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SummaryWhen stimulated, neural populations in the visual cortex exhibit fast rhythmic activity with frequencies in the gamma band (30-80 Hz). The gamma rhythm manifests as a broad resonance peak in the powerspectrum of recorded local field potentials, which exhibits various stimulus dependencies. In particular, in macaque primary visual cortex (V1), the gamma peak frequency increases with increasing stimulus contrast. Moreover, this contrast dependence is local: when contrast varies smoothly over visual space, the gamma peak frequency in each cortical column is controlled by the local contrast in that column’s receptive field. No parsimonious mechanistic explanation for these contrast dependencies of V1 gamma oscillations has been proposed. The stabilized supralinear network (SSN) is a mechanistic model of cortical circuits that has accounted for a range of visual cortical response nonlinearities and contextual modulations, as well as their contrast dependence. Here, we begin by showing that a reduced SSN model without retinotopy robustly captures the contrast dependence of gamma peak frequency, and provides a mechanistic explanation for this effect based on the observed non-saturating and supralinear input-output function of V1 neurons. Given this result, the local dependence on contrast can trivially be captured in a retinotopic SSN which however lacks horizontal synaptic connections between its cortical columns. However, long-range horizontal connections in V1 are in fact strong, and underlie contextual modulation effects such as surround suppression. We thus explored whether a retinotopically organized SSN model of V1 with strong excitatory horizontal connections can exhibit both surround suppression and the local contrast dependence of gamma peak frequency. We found that retinotopic SSNs can account for both effects, but only when the horizontal excitatory projections are composed of two components with different patterns of spatial fall-off with distance: a short-range component that only targets the source column, combined with a long-range component that targets columns neighboring the source column. We thus make a specific qualitative prediction for the spatial structure of horizontal connections in macaque V1, consistent with the columnar structure of cortex.

Neural responses to a localized visual stimulus are modulated by the content of its surrounding. This phenomenon manifests in several forms of contextual modulation, including three interrelated properties of the visual cortex: surround suppression, inverse response and surround facilitation. We devise a unified biologically realistic circuit model accounting for all these phenomena and show that i) surround suppression in L2/3 is only partially due to the recruitment of lateral inhibition; ii) long-range feedback projections are necessary for inverse response and iii) the width of the response profile in the feedback layer determines inverse size tuning. The model predicts the modulations induced by silencing somatostatin-expressing cells or higher visual areas or changing the stimulus contrast. These predictions are consistent with the experimental observations when available and can be tested in existing setups otherwise. We then show the robustness of the identified mechanisms in a model with three interneuron subclasses, built to fit the classical responses and able to predict inverse size-tuning curves.HighlightsOne model explains three different types of contextual modulation: (classical) surround suppression, (inverse) response to ‘holes’ in full field drifting gratings and cross orientation surround facilitation.Feedback, feedforward and lateral inhibitory inputs contribute to classical surround suppression in L2/3 of mouse V1 in different amounts.Observed responses to ‘holes’ in full field drifting gratings require long-range feedback projections.Surround modulation to the response to ‘holes’ in full field drifting gratings requires an increase in the characteristic length scale of the spatial pattern of activity in higher visual areas.The mechanisms uncovered by an analytically tractable model are also at work in a cell-type specific model that predicts response to a ‘hole’ stimulus.

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

SummaryWhen monkeys learn to group visual stimuli into arbitrary categories, lateral intraparietal area (LIP) neurons become category-selective. Surprisingly, the representations of learned categories are overwhelmingly biased: nearly all LIP neurons in a given animal prefer the same category over other behaviorally equivalent categories. We propose a model where such biased representations develop through the interplay between Hebbian plasticity and the recurrent connectivity of LIP. In this model, two separable processes of positive feedback unfold in parallel: in one, category selectivity emerges from competition between prefrontal inputs; in the other, bias develops due to lateral interactions among LIP neurons. This model reproduces the levels of category selectivity and bias observed under a variety of conditions, as well as the redevelopment of bias after monkeys learn redefined categories. It predicts that LIP receptive fields would spatially cluster by preferred category, which we experimentally confirm. In summary, our model reveals a mechanism by which LIP learns abstract representations and assigns meaning to sensory inputs.

