From neuronal populations to behavior: a computational journey

Cognitive behaviors originate in the responses of neuronal populations. We have a reasonable understanding of how the activity of a single neuron can be related to a specific behavior. However, it is still unclear how more complex behaviors are inferred from the responses of neuronal populations. This is a particularly timely problem because multi-neuronal recording techniques have recently become increasingly available, simultaneously spurring advances in the analysis of neuronal population data. These developments are, however, constrained by the challenges of combining theoretical and experimental approaches because both approaches have their unique set of constraints. A solution to this problem is to design computational models that are either derived or inspired by cortical computations

A normalization circuit of attention in primate lateral prefrontal cortex

The way in which visual neurons encode information pertaining to a cluttered scene with multiple stimuli, and subsequently filter behaviorally relevant information using attention remains poorly understood. Neurons of area 8a in the macaque lateral prefrontal cortex have been shown to encode visual and attentional signals. We trained two macaque monkeys in a visuospatial attention task and performed neurophysiological recordings to test how neurons in this area encode multiply presented stimuli and attentionally filter target stimuli from distractors. We found area 8a neuronal responses to several concurrently presented stimuli to resemble the average of individual responses to those stimuli when presented alone; this nonlinear response is characteristic of divisive normalization, a canonical brain computation seen to operate in various neural systems. Interestingly, the strength of normalization was dependent on visuospatial tuning, with neurons tuned for the ipsilateral visual hemifield displaying stronger normalized responses than those tuned for the contralateral hemifield. Furthermore, when presented with multiple stimuli and attending toward a target stimulus lying in the receptive field, contralateral-tuned neural activity increased and resembled that of when the target was presented alone (i.e. Winner-take-all response), whereas ipsilateral-tuned neurons were less modulated by attention and remained best-described by an average response. Taken together, our findings suggest a normalization circuit underlying attention in the primate lateral prefrontal cortex

Sustained Activation of PV+ Interneurons in Core Auditory Cortex Enables Robust Divisive Gain Control for Complex and Naturalistic Stimuli

Sensory cortices must flexibly adapt their operations to internal states and external requirements. Sustained modulation of activity levels in different inhibitory interneuron populations may provide network-level mechanisms for adjustment of sensory cortical processing on behaviorally relevant timescales. However, understanding of the computational roles of inhibitory interneuron modulation has mostly been restricted to effects at short timescales, through the use of phasic optogenetic activation and transient stimuli. Here, we investigated how modulation of inhibitory interneurons affects cortical computation on longer timescales, by using sustained, network-wide optogenetic activation of parvalbumin-positive interneurons (the largest class of cortical inhibitory interneurons) to study modulation of auditory cortical responses to prolonged and naturalistic as well as transient stimuli. We found highly conserved spectral and temporal tuning in auditory cortical neurons, despite a profound reduction in overall network activity. This reduction was predominantly divisive, and consistent across simple, complex, and naturalistic stimuli. A recurrent network model with power-law input–output functions replicated our results. We conclude that modulation of parvalbumin-positive interneurons on timescales typical of sustained neuromodulation may provide a means for robust divisive gain control conserving stimulus representations

Cortical Mechanisms Of Adaptation In Auditory Processing

Adaptation is computational strategy that underlies sensory nervous systems’ ability to accurately encode stimuli in various and dynamic contexts and shapes how animals perceive their environment. Many questions remain concerning how adaptation adjusts to particular stimulus features and its underlying mechanisms. In Chapter 2, we tested how neurons in the primary auditory cortex adapt to changes in stimulus temporal correlation. We used chronically implanted tetrodes to record neuronal spiking in rat primary auditory cortex during exposure to custom made dynamic random chord stimuli exhibiting different levels of temporal correlation. We estimated linear non-linear model for each neuron at each temporal correlation level, finding that neurons compensate for temporal correlation changes through gain-control adaptation. This experiment extends our understanding of how complex stimulus statistics are encoded in the auditory nervous system. In Chapter 3 and 4, we tested how interneurons are involved in adaptation by optogenetically suppressing parvalbumin-positive (PV) and somatostatin-positive (SOM) interneurons during tone train stimuli and using silicon probes to record neuronal spiking in mouse primary auditory cortex. In Chapter 3, we found that inhibition from both PVs and SOMs contributes to stimulus-specific adaptation (SSA) through different mechanisms. SOM inhibition was stimulus-specific, suppressing responses to standard tones more strongly than responses to deviant tones, and increasing with standard tone repetition. PVs amplified SSA because inhibition was similar for standard and deviant tones and PV mediated inhibition was insensitive to tone repetition. PVs and SOMs themselves exhibit SSA, and a Wilson-Cowan dynamic model identified that PVs and SOMs can directly contribute to SSA in pyramidal neurons. In Chapter 4, we tested how SOMs and PVs inhibition is modulated with the dynamics of adaptation and across frequency tuning, during exposure to single frequency tone trains across the neuron’s tuning curve. We found that the magnitude of SOM inhibition correlated with the magnitude of adaptive suppression, while PVs inhibition was largely insensitive to stimulus conditions. Together Chapters 3 and 4 implicate SOM inhibition in actively suppressing responses in a stimulus-specific manner while PV inhibition may passively enhance stimulus-specific suppression. These experiments inform the underlying principles and mechanisms of cortical sensory adaptation

Adjudicating between face-coding models with individual-face fMRI responses.

