Cortical Coding Research Articles

Cortical Coding of Gustatory and Thermal Signals in Active Licking Mice.

Eating behaviors are influenced by the integration of gustatory, olfactory, and somatosensory signals, which all contribute to the perception of flavor. Although extensive research has explored the neural correlates of taste in the gustatory cortex (GC), less is known about its role in encoding thermal information. This study investigates the encoding of oral thermal and chemosensory signals by GC neurons compared to the oral somatosensory cortex. In this study, we recorded the spiking activity of more than 900 GC neurons and 500 neurons from the oral somatosensory cortex in mice allowed to freely lick small drops of gustatory stimuli or deionized water at varying non-nociceptive temperatures. We then developed and used a Bayesian-based analysis technique to assess neural classification scores based on spike rate and phase timing within the lick cycle. Our results indicate that GC neurons rely predominantly on rate information, although phase information is needed to achieve maximum accuracy, to effectively encode both chemosensory and thermosensory signals. GC neurons can effectively differentiate between thermal stimuli, excelling in distinguishing both large contrasts (14°C vs. 36°C) and, although less effectively, more subtle temperature differences. Finally, a direct comparison of the decoding accuracy of thermosensory signals between the two cortices reveals that while the somatosensory cortex showed higher overall accuracy, the GC still encodes significant thermosensory information. These findings highlight the GC's dual role in processing taste and temperature, emphasizing the importance of considering temperature in future studies of taste processing.

Representation of Natural Contours by a Neural Population in Monkey V4.

The cortical visual area, V4, has been considered to code contours that contribute to the intermediate-level representation of objects. The neural responses to the complex contour features intrinsic to natural contours are expected to clarify the essence of the representation. To approach the cortical coding of natural contours, we investigated the simultaneous coding of multiple contour features in monkey (Macaca fuscata) V4 neurons and their population-level representation. A substantial number of neurons showed significant tuning for two or more features such as curvature and closure, indicating that a substantial number of V4 neurons simultaneously code multiple contour features. A large portion of the neurons responded vigorously to acutely curved contours that surrounded the center of classical receptive field, suggesting that V4 neurons tend to code prominent features of object contours. The analysis of mutual information (MI) between the neural responses and each contour feature showed that most neurons exhibited similar magnitudes for each type of MI, indicating that many neurons showing the responses depended on multiple contour features. We next examined the population-level representation by using multidimensional scaling analysis. The neural preferences to the multiple contour features and that to natural stimuli compared with silhouette stimuli increased along with the primary and secondary axes, respectively, indicating the contribution of the multiple contour features and surface textures in the population responses. Our analyses suggested that V4 neurons simultaneously code multiple contour features in natural images and represent contour and surface properties in population.

A distinct cortical code for socially learned threat.

Animals can learn about sources of danger while minimizing their own risk by observing how others respond to threats. However, the distinct neural mechanisms by which threats are learned through social observation (known as observational fear learning1-4 (OFL)) to generate behavioural responses specific to such threats remain poorly understood. The dorsomedial prefrontal cortex (dmPFC) performs several key functions that may underlie OFL, including processing of social information and disambiguation of threat cues5-11. Here we show that dmPFC is recruited and required for OFL in mice. Using cellular-resolution microendoscopic calcium imaging, we demonstrate that dmPFC neurons code for observational fear and do so in a manner that is distinct from direct experience. We find that dmPFC neuronal activity predicts upcoming switches between freezing and moving state elicited by threat. By combining neuronal circuit mapping, calcium imaging, electrophysiological recordings and optogenetics, we show that dmPFC projections to the midbrain periaqueductal grey (PAG)constrain observer freezing, and that amygdalar and hippocampal inputs to dmPFC opposingly modulate observer freezing. Together our findings reveal that dmPFC neurons compute a distinct code for observational fear and coordinate long-range neural circuits to select behavioural responses.

Construction of Objects from Pixels — Cortical Coding in Intermediate-level Visual Area V4

Construction of Objects from Pixels — Cortical Coding in Intermediate-level Visual Area V4

Auditory cortical coding of task-relevant variables during active listening

Auditory-guided behaviors are ubiquitous in everyday life, whenever auditory information is used to guide our decisions and actions. The primary route for auditory information to propagate from the ACtx is through intratelencephalic (IT) and extratelencephalic (ET) neurons in layer (L) 5. To investigate the behavioral role of IT and ET neurons, we engaged mice in a head-fixed auditory categorization task and longitudinally monitored populations of L5 IT and ET neurons using two-photon calcium imaging. Clustering analyses of these populations revealed heterogenous response motifs that correlated with various stimulus and task variables, but with distinct expression patterns across learning and task proficiency. Our results suggest a tradeoff of information between two distinct populations within L5, with IT projections playing a role in initial task acquisition, while ET projections are recruited and reinforced throughout the learning process. This underscores the differential roles of distinct descending systems and contribute to our understanding of how auditory information is processed and utilized to guide decision-making and action.

