A human-specific modifier of cortical connectivity and circuit function.

The cognitive abilities that characterize humans are thought to emerge from unique features of the cortical circuit architecture of the human brain, which include increased cortico-cortical connectivity. However, the evolutionary origin of these changes in connectivity and how they affected cortical circuit function and behaviour are currently unknown. The human-specific gene duplication SRGAP2C emerged in the ancestral genome of the Homo lineage before the major phase of increase in brain size1,2. SRGAP2C expression in mice increases the density of excitatory and inhibitory synapses received by layer 2/3 pyramidal neurons (PNs)3-5. Here we show that the increased number of excitatory synapses received by layer 2/3 PNs induced by SRGAP2C expression originates from a specific increase in local and long-range cortico-cortical connections. Mice humanized for SRGAP2C expression in all cortical PNs displayed a shift in the fraction of layer 2/3 PNs activated by sensory stimulation and an enhanced ability to learn a cortex-dependent sensory-discrimination task. Computational modelling revealed that the increased layer 4 to layer 2/3 connectivity induced by SRGAP2C expression explains some of the key changes in sensory coding properties. These results suggest that the emergence of SRGAP2C at the birth of the Homo lineage contributed to the evolution of specific structural and functional features of cortical circuits in the human cortex.

Sensation, movement and learning in the absence of barrel cortex.

For many of our senses, the role of the cerebral cortex in detecting stimuli is controversial1-17. Here we examine the effects of both acute and chronic inactivation of the primary somatosensory cortex in mice trained to move their large facial whiskers to detect an object by touch and respond with a lever to obtain a water reward. Using transgenic mice, we expressed inhibitory opsins in excitatory cortical neurons. Transient optogenetic inactivation of the primary somatosensory cortex, as well as permanent lesions, initially produced both movement and sensory deficits that impaired detection behaviour, demonstrating the link between sensory and motor systems during active sensing. Unexpectedly, lesioned mice had recovered full behavioural capabilities by the subsequent session. This rapid recovery was experience-dependent, and early re-exposure to the task after lesioning facilitated recovery. Furthermore, ablation of the primary somatosensory cortex before learning did not affect task acquisition. This combined optogenetic and lesion approach suggests that manipulations of the sensory cortex may be only temporarily disruptive to other brain structures that are themselves capable of coordinating multiple, arbitrary movements with sensation. Thus, the somatosensory cortex may be dispensable for active detection of objects in the environment.

Sound-seeking before and after hearing loss in mice.

How we move our bodies affects how we perceive sound. For instance, we can explore an environment to seek out the source of a sound and we can use head movements to compensate for hearing loss. How we do this is not well understood because many auditory experiments are designed to limit head and body movements. To study the role of movement in hearing, we developed a behavioral task called sound-seeking that rewarded mice for tracking down an ongoing sound source. Over the course of learning, mice more efficiently navigated to the sound. We then asked how auditory behavior was affected by hearing loss induced by surgical removal of the malleus from the middle ear. An innate behavior, the auditory startle response, was abolished by bilateral hearing loss and unaffected by unilateral hearing loss. Similarly, performance on the sound-seeking task drastically declined after bilateral hearing loss and did not recover. In striking contrast, mice with unilateral hearing loss were only transiently impaired on sound-seeking; over a recovery period of about a week, they regained high levels of performance, increasingly reliant on a different spatial sampling strategy. Thus, even in the face of permanent unilateral damage to the peripheral auditory system, mice recover their ability to perform a naturalistic sound-seeking task. This paradigm provides an opportunity to examine how body movement enables better hearing and resilient adaptation to sensory deprivation.

The geometry of cortical representations of touch in rodents.

Neurons often encode highly heterogeneous non-linear functions of multiple task variables, a signature of a high-dimensional geometry. We studied the representational geometry in the somatosensory cortex of mice trained to report the curvature of objects touched by their whiskers. High-speed videos of the whiskers revealed that the task can be solved by linearly integrating multiple whisker contacts over time. However, the neural activity in somatosensory cortex reflects non-linear integration of spatio-temporal features of the sensory inputs. Although the responses at first appeared disorganized, we identified an interesting structure in the representational geometry: different whisker contacts are disentangled variables represented in approximately, but not fully, orthogonal subspaces of the neural activity space. This geometry allows linear readouts to perform a broad class of tasks of different complexities without compromising the ability to generalize to novel situations.

Early selection of task-relevant features through population gating.

Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within primary auditory cortex (A1) but controlled by top-down inputs from prelimbic region of medial prefrontal cortex (mPFC), can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.

A detailed behavioral, videographic, and neural dataset on object recognition in mice.