OBJECTIVE/BACKGROUND Baseline neutrophil/lymphocyte ratio (NLR), a surrogate marker for systemic inflammation and immunosuppression, is a well-studied prognostic marker in non-small cell lung cancer (NSCLC). This study tests if interim NLR is prognostic in NSCLC patients in remission. METHODS This single-center, retrospective cohort study analyzed 131 NSCLC patients treated from 2010 to 2015 who achieved complete remission. NLR was calculated at baseline and from the first available blood sample during remission. Kaplan-Meier estimates of overall survival (OS) and time to recurrence were compared using the log-rank test for trend. Multivariable analysis was conducted using the Cox proportional hazards model. RESULTS Of the 131 cases, 63 had subsequently recurred at the last follow-up. The mean age was 64 ± 10 years. Patients with stage I (35%), II (24%), and III (41%) were included. Histology were adenocarcinoma (60%), squamous cell (33%), and unspecified (7%). The majority of patients were smokers. For the univariate analysis interim NLR was binned into tertiles, NLR 4.08. Of those with an interim NLR > 4.08, prognosis and recurrence risk were higher. In the multivariable analysis, remission NLR was strongly prognostic for OS (p

1AbstractA cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon – whether behavioral or a pattern of neural activity – and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example inferring conductance parameters in a circuit model of the stomatogastric ganglion. Then, with recurrent neural networks of increasing size, we show that EPI allows precise control over the behavior of inferred parameters, and that EPI scales better in parameter dimension than alternative techniques. In the remainder of this work, we present novel theoretical findings gained through the examination of complex parametric structure captured by EPI. In a model of primary visual cortex, we discovered how connectivity with multiple inhibitory subtypes shapes variability in the excitatory population. Finally, in a model of superior colliculus, we identified and characterized two distinct regimes of connectivity that facilitate switching between opposite tasks amidst interleaved trials, characterized each regime via insights afforded by EPI, and found conditions where these circuit models reproduce results from optogenetic silencing experiments. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.

Sickle cell hepatopathy is a well-described but uncommonly seen complication of sickle cell disease and is usually caused by multiple overlapping processes. A more acute liver complication is hepatic sequestration which is important to recognize in order to initiate life-saving treatment. A 33-year-old woman with sickle cell disease complicated by painful crises, splenic infarction and significant alcohol abuse presented with gastrointestinal distress, pain crisis, acute-on-chronic anemia, and hyperbilirubinemia in the setting of greater than baseline alcohol consumption. She was found to have hepatomegaly, encephalopathy, severe jaundice, and severe hyperbilirubinemia. She was treated with red cell exchange and supportive care which resulted in an improvement in her symptoms as well as hyperbilirubinemia. She was discharged with plans for monthly red cell exchange, iron chelation therapy, and close monitoring of liver disease was planned upon discharge. This case illustrates that chronic liver disease can occur in sickle cell disease (Hgb SS) especially in the setting of acquired iron overload. More acutely, sequestration is a serious and life-threatening complication of sickle cell disease that can culminate in acute liver failure. Primary treatment for hepatic sequestration is red cell exchange along with management of contributing comorbidities, and symptomatic management of encephalopathy. In end-stage liver disease, transplantation may be considered in the context of the patient’s clinical status.

12102 Backgroun1d: Timely identification of palliative care needs have the ability to reduce hospitalizations and improve QOL. The Supportive & Palliative Care Indicators Tool (SPICT) is used to identify patients with advanced stage medical conditions who may need special care planning. The Rothman Index (RI) detects patients at high risk of acutely decompensating in the inpatient setting and has been validated to assess 24-hour mortality risk. We used SPICT and RI in cancer patients admitted to the hospital and evaluated their roles in recognizing early palliative care needs and 6-month mortality. Methods: Advanced/metastatic cancer patients admitted to our institution from Jan 1, 2019 to June 30, 2019 were retrospectively reviewed. Patient demographics, length of hospital stay (LOS), comorbidities, palliative/hospice care referrals, vital status, initial RI score, and computed SPICT scores were obtained. Worse clinical indicators were defined as SPICT positive if it met > 2 clinical indicators or RI < 60. Univariate and bivariate analyses were performed. Results: A total of 227 patients were included, mean age 68, 34% Caucasians, 63% Blacks, 59% female, median comorbidities of 3, with majority having lung and GI malignancies. A total of 137 (60%) were SPICT +, 47 (21%) had RI < 60, and 38 (17%) concurrent SPICT + and RI < 60. SPICT + patients were more likely to have longer hospital stay, change in code status, more palliative/hospice referrals, and increased mortality. Those with RI < 60 had similar results (Table). SPICT + patients are more likely to have RI < 60 (p = 0.0013). Conclusions: SPICT and RI are valuable tools in predicting 6-month mortality and palliative/hospice care referrals. These can also be utilized to initiate early palliative and goals of care discussions in patients with advanced cancer. [Table: see text]