The perceptual representation of individual faces is often explained with reference to a norm-based face space. In such spaces, individuals are encoded as vectors where identity is primarily conveyed by direction and distinctiveness by eccentricity. Here we measured human fMRI responses and psychophysical similarity judgments of individual face exemplars, which were generated as realistic 3D animations using a computer-graphics model. We developed and evaluated multiple neurobiologically plausible computational models, each of which predicts a representational distance matrix and a regional-mean activation profile for 24 face stimuli. In the fusiform face area, a face-space coding model with sigmoidal ramp tuning provided a better account of the data than one based on exemplar tuning. However, an image-processing model with weighted banks of Gabor filters performed similarly. Accounting for the data required the inclusion of a measurement-level population averaging mechanism that approximates how fMRI voxels locally average distinct neuronal tunings. Our study demonstrates the importance of comparing multiple models and of modeling the measurement process in computational neuroimaging.This work was supported by the European Research Council (261352 awarded to NK), the UK Medical Research Council (MC_A060_5PR2 awarded to NK), and a British Academy Postdoctoral Fellowship (JDC)

Stochastic sampling provides a unifying account of visual working memory limits.

Research into human working memory limits has been shaped by the competition between different formal models, with a central point of contention being whether internal representations are continuous or discrete. Here we describe a sampling approach derived from principles of neural coding as a framework to understand working memory limits. Reconceptualizing existing models in these terms reveals strong commonalities between seemingly opposing accounts, but also allows us to identify specific points of difference. We show that the discrete versus continuous nature of sampling is not critical to model fits, but that, instead, random variability in sample counts is the key to reproducing human performance in both single- and whole-report tasks. A probabilistic limit on the number of items successfully retrieved is an emergent property of stochastic sampling, requiring no explicit mechanism to enforce it. These findings resolve discrepancies between previous accounts and establish a unified computational framework for working memory that is compatible with neural principles

Optogenetic interrogation of primary visual cortex and its impact on neural coding and behavior

Understanding the mechanism by which the brain transforms simple sensory inputs into rich perceptual experiences is one of the great mysteries of systems neuroscience. Undoubtedly this involves the activity of large populations of interconnected neurons, but while the responses of individual neurons to a variety of sensory stimuli have been well-characterized, how populations of such neurons organize their activity to create our sensory perceptions is almost entirely unknown. To investigate this complex circuitry requires the ability to causally manipulate the activity of neural populations and monitor the resultant effects. Here we focus on primary visual cortex (V1), which has been shown to be crucial for visual perception, and utilize optogenetic tools to render the activity of genetically- defined neural populations sensitive to light. By simultaneously recording and modulating (either driving or silencing) the activity of excitatory (glutamatergic) neurons, we are able to causally examine their role in visual perception. Here we report 3 major findings. First, we show that activating subpopulations of excitatory neurons can improve visual perception under certain conditions and that information in V1 used for perceptual decisions is integrated across spatially-limited populations of neurons. Further, we show that a key signature of this information integration is a reduction in correlated variability between neurons. Correlated variability has been implicated as a major source of behavioral choice related activity in the cortex, and theorized to be a major factor limiting information in cortical populations. However, until now, there has not been a way to manipulate correlations without altering firing rates or other task related variables. Here we demonstrate a novel method using optogenetic stimulation to causally manipulate correlated variability between cortical neurons without altering their firing rates. Lastly, with the goal of expanding the currently limited repertoire of optogenetic tools for non-human primates, we establish the viability of a novel optogenetic construct capable of dramatically silencing neural populations using a recently discovered anion conducting channelrhodopsin

