using water restriction. Here we describe procedures for head-fixation, water restriction and behavioral training for head-fixed mice, with a focus on active, whisker-based tactile behaviors. In these experiments mice had restricted access to water (typically 1 ml/day). After ten days of water restriction, body weight stabilized at approximately 80% of initial weight. At that point mice were trained to discriminate sensory stimuli using operant conditioning. Head-fixed mice reported stimuli by licking in go/no-go tasks and also using a forced choice paradigm using a dual lickport. In some cases mice learned to discriminate sensory stimuli in a few trials within the first behavioral session. Delay epochs lasting a second or more were used to separate sensation (e.g. tactile exploration) and action (i.e. licking). Mice performed a variety of perceptual decision tasks with high performance for hundreds of trials per behavioral session. Up to four months of continuous water restriction showed no adverse health effects. Behavioral performance correlated with the degree of water restriction, supporting the importance of controlling access to water. These behavioral paradigms can be combined with cellular resolution imaging, random access photostimulation, and whole cell recordings.

Flow of cortical activity underlying a tactile decision in mice.

Perceptual decisions involve distributed cortical activity. Does information flow sequentially from one cortical area to another, or do networks of interconnected areas contribute at the same time? Here we delineate when and how activity in specific areas drives a whisker-based decision in mice. A short-term memory component temporally separated tactile "sensation" and "action" (licking). Using optogenetic inhibition (spatial resolution, 2 mm; temporal resolution, 100 ms), we surveyed the neocortex for regions driving behavior during specific behavioral epochs. Barrel cortex was critical for sensation. During the short-term memory, unilateral inhibition of anterior lateral motor cortex biased responses to the ipsilateral side. Consistently, barrel cortex showed stimulus-specific activity during sensation, whereas motor cortex showed choice-specific preparatory activity and movement-related activity, consistent with roles in motor planning and movement. These results suggest serial information flow from sensory to motor areas during perceptual decision making.

Organization of cortical and thalamic input to pyramidal neurons in mouse motor cortex.

Determining how long-range synaptic inputs engage pyramidal neurons in primary motor cortex (M1) is important for understanding circuit mechanisms involved in regulating movement. We used channelrhodopsin-2-assisted circuit mapping to characterize the long-range excitatory synaptic connections made by multiple cortical and thalamic areas onto pyramidal neurons in mouse vibrissal motor cortex (vM1). Each projection innervated vM1 pyramidal neurons with a unique laminar profile. Collectively, the profiles for different sources of input partially overlapped and spanned all cortical layers. Specifically, orbital cortex (OC) inputs primarily targeted neurons in L6. Secondary motor cortex (M2) inputs excited neurons mainly in L5B, including pyramidal tract neurons. In contrast, thalamocortical inputs from anterior motor-related thalamic regions, including VA/VL (ventral anterior thalamic nucleus/ventrolateral thalamic nucleus), targeted neurons in L2/3 through L5B, but avoided L6. Inputs from posterior sensory-related thalamic areas, including POm (posterior thalamic nuclear group), targeted neurons only in the upper layers (L2/3 and L5A), similar to inputs from somatosensory (barrel) cortex. Our results show that long-range excitatory inputs target vM1 pyramidal neurons in a layer-specific manner. Inputs from sensory-related cortical and thalamic areas preferentially target the upper-layer pyramidal neurons in vM1. In contrast, inputs from OC and M2, areas associated with volitional and cognitive aspects of movements, bypass local circuitry and have direct monosynaptic access to neurons projecting to brainstem and thalamus.

Activity in motor-sensory projections reveals distributed coding in somatosensation.

Cortical-feedback projections to primary sensory areas terminate most heavily in layer 1 (L1) of the neocortex, where they make synapses with tuft dendrites of pyramidal neurons. L1 input is thought to provide 'contextual' information, but the signals transmitted by L1 feedback remain uncharacterized. In the rodent somatosensory system, the spatially diffuse feedback projection from vibrissal motor cortex (vM1) to vibrissal somatosensory cortex (vS1, also known as the barrel cortex) may allow whisker touch to be interpreted in the context of whisker position to compute object location. When mice palpate objects with their whiskers to localize object features, whisker touch excites vS1 and later vM1 in a somatotopic manner. Here we use axonal calcium imaging to track activity in vM1-->vS1 afferents in L1 of the barrel cortex while mice performed whisker-dependent object localization. Spatially intermingled individual axons represent whisker movements, touch and other behavioural features. In a subpopulation of axons, activity depends on object location and persists for seconds after touch. Neurons in the barrel cortex thus have information to integrate movements and touches of multiple whiskers over time, key components of object identification and navigation by active touch.