Parvalbumin neurons enhance temporal coding and reduce cortical noise in complex auditory scenes

Cortical representations supporting many cognitive abilities emerge from underlying circuits comprised of several different cell types. However, cell type-specific contributions to rate and timing-based cortical coding are not well-understood. Here, we investigated the role of parvalbumin neurons in cortical complex scene analysis. Many complex scenes contain sensory stimuli which are highly dynamic in time and compete with stimuli at other spatial locations. Parvalbumin neurons play a fundamental role in balancing excitation and inhibition in cortex and sculpting cortical temporal dynamics; yet their specific role in encoding complex scenes via timing-based coding, and the robustness of temporal representations to spatial competition, has not been investigated. Here, we address these questions in auditory cortex of mice using a cocktail party-like paradigm, integrating electrophysiology, optogenetic manipulations, and a family of spike-distance metrics, to dissect parvalbumin neurons’ contributions towards rate and timing-based coding. We find that suppressing parvalbumin neurons degrades cortical discrimination of dynamic sounds in a cocktail party-like setting via changes in rapid temporal modulations in rate and spike timing, and over a wide range of time-scales. Our findings suggest that parvalbumin neurons play a critical role in enhancing cortical temporal coding and reducing cortical noise, thereby improving representations of dynamic stimuli in complex scenes.

Peripersonal encoding of forelimb proprioception in the mouse somatosensory cortex

Conscious perception of limb movements depends on proprioceptive neural responses in the somatosensory cortex. In contrast to tactile sensations, proprioceptive cortical coding is barely studied in the mammalian brain and practically non-existent in rodent research. To understand the cortical representation of this important sensory modality we developed a passive forelimb displacement paradigm in behaving mice and also trained them to perceptually discriminate where their limb is moved in space. We delineated the rodent proprioceptive cortex with wide-field calcium imaging and optogenetic silencing experiments during behavior. Our results reveal that proprioception is represented in both sensory and motor cortical areas. In addition, behavioral measurements and responses of layer 2/3 neurons imaged with two-photon microscopy reveal that passive limb movements are both perceived and encoded in the mouse cortex as a spatial direction vector that interfaces the limb with the body’s peripersonal space.

A cortical zoom-in operation underlies covert shifts of visual spatial attention

Shifting the focus of attention without moving the eyes poses challenges for signal coding in visual cortex in terms of spatial resolution, signal routing, and cross-talk. Little is known how these problems are solved during focus shifts. Here, we analyze the spatiotemporal dynamic of neuromagnetic activity in human visual cortex as a function of the size and number of focus shifts in visual search. We find that large shifts elicit activity modulations progressing from highest (IT) through mid-level (V4) to lowest hierarchical levels (V1). Smaller shifts cause those modulations to start at lower levels in the hierarchy. Successive shifts involve repeated backward progressions through the hierarchy. We conclude that covert focus shifts arise from a cortical coarse-to-fine process progressing from retinotopic areas with larger toward areas with smaller receptive fields. This process localizes the target and increases the spatial resolution of selection, which resolves the above issues of cortical coding.

High Performance Time Series Anomaly Detection Using Brain Inspired Cortical Coding Method

Accurate and automated anomaly detection in time series data sets has an increasingly important role in a wide range of applications. Inspired by coding in the cortical networks of the brain, here we introduce a novel approach for high performance real-time anomaly detection. Cortical coding method is adaptive and dynamic, consisting of self-organized networks. In the cortical coding network introduced herein, the morphological structuring is driven by a brain inspired feature extraction strategy that aims the minimization of the signal energy dissipation while increasing the information entropy of the system. We combine the cortical coding network with transform coding and multi resolution analysis for anomaly detection. As we demonstrate here, the new coding methodology provides high computational efficiency in addition to scalability with respect to target accuracy compared to the traditional clustering algorithms. A wide variety of data sets are used to demonstrate time series anomaly detection performance. In a preliminary work presented here, we detected 77.6% of the present anomalies correctly, using the same hyperparameters for every stage of the method. The results are compared with several clustering algorithms such as K-means and its variants mini-batch K-means, sequential K-means and finally with hierarchical agglomerative clustering. Additionally, the performance of all the clustering methods are compared by memorizing all input data set without performing any clustering. The cortical coding method has shown the best performance compared to the other methods. From the results achieved so far, it appears that there is still a significant room for improvement of the success rate by, specifically, performing hyperparameter and filter optimization according to the characteristics of data sets and using a more advanced fusion model at the output layer. Low time and space complexity, high generalization performance, suitability to real-time anomaly detection, and in-memory processing compatibility are distinct advantages of the cortical coding method in a variety of anomaly detection problems, such as predictive maintenance, cybersecurity, telemedicine, risk management, and transportation safety.