The cortical microcircuit can dynamically adjust to dramatic changes in the strength, scale, and complexity of its input. In the primary visual cortex (V1), pyramidal cells (PCs) integrate widely across space when signals are weak, but narrowly when signals are strong, a phenomenon known as contrast-dependent surround suppression. Theoretical work has proposed that local interneurons could mediate a shift from cooperation to competition of PCs across cortical space, underlying this computation. We combined calcium imaging and electrophysiology to constrain a stabilized supralinear network model that explains how the four principal cell types in layer 2/3 (L2/3) of mouse V1– somatostatin (SST), parvalbumin (PV), and vasoactive intestinal peptide (VIP) interneurons, and PCs– transform inputs from layer 4 (L4) PCs to encode drifting gratings of varying size and contrast. Using bidirectional optogenetic perturbations, we confirmed key predictions of the model. Our data and modeling showed that recurrent amplification drives a transition from a positive PC→VIP⊣SST⊣PC feedback loop at small size and low contrast to a negative PC→SST⊣PC feedback loop at large size and high contrast to contribute to this flexible computation. This may represent a widespread mechanism for gating competition across cortical space to optimally meet task demands.

Electrophysiological recording in the primary visual cortex (V1) of mammals have revealed a number of complex interactions between the center and surround. Understanding the underlying circuit mechanisms is crucial to understanding fundamental brain computations. In this paper we address the following phenomena that have been observed in V1 of animals with orientation maps: 1) surround suppression that is accompanied by a decrease in the excitatory and inhibitory currents that the cell receives as the stimulus size increases beyond the cell’s summation field; 2) surround tuning to the center orientation, in which the strongest suppression arises when the surround orientation matches that of the center stimulus; and 3) feature-specific suppression, in which a surround stimulus of a given orientation specifically suppresses that orientation’s component of the response to a center plaid stimulus. We show that a stabilized supralinear network that has biologically plausible connectivity and synaptic efficacies that depend on cortical distance and orientation difference between neurons can consistently reproduce all the above phenomena. We explain the mechanism behind each result, and argue that feature-specific suppression and surround tuning to the center orientation are independent phenomena. Specifically, if we remove some aspects of the connectivity from the model it will still produce feature-specific suppression but not surround tuning to the center orientation. We also show that in the model the activity decay time constant is similar to the cortical activity decay time constant reported in mouse V1. Our model indicates that if the surround activates neurons that fall within the reach of the horizontal projections in V1, the above mentioned phenomena can be generated by V1 alone without the need of cortico-cortical feedback. Finally, we show that these results hold both in networks with rate-based units and with conductance-based spiking units. This demonstrates that the stabilized supra-linear network mechanism can be achieved in the more biological context of spiking networks.

Predicting the response of the cortical microcircuit to perturbations is a prerequisite to determine the mechanisms that mediate its response to stimulus; yet, an encompassing perspective that describes the full ensemble of the network’s response in models that accurately recapitulate recorded data is still lacking. Here we develop a class of mathematically tractable models that exactly describe the modulation of the distribution of cell-type-specific calcium-imaging activity with the contrast of a visual stimulus. The inferred parameters recover signatures of the connectivity structure found in mouse visual cortex. Analysis of this structure subsequently reveals parameter-independent relations between the responses of different cell types to perturbations and each interneuron’s role in circuit-stabilization. Leveraging recent theoretical approaches, we derive explicit expressions for the distribution of responses to partial perturbations which reveal a novel, counter intuitive effect in the sign of response functions. Finally applying the theory to inferring feedback to V1 during locomotion, we find that it is predominantly mediated by both SOM and VIP modulation.