A biologically plausible model for same/different discrimination.

Abstract rules can be learned by several species (not only humans). We propose a biologically plausible model for same/different discrimination, that can point towards the neural basis of abstract concept learning. By including a neural adaptation mechanism to a discriminator model formerly introduced in the literature, selective clusters of neurons fire depending on whether or not the stimuli compared are the same or not. These selective neurons are consistent with experimental findings in the literature. Moreover, reward and attention can modulate the relative strength of each attribute/feature of the stimulus, so more complex abstract discriminations can be achieved using the proposed model as a building block. As a formal model, it can be easily incorporated into several applications in robotics and intelligent machines.

Generation of spatiotemporally correlated spike trains and local field potentials using a multivariate autoregressive process.

Experimental advances allowing for the simultaneous recording of activity at multiple sites have significantly increased our understanding of the spatiotemporal patterns in neural activity. The impact of such patterns on neural coding is a fundamental question in neuroscience. The simulation of spike trains with predetermined activity patterns is therefore an important ingredient in the study of potential neural codes. Such artificially generated spike trains could also be used to manipulate cortical neurons in vitro and in vivo. Here, we propose a method to generate spike trains with given mean firing rates and cross-correlations. To capture this statistical structure we generate a point process by thresholding a stochastic process that is continuous in space and discrete in time. This stochastic process is obtained by filtering Gaussian noise through a multivariate autoregressive (AR) model. The parameters of the AR model are obtained by a nonlinear transformation of the point-process correlations to the continuous-process correlations. The proposed method is very efficient and allows for the simulation of large neural populations. It can be optimized to the structure of spatiotemporal correlations and generalized to nonstationary processes and spatiotemporal patterns of local field potentials and spike trains.

Attention alters visual plasticity during exposure-based learning.

It is generally believed that attention enhances the processing of sensory information during perception and learning. Here we report that, contrary to common belief, attention limits the degree of plasticity induced by repeated exposure to image features. Specifically, daily exposure to oriented stimuli that are not linked to a specific task causes an orientation-specific improvement in perceptual performance along the "exposed" axes. This effect is modulated by attention: human subjects showed a larger improvement in orientation discrimination when attention is directed toward the location where stimuli are presented. However, the capacity to perform discriminations away from the exposed orientation is enhanced when the exposure stimuli are unattended. Importantly, the improvement in orientation discrimination at the unattended location leads to a robust enhancement in the discrimination of complex stimuli, such as natural texture images, with orientation components along the exposed axes, whereas the improvement in orientation discrimination at the attended location exhibits only weak transfer to complex stimuli. These results indicate that sensory adaptation by passive stimulus exposure should be viewed as a form of perceptual learning that is complementary to practice-based learning in that it reduces constraints on generalization.

Adaptive coding of visual information in neural populations.

Our perception of the environment relies on the capacity of neural networks to adapt rapidly to changes in incoming stimuli. It is increasingly being realized that the neural code is adaptive, that is, sensory neurons change their responses and selectivity in a dynamic manner to match the changes in input stimuli. Understanding how rapid exposure, or adaptation, to a stimulus of fixed structure changes information processing by cortical networks is essential for understanding the relationship between sensory coding and behaviour. Physiological investigations of adaptation have contributed greatly to our understanding of how individual sensory neurons change their responses to influence stimulus coding, yet whether and how adaptation affects information coding in neural populations is unknown. Here we examine how brief adaptation (on the timescale of visual fixation) influences the structure of interneuronal correlations and the accuracy of population coding in the macaque (Macaca mulatta) primary visual cortex (V1). We find that brief adaptation to a stimulus of fixed structure reorganizes the distribution of correlations across the entire network by selectively reducing their mean and variability. The post-adaptation changes in neuronal correlations are associated with specific, stimulus-dependent changes in the efficiency of the population code, and are consistent with changes in perceptual performance after adaptation. Our results have implications beyond the predictions of current theories of sensory coding, suggesting that brief adaptation improves the accuracy of population coding to optimize neuronal performance during natural viewing.