Oral thermal processing in the gustatory cortex of awake mice.

Oral temperature is a sensory cue relevant to food preference and nutrition. To understand how orally sourced thermal inputs are represented in the gustatory cortex (GC), we recorded neural responses from the GC of male and female mice presented with deionized water at different innocuous temperatures (14 °C, 25 °C, and 36 °C) and taste stimuli (room temperature). Our results demonstrate that GC neurons encode orally sourced thermal information in the absence of classical taste qualities at the single neuron and population levels, as confirmed through additional experiments comparing GC neuron responses to water and artificial saliva. Analysis of thermal-evoked responses showed broadly tuned neurons that responded to temperature in a mostly monotonic manner. Spatial location may play a minor role regarding thermosensory activity; aside from the most ventral GC, neurons reliably responded to and encoded thermal information across the dorso-ventral and antero-postero cortical axes. Additional analysis revealed that more than half of the GC neurons that encoded chemosensory taste stimuli also accurately discriminated thermal information, providing additional evidence of the GC's involvement in processing thermosensory information important for ingestive behaviors. In terms of convergence, we found that GC neurons encoding information about both taste and temperature were broadly tuned and carried more information than taste-selective-only neurons; both groups encoded similar information about the palatability of stimuli. Altogether, our data reveal new details of the cortical code for the mammalian oral thermosensory system in behaving mice and pave the way for future investigations on GC functions and operational principles with respect to thermogustation.

Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation.

A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential in performance optimization. Here, we introduce a feature extraction method inspired by energy-entropy relations of sensory cortical networks in the brain. Dubbed the brain-inspired cortex, the algorithm provides convergence to orthogonal features from streaming signals with superior computational efficiency while processing data in a compressed form. We demonstrate the performance of the new algorithm using artificially created complex data by comparing it with the commonly used traditional clustering algorithms, such as Birch, GMM, and K-means. While the data processing time is significantly reduced-seconds versus hours-encoding distortions remain essentially the same in the new algorithm, providing a basis for better generalization. Although we show herein the superior performance of the cortical coding model in clustering and vector quantization, it also provides potent implementation opportunities for machine learning fundamental components, such as reasoning, anomaly detection and classification in large scope applications, e.g., finance, cybersecurity, and healthcare.

A Redundant Cortical Code for Speech Envelope.

Animal communication sounds exhibit complex temporal structure because of the amplitude fluctuations that comprise the sound envelope. In human speech, envelope modulations drive synchronized activity in auditory cortex (AC), which correlates strongly with comprehension (Giraud and Poeppel, 2012; Peelle and Davis, 2012; Haegens and Zion Golumbic, 2018). Studies of envelope coding in single neurons, performed in nonhuman animals, have focused on periodic amplitude modulation (AM) stimuli and use response metrics that are not easy to juxtapose with data from humans. In this study, we sought to bridge these fields. Specifically, we looked directly at the temporal relationship between stimulus envelope and spiking, and we assessed whether the apparent diversity across neurons' AM responses contributes to the population representation of speech-like sound envelopes. We gathered responses from single neurons to vocoded speech stimuli and compared them to sinusoidal AM responses in auditory cortex (AC) of alert, freely moving Mongolian gerbils of both sexes. While AC neurons displayed heterogeneous tuning to AM rate, their temporal dynamics were stereotyped. Preferred response phases accumulated near the onsets of sinusoidal AM periods for slower rates (<8 Hz), and an over-representation of amplitude edges was apparent in population responses to both sinusoidal AM and vocoded speech envelopes. Crucially, this encoding bias imparted a decoding benefit: a classifier could discriminate vocoded speech stimuli using summed population activity, while higher frequency modulations required a more sophisticated decoder that tracked spiking responses from individual cells. Together, our results imply that the envelope structure relevant to parsing an acoustic stream could be read-out from a distributed, redundant population code.SIGNIFICANCE STATEMENT Animal communication sounds have rich temporal structure and are often produced in extended sequences, including the syllabic structure of human speech. Although the auditory cortex (AC) is known to play a crucial role in representing speech syllables, the contribution of individual neurons remains uncertain. Here, we characterized the representations of both simple, amplitude-modulated sounds and complex, speech-like stimuli within a broad population of cortical neurons, and we found an overrepresentation of amplitude edges. Thus, a phasic, redundant code in auditory cortex can provide a mechanistic explanation for segmenting acoustic streams like human speech.