Mechanisms of attention in visual cortex and the amygdala

Spatial attention enhances perception at specific locations in the visual field, measured behaviorally as improved task performance and faster reaction times. In visual cortex, neurons with receptive fields at attended locations display enhanced responses. This neural modulation is presumed to underlie the associated behavioral benefit, although the mechanisms linking sensory cortical modulation to perceptual enhancement remain unclear. In studies of spatial attention, experimentalists persuade animals to attend to particular locations by associating them with a higher probability or magnitude of reward. Notably, these manipulations alter in tandem both the absolute expectation of reward at a particular location, as well as the expectation of reward relative to other locations in the visual field. We reasoned that independently changing absolute and relative reward expectations could provide insight into the mechanisms of attention. We trained monkeys to discriminate the orientation of two stimuli presented simultaneously in different hemifields while independently varying the reward magnitude associated with correct discrimination at each location. Behavioral measures of attention were controlled by the relative value of each location. By contrast, neurons in visual area V4 were consistently modulated by absolute reward value, exhibiting increased firing rates, increased gamma-band power, and decreased trial-to-trial variability whenever receptive field locations were associated with large rewards. Thus, neural modulation in V4 can be robustly dissociated from the perceptual benefits of spatial attention; performance could be enhanced without neural modulation, and neural activity could be modulated without substantial perceptual improvement. These data challenge the notion that the perceptual benefits of spatial attention rely on increased signal-to-noise in V4. Instead, these benefits likely derive from downstream selection mechanisms. In identifying brain areas involved with attention, a distinction is generally made between sensory areas like V4— where the representation of the visual field is modulated by attentional state— and attentional “source" areas, primarily in the oculomotor system, that determine and control the locus of attention. The amygdala, long recognized for its role in mediating emotional responses, may also play a role in the control of attention. The amygdala sends prominent feedback projections to visual cortex, and recent physiological studies demonstrate that amygdala neurons carry spatial signals sufficient to guide attention. To characterize the role of the amygdala in the control of attention, we recorded neural activity in the amygdala and V4 simultaneously during performance of the orientation discrimination task. In preliminary data analysis, we note two sets of findings. First, consistent with prior work, we found that amygdala neurons combine information about space and value. Rewards both contralateral and ipsilateral to amygdala neurons modulated responses, but contralateral rewards had a larger effect. Therefore, notably distinct from known attentional control sources in the oculomotor system, spatial-reward responses in the amygdala do not reflect the relative value of locations. Second, we found signatures of functional connectivity between the amygdala and V4 during task performance. Reward cue presentation was associated with elevated alpha and beta coherence, and attention to locations contralateral to the amygdala and inside the receptive field of V4 neurons was associated with elevated inter-area gamma coherence. These results suggest that the amygdala may serve a unique role in the control of spatial attention. Together, these experiments contribute towards an understanding of the brain-to-behavior mechanisms linking neural activity in V4 and the amygdala to the dramatic perceptual and behavioral improvement associated with attention

Prefrontal rhythms for cognitive control

Goal-directed behavior requires flexible selection among action plans and updating behavioral strategies when they fail to achieve desired goals. Lateral prefrontal cortex (LPFC) is implicated in the execution of behavior-guiding rule-based cognitive control while anterior cingulate cortex (ACC) is implicated in monitoring processes and updating rules. Rule-based cognitive control requires selective processing while process monitoring benefits from combinatorial processing. I used a combination of computational and experimental methods to investigate how network oscillations and neuronal heterogeneity contribute to cognitive control through their effects on selective versus combinatorial processing modes in LPFC and ACC. First, I adapted an existing LPFC model to explore input frequency- and coherence-based output selection mechanisms for flexible routing of rate-coded signals. I show that the oscillatory states of input encoding populations can exhibit a stronger influence over downstream competition than their activity levels. This enables an output driven by a weaker resonant input signal to suppress lower-frequency competing responses to stronger, less resonant (though possibly higher-frequency) input signals. While signals are encoded in population firing rates, output selection and signal routing can be governed independently by the frequency and coherence of oscillatory inputs and their correspondence with output resonant properties. Flexible response selection and gating can be achieved by oscillatory state control mechanisms operating on input encoding populations. These dynamic mechanisms enable experimentally-observed LPFC beta and gamma oscillations to flexibly govern the selection and gating of rate-coded signals for downstream read-out. Furthermore, I demonstrate how differential drives to distinct interneuron populations can switch working memory representations between asynchronous and oscillatory states that support rule-based selection. Next, I analyzed physiological data from the LeBeau laboratory and built a de novo model constrained by the biological data. Experimental data demonstrated that fast network oscillations at both the beta- and gamma frequency bands could be elicited in vitro in ACC and neurons exhibited a wide range of intrinsic properties. Computational modeling of the ACC network revealed that the frequency of network oscillation generated was dependent upon the time course of inhibition. Principal cell heterogeneity broadened the range of frequencies generated by the model network. In addition, with different frequency inputs to two neuronal assemblies, heterogeneity decreased competition and increased spike coherence between the networks thus conferring a combinatorial advantage to the network. These findings suggest that oscillating neuronal populations can support either response selection (routing), or combination, depending on the interplay between the kinetics of synaptic inhibition and the degree of heterogeneity of principal cell intrinsic conductances. Such differences may support functional differences between the roles of LPFC and ACC in cognitive control

Noise in neural populations accounts for errors in working memory.

Errors in short-term memory increase with the quantity of information stored, limiting the complexity of cognition and behavior. In visual memory, attempts to account for errors in terms of allocation of a limited pool of working memory resources have met with some success, but the biological basis for this cognitive architecture is unclear. An alternative perspective attributes recall errors to noise in tuned populations of neurons that encode stimulus features in spiking activity. I show that errors associated with decreasing signal strength in probabilistically spiking neurons reproduce the pattern of failures in human recall under increasing memory load. In particular, deviations from the normal distribution that are characteristic of working memory errors and have been attributed previously to guesses or variability in precision are shown to arise as a natural consequence of decoding populations of tuned neurons. Observers possess fine control over memory representations and prioritize accurate storage of behaviorally relevant information, at a cost to lower priority stimuli. I show that changing the input drive to neurons encoding a prioritized stimulus biases population activity in a manner that reproduces this empirical tradeoff in memory precision. In a task in which predictive cues indicate stimuli most probable for test, human observers use the cues in an optimal manner to maximize performance, within the constraints imposed by neural noise