Objective: Most cutaneous squamous cell carcinomas (cSCC) are low risk and can be treated with simple excision or ablation. High-risk cSCC require invasive treatment, including radical surgery. We present our experience in treating invasive cSCC of the pelvis and extremities. Method: A retrospective review of the data of patients with invasive cSCC, indicated for surgery between 2014 and 2018, from a single institution was carried out. Results: A total of 19 patients (nine men, 10 women) were included in the study. Mean age was 62 years; mean tumour size was 8.6cm). Of the 19 patients, five patients with paraplegia with cSCC arising from hard-to-heal ulcers died of infection or bleeding after surgery or systemic therapy. Also, nine patients with localised cSCC underwent margin-negative resection with or without radiation; one patient experienced disease relapse. Of the participants, two patients with previous transplants and multifocal aggressive cSCC underwent numerous resections but succumbed to disease, and two patients who presented with locally recurrent disease after previous positive margin resection and radiation underwent re-resection but developed recurrent disease. Conclusions: Prognosis for invasive cSCC largely depends on clinical setting. Tumours arising from ulcers in patients with paraplegia have a poor prognosis regardless of treatment. Invasive cSCC in transplant patients are often multifocal and often recur. Debulking procedures are associated with local recurrence despite radiation. Patients presenting with localised disease have a favourable prognosis with wide resection, flap coverage and adjuvant therapy.

An inhibition-stabilized network (ISN) is a network of excitatory and inhibitory cells at a stable fixed point of firing rates for a given input, for which the excitatory subnetwork would be unstable if inhibitory rates were frozen at their fixed point values. It has been shown that in a low-dimensional model (one unit per neuronal subtype) of an ISN with a single excitatory and single inhibitory cell type, the inhibitory unit shows a “paradoxical” response, lowering (raising) its steady-state firing rate in response to addition to it of excitatory (inhibitory) input. This has been generalized to an ISN with multiple inhibitory cell types: if input is given only to inhibitory cells, the steady-state inhibition received by excitatory cells changes paradoxically, that is, it decreases (increases) if the steady-state excitatory firing rates decrease (increase).We generalize these analyses of paradoxical effects to low-dimensional networks with multiple cell types of both excitatory and inhibitory neurons. The analysis depends on the connectivity matrix of the network linearized about a given fixed point, and its eigenvectors or “modes”. We show the following: (1) A given cell type shows a paradoxical change in steady-state rate in response to input it receives, if and only if the network with that cell type omitted has an odd number of unstable modes. Excitatory neurons can show paradoxical responses when there are two or more inhibitory subtypes. (2) More generally, if the cell types are divided into two nonoverlapping subsets A and B, then subset B has an odd (even) number of modes that show paradoxical response if and only if subset A has an odd (even) number of unstable modes. (3) The net steady-state inhibition received by any unstable mode of the excitatory subnetwork will change paradoxically, i.e. in the same direction as the change in amplitude of that mode. In particular, this means that a sufficient condition to determine that a network is an ISN is if, in response to an input only to inhibitory cells, the firing rates of and inhibition received by all excitatory cell types all change in the same direction. This in turn will be true if all E cells and all inhibitory cell types that connect to E cells change their firing rates in the same direction.

Hyperprogression associated with immunotherapy has been reported previously with melanoma, non-small cell lung cancer (NSCLC), renal, and urothelial cancers but not with sarcoma. A 63-year old man with a biopsy-proven, localized 13 cm high-grade myxoid/round cell liposarcoma of the thigh was treated with concurrent, neoadjuvant checkpoint inhibitor immunotherapy and radiotherapy. After his subsequent wide surgical resection, he developed small hepatic lesions that rapidly progressed and caused his death, raising the possibility of hyperprogression in this entity.

Context guides perception by influencing stimulus saliency. Accordingly, in visual cortex, responses to a stimulus are modulated by context, the visual scene surrounding the stimulus. Responses are suppressed when stimulus and surround are similar but not when they differ. The underlying mechanisms remain unclear. Here, we use optical recordings, manipulations, and computational modeling to show that disinhibitory circuits consisting of vasoactive intestinal peptide (VIP)-expressing and somatostatin (SOM)-expressing inhibitory neurons modulate responses in mouse visual cortex depending on similarity between stimulus and surround, primarily by modulating recurrent excitation. When stimulus and surround are similar, VIP neurons are inactive, and activity of SOM neurons leads to suppression of excitatory neurons. However, when stimulus and surround differ, VIP neurons are active, inhibiting SOM neurons, which leads to relief of excitatory neurons from suppression. We have identified a canonical cortical disinhibitory circuit that contributes to contextual modulation and may regulate perceptual saliency.

SO_SCPLOWUMMARYC_SCPLOWContext strongly guides perception by influencing the saliency of sensory stimuli. Accordingly, sensory neurons respond differently to the same stimulus depending on the context in which the stimulus is embedded. In the visual system, the relationship between the features of the stimulus and those of the surround determine how excitatory neurons are modulated. Their responses are suppressed when center and surround share the same features but not when they are different. The mechanisms that relieve surround suppression and thereby generate contextual modulation remain unclear. Here we show that the disinhibitory circuit consisting of somatostatin-expressing (SOM) and vasoactive-intestinal-peptide-expressing (VIP) inhibitory neurons contextually modulates excitatory neurons in mouse primary visual cortex. When the stimulus and the surround share the same features, VIP neurons are inactive and SOM neurons can suppress excitatory neurons. However, when the features of the stimulus differ from those of the surround, VIP neurons are activated, thereby inhibiting SOM neurons and relieving excitatory neurons from suppression. Optogenetic silencing of VIP neurons, via disinhibition of SOM neurons, reduces the contextual modulation of excitatory neurons. We have identified a canonical cortical disinhibitory circuit which contributes to contextual modulation of excitatory neurons, and may thereby regulate perceptual saliency.

Advanced non-small-cell lung cancer (NSCLC) remains a challenging disease. The limited utility of chemotherapy indicates the need for additional therapeutic options. Targeted therapy continues to be an important tool in the treatment of NSCLC. Mutations within the RAS-RAF-MEK-MAPK pathway, specifically the BRAF V600E mutation, have become an important target for the subset of NSCLC patients with this mutation. This paper summarizes the clinical evidence that lead to the recent approval of the combination of dabrafenib and trametinib to treat patients with advanced NSCLC who harbor a BRAF V600E mutation.

In the visual system, the response to a stimulus in a neuron’s receptive field can be modulated by stimulus context, and the strength of these contextual influences vary with stimulus intensity. Recent work has shown how a theoretical model, the stabilized supralinear network (SSN), can account for such modulatory influences, using a small set of computational mechanisms. While the predictions of the SSN have been confirmed in primary visual cortex (V1), its computational principles apply with equal validity to any cortical structure. We have therefore tested the generality of the SSN by examining modulatory influences in the middle temporal area (MT) of the macaque visual cortex, using electrophysiological recordings and pharmacological manipulations. We developed a novel stimulus that can be adjusted parametrically to be larger or smaller in the space of all possible motion directions. We found, as predicted by the SSN, that MT neurons integrate across motion directions for low-contrast stimuli, but that they exhibit suppression by the same stimuli when they are high in contrast. These results are analogous to those found in visual cortex when stimulus size is varied in the space domain. We further tested the mechanisms of inhibition using pharmacologically manipulations of inhibitory efficacy. As predicted by the SSN, local manipulation of inhibitory strength altered firing rates, but did not change the strength of surround suppression. These results are consistent with the idea that the SSN can account for modulatory influences along different stimulus dimensions and in different cortical areas.Significance StatementVisual neurons are selective for specific stimulus features in a region of visual space known as the receptive field, but can be modulated by stimuli outside of the receptive field. The SSN model has been proposed to account for these and other modulatory influences, and tested in V1. As this model is not specific to any particular stimulus feature or brain region, we wondered whether similar modulatory influences might be observed for other stimulus dimensions and other regions. We tested for specific patterns of modulatory influences in the domain of motion direction, using electrophysiological recordings from MT. Our data confirm the predictions of the SSN in MT, suggesting that the SSN computations might be a generic feature of sensory cortex.