Emergent reliability in sensory cortical coding and inter-area communication.

Reliable sensory discrimination must arise from high-fidelity neural representations and communication between brain areas. However, how neocortical sensory processing overcomes the substantialvariability of neuronal sensory responses remains undetermined1-6. Here we imaged neuronalactivity in eight neocortical areas concurrently and over five days in mice performing a visual discrimination task, yielding longitudinal recordings of more than 21,000 neurons. Analyses revealed a sequence of events across the neocortex starting from a resting state, to early stages of perception, and through the formation of a task response. At rest, the neocortex had one pattern of functional connections, identified through sets of areas that shared activity cofluctuations7,8. Within about 200 ms after the onset of the sensory stimulus, such connections rearranged, with different areas sharing cofluctuations and task-related information. During this short-lived state(approximately 300 ms duration), bothinter-area sensory data transmission and the redundancy of sensory encoding peaked, reflecting a transient increase in correlated fluctuations among task-related neurons. By around 0.5 s after stimulus onset, thevisual representation reached a more stable form, the structure of which was robust to the prominent, day-to-day variations in the responses of individual cells. About 1 s into stimulus presentation, a global fluctuation mode conveyed the upcoming response of the mouse to every area examined and was orthogonal to modes carrying sensory data. Overall, the neocortex supports sensory performance through brief elevations in sensory coding redundancynear the start of perception, neural populationcodes that are robust to cellular variability, and widespreadinter-area fluctuation modes that transmit sensory data and task responses in non-interfering channels.

Metabolic demands, sensory deficits: Tradeoffs in times of scarcity

The brain requires a lot of energy to carry out its functions at peak performance. In times of energy deficit, something has to give. In this issue of Neuron, Padamsey etal. (2021) explore how metabolic demands impact cortical coding by demonstrating the effects of food restriction on visual processing.

Basal Forebrain Cholinergic Neurons Selectively Drive Coordinated Motor Learning in Mice.

Motor control requires precise temporal and spatial encoding across distinct motor centers that is refined through the repetition of learning. The recruitment of motor regions requires modulatory input to shape circuit activity. Here, we identify a role for the baso-cortical cholinergic pathway in the acquisition of a coordinated motor skill in mice. Targeted depletion of basal forebrain cholinergic neurons results in significant impairments in training on the rotarod task of coordinated movement. Cholinergic neuromodulation is required during training sessions as chemogenetic inactivation of cholinergic neurons also impairs task acquisition. Rotarod learning is known to drive refinement of corticostriatal neurons arising in both medial prefrontal cortex (mPFC) and motor cortex, and we have found that cholinergic input to both motor regions is required for task acquisition. Critically, the effects of cholinergic neuromodulation are restricted to the acquisition stage, as depletion of basal forebrain cholinergic neurons after learning does not affect task execution. Our results indicate a critical role for cholinergic neuromodulation of distant cortical motor centers during coordinated motor learning.SIGNIFICANCE STATEMENT Acetylcholine release from basal forebrain cholinergic neuron terminals rapidly modulates neuronal excitability, circuit dynamics, and cortical coding; all processes required for processing complex sensory information, cognition, and attention. We found that depletion or transient silencing of cholinergic inputs to anatomically isolated motor areas, medial prefrontal cortex (mPFC) and motor cortex, selectively led to significant impairments on coordinated motor learning; disrupting this baso-cortical network after acquisition elicited no effect on task execution. Our results indicate a pivotal role for cholinergic neuromodulation of distant cortical motor centers during coordinated motor learning. These findings support the concept that cognitive components (such as attention) are indispensable in the adjustment of motor output and training-induced improvements in motor performance.

Probabilistically segregated neural circuits and subcritical linguistics.

Early studies of cortical information codes and memory capacity have assumed large neural networks, which, subject to evenly probable binary (on/off) activity, were found to be endowed with large storage and retrieval capacities under the Hebbian paradigm. Here, we show that such networks are plagued with exceedingly high cross-network connectivity, yielding long code words, which are linguistically non-realistic and difficult to memorize and comprehend. Noting that the neural circuit activity code is jointly governed by somatic and synaptic activity states, termed neural circuit polarities, we show that, subject to subcritical polarity probability, random-graph-theoretic considerations imply small neural circuit segregation. Such circuits are shown to represent linguistically plausible cortical code words which, in turn, facilitate storage and retrieval of both circuit connectivity and firing-rate dynamics.