Mice adeptly use their whiskers to touch, recognize, and learn about objects in their environment. This behavior is enabled by computations performed by populations of neurons in the somatosensory cortex. To understand these computations, we trained mice to use their whiskers to recognize different shapes while we recorded activity in the barrel cortex, which processes whisker input. Here, we present a large dataset of high-speed video of the whiskers, along with rigorous tracking of the entire extent of multiple whiskers and every contact they made on the shape. We used spike sorting to identify individual neurons, which responded with precise timing to whisker contacts and motion. These data will be useful for understanding the behavioral strategies mice use to explore objects, as well as the neuronal dynamics that mediate those strategies. In addition, our carefully curated labeled data could be used to develop new computer vision algorithms for tracking body posture, or for extracting responses of individual neurons from large-scale neural recordings.

Decision making: Making sense of non-sensory neurons.

Decisions can be made internally and implicitly, without being expressed explicitly. A new study reveals how implicit decisions might engage the enigmatic 'non-sensory' neurons in sensory cortex.

Sensorimotor strategies and neuronal representations for shape discrimination.

Humans and other animals can identify objects by active touch, requiring the coordination of exploratory motion and tactile sensation. Both the motor strategies and neural representations employed could depend on the subject's goals. We developed a shape discrimination task that challenged head-fixed mice to discriminate concave from convex shapes. Behavioral decoding revealed that mice did this by comparing contacts across whiskers. In contrast, a separate group of mice performing a shape detection task simply summed up contacts over whiskers. We recorded populations of neurons in the barrel cortex, which processes whisker input, and found that individual neurons across the cortical layers encoded touch, whisker motion, and task-related signals. Sensory representations were task-specific: during shape discrimination, but not detection, neurons responded most to behaviorally relevant whiskers, overriding somatotopy. Thus, sensory cortex employs task-specific representations compatible with behaviorally relevant computations.

Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons.

Neurons recorded in behaving animals often do not discernibly respond to sensory input and are not overtly task-modulated. These non-classically responsive neurons are difficult to interpret and are typically neglected from analysis, confounding attempts to connect neural activity to perception and behavior. Here, we describe a trial-by-trial, spike-timing-based algorithm to reveal the coding capacities of these neurons in auditory and frontal cortex of behaving rats. Classically responsive and non-classically responsive cells contained significant information about sensory stimuli and behavioral decisions. Stimulus category was more accurately represented in frontal cortex than auditory cortex, via ensembles of non-classically responsive cells coordinating the behavioral meaning of spike timings on correct but not error trials. This unbiased approach allows the contribution of all recorded neurons - particularly those without obvious task-related, trial-averaged firing rate modulation - to be assessed for behavioral relevance on single trials.

A Device for Human Ultrasonic Echolocation.

OBJECTIVE: We present a device that combines principles of ultrasonic echolocation and spatial hearing to provide human users with environmental cues that are 1) not otherwise available to the human auditory system, and 2) richer in object and spatial information than the more heavily processed sonar cues of other assistive devices. The device consists of a wearable headset with an ultrasonic emitter and stereo microphones with affixed artificial pinnae. The goal of this study is to describe the device and evaluate the utility of the echoic information it provides. METHODS: The echoes of ultrasonic pulses were recorded and time stretched to lower their frequencies into the human auditory range, then played back to the user. We tested performance among naive and experienced sighted volunteers using a set of localization experiments, in which the locations of echo-reflective surfaces were judged using these time-stretched echoes. RESULTS: Naive subjects were able to make laterality and distance judgments, suggesting that the echoes provide innately useful information without prior training. Naive subjects were generally unable to make elevation judgments from recorded echoes. However, trained subjects demonstrated an ability to judge elevation as well. CONCLUSION: This suggests that the device can be used effectively to examine the environment and that the human auditory system can rapidly adapt to these artificial echolocation cues. SIGNIFICANCE: Interpreting and interacting with the external world constitutes a major challenge for persons who are blind or visually impaired. This device has the potential to aid blind people in interacting with their environment.

Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents.

Animals can selectively respond to a target sound despite simultaneous distractors, just as humans can respond to one voice at a crowded cocktail party. To investigate the underlying neural mechanisms, we recorded single-unit activity in primary auditory cortex (A1) and medial prefrontal cortex (mPFC) of rats selectively responding to a target sound from a mixture. We found that prestimulus activity in mPFC encoded the selection rule-which sound from the mixture the rat should select. Moreover, electrically disrupting mPFC significantly impaired performance. Surprisingly, prestimulus activity in A1 also encoded selection rule, a cognitive variable typically considered the domain of prefrontal regions. Prestimulus changes correlated with stimulus-evoked changes, but stimulus tuning was not strongly affected. We suggest a model in which anticipatory activation of a specific network of neurons underlies the selection of a sound from a mixture, giving rise to robust and widespread rule encoding in both brain regions.

Neo: an object model for handling electrophysiology data in multiple formats.

Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named "Neo," suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology.