Three PhD students funded by BBSRC MIBTP. Please find more information on https://sites.google.com/site/jiankliu/join-us 1. Towards a functional model for associative learning and memory formation Drs Jian Liu and Rodrigo Quian Quiroga, CSN/NPB, University of Leicester 2. Neuronal coupling across spatiotemporal scales and dimensions of cortical population activity Drs Michael Okun and Jian Liu, CSN/NPB, University of Leicester 3. Decoding movement from single neurons in motor cortex and their subcortical targets Drs Todor Gerdjikov and Jian Liu, CSN/NPB, University of Leicester

Neural dynamics represent the hard-to-interpret substrate of circuit computations. Advances in large-scale recordings have highlighted the sheer spatiotemporal complexity of circuit dynamics within and across circuits, portraying in detail the difficulty of interpreting such dynamics and relating it to computation. Indeed, even in extremely simplified experimental conditions, one observes high-dimensional temporal dynamics in the relevant circuits. This complexity can be potentially addressed by the notion that not all changes in population activity have equal meaning, i.e., a small change in the evolution of activity along a particular dimension may have a bigger effect on a given computation than a large change in another. We term such conditions dimension-specific computation. Considering motor preparatory activity in a delayed response task we utilized neural recordings performed simultaneously with optogenetic perturbations to probe circuit dynamics. First, we revealed a remarkable robustness in the detailed evolution of certain dimensions of the population activity, beyond what was thought to be the case experimentally and theoretically. Second, the robust dimension in activity space carries nearly all of the decodable behavioral information whereas other non-robust dimensions contained nearly no decodable information, as if the circuit was setup to make informative dimensions stiff, i.e., resistive to perturbations, leaving uninformative dimensions sloppy, i.e., sensitive to perturbations. Third, we show that this robustness can be achieved by a modular organization of circuitry, whereby modules whose dynamics normally evolve independently can correct each other’s dynamics when an individual module is perturbed, a common design feature in robust systems engineering. Finally, we will recent work extending this framework to understanding the neural dynamics underlying preparation of speech.

Biological agents continually reconcile the internal states of their brain circuits with incoming sensory and environmental evidence to evaluate when and how to act. The brains of biological agents, including animals and humans, exploit many evolutionary innovations, chiefly modularity—observable at the level of anatomically-defined brain regions, cortical layers, and cell types among others—that can be repurposed in a compositional manner to endow the animal with a highly flexible behavioral repertoire. Accordingly, their behaviors show their own modularity, yet such behavioral modules seldom correspond directly to traditional notions of modularity in brains. It remains unclear how to link neural and behavioral modularity in a compositional manner. We propose a comprehensive framework—compositional modes—to identify overarching compositionality spanning specialized submodules, such as brain regions. Our framework directly links the behavioral repertoire with distributed patterns of population activity, brain-wide, at multiple concurrent spatial and temporal scales. Using whole-brain recordings of zebrafish brains, we introduce an unsupervised pipeline based on neural network models, constrained by biological data, to reveal highly conserved compositional modes across individuals despite the naturalistic (spontaneous or task-independent) nature of their behaviors. These modes provided a scaffolding for other modes that account for the idiosyncratic behavior of each fish. We then demonstrate experimentally that compositional modes can be manipulated in a consistent manner by behavioral and pharmacological perturbations. Our results demonstrate that even natural behavior in different individuals can be decomposed and understood using a relatively small number of neurobehavioral modules—the compositional modes—and elucidate a compositional neural basis of behavior. This approach aligns with recent progress in understanding how reasoning capabilities and internal representational structures develop over the course of learning or training, offering insights into the modularity and flexibility in artificial and biological agents.

A core aspect of clinical epileptology revolves around relating epileptic field potentials to underlying neural sources (e.g. an “epileptogenic focus”). Yet still, how neural population activity relates to epileptic field potentials and ultimately clinical phenomenology, remains far from being understood. After a brief overview on this topic, this seminar will focus on unpublished work, with an emphasis on seizure-related focal spreading depression. The presented results will include hippocampal and neocortical chronic in vivo two-photon population imaging and local field potential recordings of epileptic micronetworks in mice, in the context of viral encephalitis or optogenetic stimulation. The findings are corroborated by invasive depth electrode recordings (macroelectrodes and BF microwires) in epilepsy patients during pre-surgical evaluation. The presented work carries general implications for clinical epileptology, and basic epilepsy research.

Most brain functions involve interactions among multiple, distinct areas or nuclei. Yet our understanding of how populations of neurons in interconnected brain areas communicate is in its infancy. Using a population approach, we found that interactions between early visual cortical areas (V1 and V2) occur through a low-dimensional bottleneck, termed a communication subspace. In this talk, I will focus on the statistical methods we have developed for studying interactions between brain areas. First, I will describe Delayed Latents Across Groups (DLAG), designed to disentangle concurrent, bi-directional (i.e., feedforward and feedback) interactions between areas. Second, I will describe an extension of DLAG applicable to three or more areas, and demonstrate its utility for studying simultaneous Neuropixels recordings in areas V1, V2, and V3. Our results provide a framework for understanding how neuronal population activity is gated and selectively routed across brain areas.

Working memory (WM) is a cognitive function that allows the short-term maintenance and manipulation of information when no longer accessible to the senses. It relies on temporarily storing stimulus features in the activity of neuronal populations. To preserve these dynamics from distraction it has been proposed that pre and post-distraction population activity decomposes into orthogonal subspaces. If orthogonalization is necessary to avoid WM distraction, it should emerge as performance in the task improves. We sought evidence of WM orthogonalization learning and the underlying mechanisms by analyzing calcium imaging data from the prelimbic (PrL) and anterior cingulate (ACC) cortices of mice as they learned to perform an olfactory dual task. The dual task combines an outer Delayed Paired-Association task (DPA) with an inner Go-NoGo task. We examined how neuronal activity reflected the process of protecting the DPA sample information against Go/NoGo distractors. As mice learned the task, we measured the overlap between the neural activity onto the low-dimensional subspaces that encode sample or distractor odors. Early in the training, pre-distraction activity overlapped with both sample and distractor subspaces. Later in the training, pre-distraction activity was strictly confined to the sample subspace, resulting in a more robust sample code. To gain mechanistic insight into how these low-dimensional WM representations evolve with learning we built a recurrent spiking network model of excitatory and inhibitory neurons with low-rank connections. The model links learning to (1) the orthogonalization of sample and distractor WM subspaces and (2) the orthogonalization of each subspace with irrelevant inputs. We validated (1) by measuring the angular distance between the sample and distractor subspaces through learning in the data. Prediction (2) was validated in PrL through the photoinhibition of ACC to PrL inputs, which induced early-training neural dynamics in well-trained animals. In the model, learning drives the network from a double-well attractor toward a more continuous ring attractor regime. We tested signatures for this dynamical evolution in the experimental data by estimating the energy landscape of the dynamics on a one-dimensional ring. In sum, our study defines network dynamics underlying the process of learning to shield WM representations from distracting tasks.

What is the relationship between task, network architecture, and population activity in nonlinear deep networks? I will describe the Gated Deep Linear Network framework, which schematizes how pathways of information flow impact learning dynamics within an architecture. Because of the gating, these networks can compute nonlinear functions of their input. We derive an exact reduction and, for certain cases, exact solutions to the dynamics of learning. The reduction takes the form of a neural race with an implicit bias towards shared representations, which then govern the model’s ability to systematically generalize, multi-task, and transfer. We show how appropriate network architectures can help factorize and abstract knowledge. Together, these results begin to shed light on the links between architecture, learning dynamics and network performance.

The medial entorhinal cortex (MEC) hosts many of the brain’s circuit elements for spatial navigation and episodic memory, operations that require neural activity to be organized across long durations of experience. While location is known to be encoded by a plethora of spatially tuned cell types in this brain region, little is known about how the activity of entorhinal cells is tied together over time. Among the brain’s most powerful mechanisms for neural coordination are network oscillations, which dynamically synchronize neural activity across circuit elements. In MEC, theta and gamma oscillations provide temporal structure to the neural population activity at subsecond time scales. It remains an open question, however, whether similarly coordination occurs in MEC at behavioural time scales, in the second-to-minute regime. In this talk I will show that MEC activity can be organized into a minute-scale oscillation that entrains nearly the entire cell population, with periods ranging from 10 to 100 seconds. Throughout this ultraslow oscillation, neural activity progresses in periodic and stereotyped sequences. The oscillation sometimes advances uninterruptedly for tens of minutes, transcending epochs of locomotion and immobility. Similar oscillatory sequences were not observed in neighboring parasubiculum or in visual cortex. The ultraslow periodic sequences in MEC may have the potential to couple its neurons and circuits across extended time scales and to serve as a scaffold for processes that unfold at behavioural time scales.

We recorded and analyzed the population activity of hundreds of neurons in the medial premotor areas (MPC) of rhesus monkeys performing an isochronous tapping task guided by brief flashing stimuli or auditory tones. The animals showed a strong bias towards visual metronomes, with rhythmic tapping that was more precise and accurate than for auditory metronomes. The population dynamics in state space as well as the corresponding neural sequences shared the following properties across modalities: the circular dynamics of the neural trajectories and the neural sequences formed a regenerating loop for every produced interval, producing a relative time representation; the trajectories converged in similar state space at tapping times while the moving bumps restart at this point, resetting the beat-based clock; the tempo of the synchronized tapping was encoded by a combination of amplitude modulation and temporal scaling in the neural trajectories. In addition, the modality induced a displacement in the neural trajectories in auditory and visual subspaces without greatly altering time keeping mechanism. These results suggest that the interaction between the amodal internal representation of pulse within MPC and a modality specific external input generates a neural rhythmic clock whose dynamics define the temporal execution of tapping using auditory and visual metronomes.

Many decisions under uncertainty entail dynamic belief updating: multiple pieces of evidence informing about the state of the environment are accumulated across time to infer the environmental state, and choose a corresponding action. Traditionally, this process has been conceptualized as a linear and perfect (i.e., without loss) integration of sensory information along purely feedforward sensory-motor pathways. Yet, natural environments can undergo hidden changes in their state, which requires a non-linear accumulation of decision evidence that strikes a tradeoff between stability and flexibility in response to change. How this adaptive computation is implemented in the brain has remained unknown. In this talk, I will present an approach that my laboratory has developed to identify evidence accumulation signatures in human behavior and neural population activity (measured with magnetoencephalography, MEG), across a large number of cortical areas. Applying this approach to data recorded during visual evidence accumulation tasks with change-points, we find that behavior and neural activity in frontal and parietal regions involved in motor planning exhibit hallmarks signatures of adaptive evidence accumulation. The same signatures of adaptive behavior and neural activity emerge naturally from simulations of a biophysically detailed model of a recurrent cortical microcircuit. The MEG data further show that decision dynamics in parietal and frontal cortex are mirrored by a selective modulation of the state of early visual cortex. This state modulation is (i) specifically expressed in the alpha frequency-band, (ii) consistent with feedback of evolving belief states from frontal cortex, (iii) dependent on the environmental volatility, and (iv) amplified by pupil-linked arousal responses during evidence accumulation. Together, our findings link normative decision computations to recurrent cortical circuit dynamics and highlight the adaptive nature of decision-related long-range feedback processing in the brain.

Magnitude estimation and stimulus discrimination tasks are affected by perceptual biases that cause the stimulus parameter to be perceived as shifted toward the mean of its distribution. These biases have been extensively studied in psychophysics and, more recently and to a lesser extent, with neural activity recordings. New computational techniques allow us to train spiking recurrent neural networks on the tasks used in the experiments. This provides us with another valuable tool with which to investigate the network mechanisms responsible for the biases and how behavior could be modeled. As an example, in this talk I will consider networks trained to discriminate the durations of temporal intervals. The trained networks presented the contraction bias, even though they were trained with a stimulus sequence without temporal correlations. The neural activity during the delay period carried information about the stimuli of the current trial and previous trials, this being one of the mechanisms that originated the contraction bias. The population activity described trajectories in a low-dimensional space and their relative locations depended on the prior distribution. The results can be modeled as an ideal observer that during the delay period sees a combination of the current and the previous stimuli. Finally, I will describe how the neural trajectories in state space encode an estimate of the interval duration. The approach could be applied to other cognitive tasks.

A central goal of vision science is to understand the principles underlying the perception and neural coding of the complex visual environment of our everyday experience. In the visual cortex, foundational work with artificial stimuli, and more recent work combining natural images and deep convolutional neural networks, have revealed much about the tuning of cortical neurons to specific image features. However, a major limitation of this existing work is its focus on single-neuron response strength to isolated images. First, during natural vision, the inputs to cortical neurons are not isolated but rather embedded in a rich spatial and temporal context. Second, the full structure of population activity—including the substantial trial-to-trial variability that is shared among neurons—determines encoded information and, ultimately, perception. In the first part of this talk, I will argue for a normative approach to study encoding of natural images in primary visual cortex (V1), which combines a detailed understanding of the sensory inputs with a theory of how those inputs should be represented. Specifically, we hypothesize that V1 response structure serves to approximate a probabilistic representation optimized to the statistics of natural visual inputs, and that contextual modulation is an integral aspect of achieving this goal. I will present a concrete computational framework that instantiates this hypothesis, and data recorded using multielectrode arrays in macaque V1 to test its predictions. In the second part, I will discuss how we are leveraging this framework to develop deep probabilistic algorithms for natural image and video segmentation.

We use the olfactory system and forebrain of (adult) zebrafish as a model to analyze how relevant information is extracted from sensory inputs, how information is stored in memory circuits, and how sensory inputs inform behavior. A series of recent findings provides evidence that inhibition has not only homeostatic functions in neuronal circuits but makes highly specific, instructive contributions to behaviorally relevant computations in different brain regions. These observations imply that the connectivity among excitatory and inhibitory neurons exhibits essential higher-order structure that cannot be determined without dense network reconstructions. To analyze such connectivity we developed an approach referred to as “dynamical connectomics” that combines 2-photon calcium imaging of neuronal population activity with EM-based dense neuronal circuit reconstruction. In the olfactory bulb, this approach identified specific connectivity among co-tuned cohorts of excitatory and inhibitory neurons that can account for the decorrelation and normalization (“whitening”) of odor representations in this brain region. These results provide a mechanistic explanation for a fundamental neural computation that strictly requires specific network connectivity.

Coping with threatening situations requires both identifying stimuli predicting danger and selecting adaptive behavioral responses in order to survive. The dorso medial prefrontal cortex (dmPFC) is a critical structure involved in the regulation of threat-related behaviour, yet it is still largely unclear how threat-predicting stimuli and defensive behaviours are associated within prefrontal networks in order to successfully drive adaptive responses. To address these questions, we used a combination of extracellular recordings, neuronal decoding approaches, and optogenetic manipulations to show that threat representations and the initiation of avoidance behaviour are dynamically encoded in the overall population activity of dmPFC neurons. These data indicate that although dmPFC population activity at stimulus onset encodes sustained threat representations and discriminates threat- from non-threat cues, it does not predict action outcome. In contrast, transient dmPFC population activity prior to action initiation reliably predicts avoided from non-avoided trials. Accordingly, optogenetic inhibition of prefrontal activity critically constrained the selection of adaptive defensive responses in a time-dependent manner. These results reveal that the adaptive selection of active fear responses relies on a dynamic process of information linking threats with defensive actions unfolding within prefrontal networks.

Interoception, the sense of internal bodily signals, is essential for physiological homeostasis, cognition, and emotions. While human insular cortex (InsCtx) is implicated in interoception, the cellular and circuit mechanisms remain unclear. I will describe our recent work imaging mouse InsCtx neurons during two physiological deficiency states – hunger and thirst. InsCtx ongoing activity patterns reliably tracked the gradual return to homeostasis, but not changes in behavior. Accordingly, while artificial induction of hunger/thirst in sated mice via activation of specific hypothalamic neurons (AgRP/SFOGLUT) restored cue-evoked food/water-seeking, InsCtx ongoing activity continued to reflect physiological satiety. During natural hunger/thirst, food/water cues rapidly and transiently shifted InsCtx population activity to the future satiety-related pattern. During artificial hunger/thirst, food/water cues further shifted activity beyond the current satiety-related pattern. Together with circuit-mapping experiments, these findings suggest that InsCtx integrates visceral-sensory inputs regarding current physiological state with hypothalamus-gated amygdala inputs signaling upcoming ingestion of food/water, to compute a prediction of future physiological state.

The ability to simultaneously record the activity from tens to thousands to tens of thousands of neurons has allowed us to analyze the computational role of population activity as opposed to single neuron activity. Recent work on a variety of cortical areas suggests that neural function may be built on the activation of population-wide activity patterns, the neural modes, rather than on the independent modulation of individual neural activity. These neural modes, the dominant covariation patterns within the neural population, define a low dimensional neural manifold that captures most of the variance in the recorded neural activity. We refer to the time-dependent activation of the neural modes as their latent dynamics. As an example, we focus on the ability to execute learned actions in a reliable and stable manner. We hypothesize that the ability to perform a given behavior in a consistent manner requires that the latent dynamics underlying the behavior also be stable. The stable latent dynamics, once identified, allows for the prediction of various behavioral features, using models whose parameters remain fixed throughout long timespans. We posit that latent cortical dynamics within the manifold are the fundamental and stable building blocks underlying consistent behavioral execution.

Odor memories are exceptionally robust and essential for the survival of many species. In rodents, the olfactory cortex shows features of an autoassociative memory network and plays a key role in the retrieval of olfactory memories (Meissner-Bernard et al., 2019). Interestingly, the telencephalic area Dp, the zebrafish homolog of olfactory cortex, transiently enters a state of precise balance during the presentation of an odor (Rupprecht and Friedrich, 2018). This state is characterized by large synaptic conductances (relative to the resting conductance) and by co-tuning of excitation and inhibition in odor space and in time at the level of individual neurons. Our aim is to understand how this precise synaptic balance affects memory function. For this purpose, we build a simplified, yet biologically plausible spiking neural network model of Dp using experimental observations as constraints: besides precise balance, key features of Dp dynamics include low firing rates, odor-specific population activity and a dominance of recurrent inputs from Dp neurons relative to afferent inputs from neurons in the olfactory bulb. To achieve co-tuning of excitation and inhibition, we introduce structured connectivity by increasing connection probabilities and/or strength among ensembles of excitatory and inhibitory neurons. These ensembles are therefore structural memories of activity patterns representing specific odors. They form functional inhibitory-stabilized subnetworks, as identified by the “paradoxical effect” signature (Tsodyks et al., 1997): inhibition of inhibitory “memory” neurons leads to an increase of their activity. We investigate the benefits of co-tuning for olfactory and memory processing, by comparing inhibitory-stabilized networks with and without co-tuning. We find that co-tuned excitation and inhibition improves robustness to noise, pattern completion and pattern separation. In other words, retrieval of stored information from partial or degraded sensory inputs is enhanced, which is relevant in light of the instability of the olfactory environment. Furthermore, in co-tuned networks, odor-evoked activation of stored patterns does not persist after removal of the stimulus and may therefore subserve fast pattern classification. These findings provide valuable insights into the computations performed by the olfactory cortex, and into general effects of balanced state dynamics in associative memory networks.

The ability to simultaneously record the activity from tens to thousands and maybe even tens of thousands of neurons has allowed us to analyze the computational role of population activity as opposed to single neuron activity. Recent work on a variety of cortical areas suggests that neural function may be built on the activation of population-wide activity patterns, the neural modes, rather than on the independent modulation of individual neural activity. These neural modes, the dominant covariation patterns within the neural population, define a low dimensional neural manifold that captures most of the variance in the recorded neural activity. We refer to the time-dependent activation of the neural modes as their latent dynamics, and argue that latent cortical dynamics within the manifold are the fundamental and stable building blocks of neural population activity.

Classic studies show that in many species – from leech and cricket to primate – responses of neural populations can be quite successfully read out using a measure neural population activity termed the population vector. However, despite its successes, detailed analyses have shown that the standard population vector discards substantial amounts of information contained in the responses of a neural population, and so is unlikely to accurately describe how signal communication between parts of the nervous system. I will describe recent theoretical results showing how to modify the population vector expression in order to read out neural responses without information loss, ideally. These results make it possible to quantify the contribution of weakly tuned neurons to perception. I will also discuss numerical methods that can be used to minimize information loss when reading out responses of large neural populations.

During learning, populations of neurons alter their connectivity and activity patterns, enabling the brain to construct a model of the external world. Conventional wisdom holds that the durability of a such a model is reflected in the stability of neural responses and the stability of synaptic connections that form memory engrams. However, recent experimental findings have challenged this idea, revealing that neural population activity in circuits involved in sensory perception, motor planning and spatial memory continually change over time during familiar behavioural tasks. This continual change suggests significant redundancy in neural representations, with many circuit configurations providing equivalent function. I will describe recent work that explores the consequences of such redundancy for learning and for task representation. Despite large changes in neural activity, we find cortical responses in sensorimotor tasks admit a relatively stable readout at the population level. Furthermore, we find that redundancy in circuit connectivity can make a task easier to learn and compensate for deficiencies in biological learning rules. Finally, if neuronal connections are subject to an unavoidable level of turnover, the level of plasticity required to optimally maintain a memory is generally lower than the total change due to turnover itself, predicting continual reconfiguration of an engram.

A central question in motor physiology has been whether motor cortex activity resembles muscle activity, and if not, why not? Over fifty years, extensive observations have failed to provide a concise answer, and the topic remains much debated. To provide a different perspective, we employed a novel behavioral paradigm that affords extensive comparison between time-evolving neural and muscle activity. Single motor-cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid ’trajectory tangling’: moments where similar activity patterns led to dissimilar future patterns. Avoidance of trajectory tangling was present across tasks and species. Network models revealed a potential reason for this consistent feature: low tangling confers noise robustness. Remarkably, we were able to predict motor cortex activity from muscle activity alone, by leveraging the hypothesis that muscle-like commands are embedded in additional structure that yields low tangling. Our results argue that motor cortex embeds descending commands in additional structure that ensure low tangling, and thus noise-robustness. The dominant structure in motor cortex may thus serve not a representational function (encoding specific variables) but a computational function: ensuring that outgoing commands can be generated reliably. Our results establish the utility of an emerging approach: understanding the structure of neural activity based on properties of population geometry that flow from normative principles such as noise robustness.

Coping with threatening situations requires both identifying stimuli predicting danger and selecting adaptive behavioral responses in order to survive. The dorso medial prefrontal cortex (dmPFC) is a critical structure involved in the regulation of threat-related behaviour, yet it is still largely unclear how threat-predicting stimuli and defensive behaviours are associated within prefrontal networks in order to successfully drive adaptive responses. To address these questions, we used a combination of extracellular recordings, neuronal decoding approaches, and optogenetic manipulations to show that threat representations and the initiation of avoidance behaviour are dynamically encoded in the overall population activity of dmPFC neurons. These data indicate that although dmPFC population activity at stimulus onset encodes sustained threat representations and discriminates threat- from non-threat cues, it does not predict action outcome. In contrast, transient dmPFC population activity prior to action initiation reliably predicts avoided from non-avoided trials. Accordingly, optogenetic inhibition of prefrontal activity critically constrained the selection of adaptive defensive responses in a time-dependent manner. These results reveal that the adaptive selection of active fear responses relies on a dynamic process of information linking threats with defensive actions unfolding within prefrontal networks.

Sensory representations are typically thought as neuronal activity patterns that encode physical attributes of the outside world. However, increasing evidence is showing that as animals learned the association between a sensory stimulus and its behavioral relevance, stimulus representation in sensory cortical areas can change. In this seminar I will present recent experiments from our lab showing that the activity in the olfactory piriform cortex (PC) of mice encodes not only odor information, but also non-olfactory variables associated with the behavioral task. By developing an associative olfactory learning task, in which animals learn to associate a particular context with an odor and a reward, we were able to record the activity of multiple neurons as the animal runs in a virtual reality corridor. By analyzing the population activity dynamics using Principal Components Analysis, we find different population trajectories evolving through time that can discriminate aspects of different trial types. By using Generalized Linear Models we further dissected the contribution of different sensory and non-sensory variables to the modulation of PC activity. Interestingly, the experiments show that variables related to both sensory and non-sensory aspects of the task (e.g., odor, context, reward, licking, sniffing rate and running speed) differently modulate PC activity, suggesting that the PC adapt odor processing depending on experience and behavior.

Neurons in the primary visual cortex (V1) encode the orientation and contrast of visual stimuli through changes in firing rate (Hubel and Wiesel, 1962). Their activity typically peaks at a preferred orientation and decays to zero at the orientations that are orthogonal to the preferred. This activity pattern is re-scaled by contrast but its shape is preserved, a phenomenon known as contrast invariance. Contrast-invariant selectivity is also observed at the population level in V1 (Carandini and Sengpiel, 2004). The mechanisms supporting the emergence of contrast-invariance at the population level remain unclear. How does the activity of different neurons with diverse orientation selectivity and non-linear contrast sensitivity combine to give rise to contrast-invariant population selectivity? Theoretical studies have shown that in the balance limit, the properties of single-neurons do not determine the population activity (van Vreeswijk and Sompolinsky, 1996). Instead, the synaptic dynamics (Mongillo et al., 2012) as well as the intracortical connectivity (Rosenbaum and Doiron, 2014) shape the population activity in balanced networks. We report that short-term plasticity can change the synaptic strength between neurons as a function of the presynaptic activity, which in turns modifies the population response to a stimulus. Thus, the same circuit can process a stimulus in different ways –linearly, sublinearly, supralinearly – depending on the properties of the synapses. We found that balanced networks with excitatory to excitatory short-term synaptic plasticity cannot be contrast-invariant. Instead, short-term plasticity modifies the network selectivity such that the tuning curves are narrower (broader) for increasing contrast if synapses are facilitating (depressing). Based on these results, we wondered whether balanced networks with plastic synapses (other than short-term) can support the emergence of contrast-invariant selectivity. Mathematically, we found that the only synaptic transformation that supports perfect contrast invariance in balanced networks is a power-law release of neurotransmitter as a function of the presynaptic firing rate (in the excitatory to excitatory and in the excitatory to inhibitory neurons). We validate this finding using spiking network simulations, where we report contrast-invariant tuning curves when synapses release the neurotransmitter following a power- law function of the presynaptic firing rate. In summary, we show that synaptic plasticity controls the type of non-linear network response to stimulus contrast and that it can be a potential mechanism mediating the emergence of contrast invariance in balanced networks with orientation-dependent connectivity. Our results therefore connect the physiology of individual synapses to the network level and may help understand the establishment of contrast-invariant selectivity.

Genetic targeting of neuronal cells with activity reporters (calcium or voltage indicators) has initiated the paradigmatic transition whereby photons have replaced electrons for reading large-scale brain activities at cellular resolution. This has alleviated the limitations of single cell or extracellular electrophysiological probing, which only give access to the activity of at best a few neurons simultaneously and to population activity of unresolved cellular origin, respectively. In parallel, optogenetics has demonstrated that targeting neuronal cells with photosensitive microbial opsins, enables the transduction of photons into electrical currents of opposite polarities thus writing, through activation or inhibition, neuronal signals in a non-invasive way. These progresses have in turn stimulated the development of sophisticated optical methods to increase spatial and temporal resolution, light penetration depth and imaging volume. Today, nonlinear microscopy, combined with spatio-temporal wave front shaping, endoscopic probes engineering or multi scan heads design, enable in vivo in depth, simultaneous recording of thousands of cells in mm 3 volumes at single-spike precision and single-cell resolution. Joint progress in opsin engineering, wave front shaping and laser development have provided the methodology, that we named circuits optogenetics, to control single or multiple target activity independently in space and time with single- neuron and single-spike precision, at large depths. Here, we will review the most significant breakthroughs of the past years, which enable reading and writing neuronal activity at the relevant spatiotemporal scale for brain circuits manipulation, with particular emphasis on the most recent advances in circuit optogenetics.

Learning is a population phenomenon. Since it is the organized activity of populations of neurons that cause movement, learning a new skill must involve reshaping those population activity patterns. Seeing how the brain does this has been elusive, but a brain-computer interface approach can yield new insight. We presented monkeys with novel BCI mappings that we knew would be difficult for them to learn how to control. Over several days, we observed the emergence of new patterns of neural activity that endowed the animals with the ability to perform better at the BCI task. We speculate that there also exists a direct relationship between new patterns of neural activity and new abilities during natural movements, but it is much harder to see in that setting.

Visual processing begins in the retina: within only two synaptic layers, multiple parallel feature channels emerge, which relay highly processed visual information to different parts of the brain. To functionally characterize these feature channels we perform calcium and glutamate population activity recordings at different levels of the mouse retina. This allows following the complete visual signal across consecutive processing stages in a systematic way. In my talk, I will summarize our recent findings on the functional diversity of retinal output channels and how they arise within the retinal network. Specifically, I will talk about the role of inhibition and cell-type specific dendritic processing in generating diverse visual channels. Then, I will focus on how color – a single visual feature – emerges across all retinal processing layers and link our results to behavioral output and the statistics of mouse natural scenes. With our approach, we hope to identify general computational principles of retinal signaling, thereby increasing our understanding of what the eye tells the brain.