Cortical Coding of Whisking Phase during Surface Whisking

In rodent whisker sensation, whisker position signals, including whisking phase, are integrated with touch signals to enable spatially accurate tactile perception, but other functions of phase coding are unclear. We investigate how phase coding affects the neural coding of surface features during surface whisking. In mice performing rough-smooth discrimination, S1 units exhibit much stronger phase tuning during surface whisking than in prior studies of whisking in air. Among putative pyramidal cells, preferred phase tiles phase space, but protraction phases are strongly over-represented. Fast-spiking units are nearly all protraction tuned. This protraction bias increases the coding of stick-slip whisker events during protraction, suggesting that surface features are preferentially encoded during protraction. Correspondingly, protraction-tuned units encode rough-smooth texture better than retraction-tuned units and encode the precise spatial location of surface ridges with higher acuity. This suggests that protraction is the main information-gathering phase for high-resolution surface features, with phase coding organized to support this function.

Vision and Locomotion Combine to Drive Path Integration Sequences in Mouse Retrosplenial Cortex.

The retrosplenial cortex (RSC) is involved in a broad range of cognitive functions, integrating rich sensory, motor, and spatial signals from multiple brain areas, including the hippocampal system. RSC neurons show hippocampus-dependent activity reminiscent of place cell sequences. Using cellular calcium imaging in a virtual reality (VR)-based locomotion task, we investigate how the integration of visual and locomotor inputs may give rise to such activity in RSC. A substantial population shows neural sequences that track position in the VR environment. This activity is driven by the conjunction of visual stimuli sequences and active movement, which is suggestive of path integration. The activity is anchored to a reference point and predominantly follows the VR upon manipulations of optic flow against locomotion. Thus, locomotion-gated optic flow, combined with the presence of contextual cues at the start of each trial, is sufficient to drive the sequential activity. A subpopulation shows landmark-related visual responses that are modulated by animal's position in the VR. Thus, rather than fragmenting the spatial representationinto equivalent locomotion-based ensemble versus optic-flow-based ensemble, in RSC, optic flow appears to override locomotion signals coherently in the population, when the gain betweenthe two signals is altered.

Fundamental bounds on the fidelity of sensory cortical coding.

How the brain processes information accurately despite stochastic neural activity is a longstanding question1. For instance, perception is fundamentally limited by the information that the brain can extract from the noisy dynamics of sensory neurons. Seminal experiments2,3 suggest that correlated noise in sensory cortical neural ensembles is what limits their coding accuracy4-6, although how correlated noise affects neural codes remains debated7-11. Recent theoretical work proposes that how a neural ensemble's sensory tuning properties relate statistically to its correlated noise patterns is a greater determinant of coding accuracy than is absolute noise strength12-14. However, without simultaneous recordings from thousands of cortical neurons with shared sensory inputs, it is unknown whether correlated noise limits coding fidelity. Here we present a 16-beam, two-photon microscope to monitor activity across the mouse primary visual cortex, along with analyses to quantify the information conveyed by large neural ensembles. We found that, in the visual cortex, correlated noise constrained signalling for ensembles with 800-1,300 neurons. Several noise components of the ensemble dynamics grew proportionally to the ensemble size and the encoded visual signals, revealing the predicted information-limiting correlations12-14. Notably, visual signals were perpendicular to the largest noise mode, which therefore did not limit coding fidelity. The information-limiting noise modes were approximately ten times smaller and concordant with mouse visual acuity15. Therefore, cortical design principles appear to enhance coding accuracy by restricting around 90% of noise fluctuations to modes that do not limit signalling fidelity, whereas much weaker correlated noise modes inherently bound sensory discrimination.

Targeted Cortical Manipulation of Auditory Perception

SummaryDriving perception by direct activation of neural ensembles in cortex is a necessary step for achieving a causal understanding of the neural code for auditory perception and developing central sensory rehabilitation methods. Here, using optogenetic manipulations during an auditory discrimination task in mice, we show that auditory cortex can be short-circuited by coarser pathways for simple sound identification. Yet when the sensory decision becomes more complex, involving temporal integration of information, auditory cortex activity is required for sound discrimination and targeted activation of specific cortical ensembles changes perceptual decisions, as predicted by our readout of the cortical code. Hence, auditory cortex representations contribute to sound discriminations by refining decisions from parallel routes.