Purpose: High-dose-rate electronic brachytherapy (EBT) provides a non-surgical treatment option for non-melanoma skin cancer (NMSC). This matched-pair cohort study compared the outcomes of treatment with EBT to those of Mohs micrographic surgery (MMS) in patients with NMSC. Material and methods : At four treatment centers, patients treated with EBT were case matched to patients treated with MMS based on retrospectively-collected patient age, lesion size, location and type, and year of treatment. Follow-up data were prospectively collected and included local recurrence, toxicities, cosmesis, and patient-reported outcomes. Results: The 369 patients (188 in the EBT treatment group and 181 in the MMS treatment group) had 416 lesions (208 in the EBT group and 208 in the MMS group), including 226 basal cell carcinomas (BCC) and 190 squamous cell carcinomas (SCC). Most patients were Caucasian (98.9% and 99.5%) and male (65.4% and 66.3%) of median age 80.7 (range: 61-98) (EBT) and 76.8 (range: 51-98) years (MMS). Most lesions were size > 1 cm and ≤ 2 cm, and located on the head. At mean 3.4 years post-treatment, 99.5% of EBT, and 100.0% of MMS-treated lesions were free of recurrence (p = ns). One recurrence was noted in the EBT group. Physicians rated cosmesis as “excellent” or “good” in 97.6% of EBT-treated lesions, and 95.7% of MMS-treated lesions. Conclusions : This matched-pair cohort study supports the use of EBT as an effective non-surgical treatment option for NMSC with equivalent recurrence rates and cosmetic outcomes to MMS in appropriately-selected patients with early stage NMSC at extended follow-up.

Selective visual attention modulates neural activity in the visual system in complex ways and leads to enhanced performance on difficult visual tasks. Here, we show that a simple circuit model, the stabilized supralinear network, gives a unified account of a wide variety of effects of attention on neural responses. We replicate results from studies of both feature and spatial attention, addressing findings in a variety of experimental paradigms on changes both in firing rates and in correlated neural variability. Finally, we expand this circuit model into an architecture that can perform visual tasks—a convolutional neural network—in order to show that these neural effects can enhance detection performance. This work provides the first unified mechanistic account of the effects of attention on neural and behavioral responses.

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In the case of artificial neural networks, the three components specified by design are the objective functions, the learning rules, and architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.

Many studies have shown that the excitation and inhibition received by cortical neurons remain roughly balanced across many conditions. A key question for understanding the dynamical regime of cortex is the nature of this balancing. Theorists have shown that network dynamics can yield systematic cancellation of most of a neuron's excitatory input by inhibition. We review a wide range of evidence pointing to this cancellation occurring in a regime in which the balance is loose, meaning that the net input remaining after cancellation of excitation and inhibition is comparable in size to the factors that cancel, rather than tight, meaning that the net input is very small relative to the cancelling factors. This choice of regime has important implications for cortical functional responses, as we describe: loose balance, but not tight balance, can yield many nonlinear population behaviors seen in sensory cortical neurons, allow the presence of correlated variability, and yield decrease of that variability with increasing external stimulus drive as observed across multiple cortical areas., 21 pages, 2 figures

One of the most fundamental organizational principles of the brain is the separation of excitatory (E) and inhibitory (I) neurons. In addition to their opposing effects on post-synaptic neurons, E and I cells tend to differ in their selectivity and connectivity. Although many such differences have been characterized experimentally, it is not clear why they exist in the first place. We studied this question in an artificial neural network equipped with multiple E and I cell types. We found that a deep convolutional recurrent network trained to perform an object classification task was able to capture salient distinctions between E and I neurons. We explored the necessary conditions for the network to develop distinct selectivity and connectivity across cell types. We found that neurons that project to higher-order areas will have greater stimulus selectivity, regardless of whether they are excitatory or not. Sparser connectivity is required for higher selectivity, but only when the recurrent connections are excitatory. These findings demonstrate that the differences observed across E and I neurons are not independent, and can be explained using a smaller number of factors.