Despite the consistency of symptoms at the cognitive level, we now know that brain disorders like Autism and Schizophrenia can each arise from mutations in >100 different genes. Presumably there is a convergence of “symptoms” at the level of neural circuits in diagnosed individuals. In this talk I will argue that redundancy in neural circuit parameters implies that we should take a circuit-function rather that circuit-component approach to understanding these disorders. Then I will present our recent empirical work testing a circuit-function theory for Autism: the idea that neural circuits show excess trial-to-trial variability in response to sensory stimuli, and instability in the representations across a timescale of days. For this we analysed in vivo neural population activity data recorded from somatosensory cortex of mouse models of Fragile-X syndrome, a disorder related to autism. Work with Beatriz Mizusaki (Univ of Bristol), Nazim Kourdougli, Anand Suresh, and Carlos Portera-Cailliau (Univ of California, Los Angeles).

Olfactory navigation is essential for the survival of living beings from unicellular organisms to mammals. In the wild, rodents combine odor information with an internal spatial representation of the environment for foraging and navigation. What are the neural circuits in the brain that implement these behaviours? My research addresses this question by examining the synaptic circuits and neural population activity in the olfactory cortex to understand the integration of olfactory and spatial information. Primary olfactory (piriform) cortex (PCx) has long been recognized as a highly associative brain structure. What is the behavioural and functional role of these associative synapses in PCx? We designed an odor-cued navigation task, where rats must use both olfactory and spatial information to obtain water rewards. We recorded from populations of posterior piriform cortex (pPCx) neurons during behaviour and found that individual neurons were not only odor-selective, but also fired differentially to the same odor sampled at different locations, forming an “olfactory place map”. Spatial locations can be decoded from simultaneously recorded pPCx population, and spatial selectivity is maintained in the absence of odors, across behavioural contexts. This novel olfactory place map is consistent with our finding for a dominant role of associative excitatory synapses in shaping PCx representations, and suggest a role for PCx spatial representations in supporting olfactory navigation. This work not only provides insight into the neural basis for how odors can be used for navigation, but also reveals PCx as a prime site for addressing the general question of how sensory information is anchored within memory systems and combined with cognitive maps to guide flexible behaviour.

Information is represented in the brain by the coordinated activity of populations of neurons. Recent large-scale neural recording methods in combination with machine learning algorithms are helping understand how sensory processing and cognition emerge from neural population activity. This talk will explore the most popular machine learning methods used to gather meaningful low-dimensional representations from higher-dimensional neural recordings. To illustrate the potential of these approaches, Pedro will present his research in which chemical information is decoded from the olfactory system of the mouse for technological applications. Pedro and co-researchers have successfully extracted odor identity and concentration from olfactory receptor neuron low-dimensional activity trajectories. They have further developed a novel method to identify a shared latent space that allowed decoding of odor information across animals.

Animals perform learned actions with remarkable consistency for years after acquiring a skill. What is the neural correlate of this stability? We explore this question from the perspective of neural populations. Recent work suggests that the building blocks of neural function may be the activation of population-wide activity patterns: neural modes that capture the dominant co-variation patterns of population activity and define a task specific low dimensional neural manifold. The time-dependent activation of the neural modes results in latent dynamics. We hypothesize that the latent dynamics associated with the consistent execution of a behaviour need to remain stable, and use an alignment method to establish this stability. Once identified, stable latent dynamics allow for the prediction of various behavioural features via fixed decoder models. We conclude that latent cortical dynamics within the task manifold are the fundamental and stable building blocks underlying consistent behaviour.

Bursts in gamma and other frequency ranges are thought to contribute to the efficiency of working memory or communication tasks. Abnormalities in bursts have also been associated with motor and psychiatric disorders. The determinants of burst generation are not known, specifically how single cell and connectivity parameters influence burst statistics and the corresponding brain states. We first present a generic mathematical model for burst generation in an excitatory-inhibitory (EI) network with self-couplings. The resulting equations for the stochastic phase and envelope of the rhythm’s fluctuations are shown to depend on only two meta-parameters that combine all the network parameters. They allow us to identify different regimes of amplitude excursions, and to highlight the supportive role that network finite-size effects and noisy inputs to the EI network can have. We discuss how burst attributes, such as their durations and peak frequency content, depend on the network parameters. In practice, the problem above follows the a priori challenge of fitting such E-I spiking networks to single neuron or population data. Thus, the second part of the talk will discuss a novel method to fit mesoscale dynamics using single neuron data along with a low-dimensional, and hence statistically tractable, single neuron model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous ‘pools’ of neurons, and modelling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to either optimize parameters by gradient ascent on the log-likelihood, or to perform Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. We illustrate this approach using an E-I network of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived. We show that both single-neuron and connectivity parameters can be adequately recovered from simulated data.