Binary and analog variation of synapses between cortical pyramidal neurons

A myriad of pathological changes associated with epilepsy, including the loss of specific cell types, improper expression of individual ion channels, and synaptic sprouting, can be recast as decreases in cell and circuit heterogeneity. In recent experimental work, we demonstrated that biophysical diversity is a key characteristic of human cortical pyramidal cells, and past theoretical work has shown that neuronal heterogeneity improves a neural circuit’s ability to encode information. Viewed alongside the fact that seizure is an information-poor brain state, these findings motivate the hypothesis that epileptogenesis can be recontextualized as a process where reduction in cellular heterogeneity renders neural circuits less resilient to seizure onset. By comparing whole-cell patch clamp recordings from layer 5 (L5) human cortical pyramidal neurons from epileptogenic and non-epileptogenic tissue, we present the first direct experimental evidence that a significant reduction in neural heterogeneity accompanies epilepsy. We directly implement experimentally-obtained heterogeneity levels in cortical excitatory-inhibitory (E-I) stochastic spiking network models. Low heterogeneity networks display unique dynamics typified by a sudden transition into a hyper-active and synchronous state paralleling ictogenesis. Mean-field analysis reveals a distinct mathematical structure in these networks distinguished by multi-stability. Furthermore, the mathematically characterized linearizing effect of heterogeneity on input-output response functions explains the counter-intuitive experimentally observed reduction in single-cell excitability in epileptogenic neurons. This joint experimental, computational, and mathematical study showcases that decreased neuronal heterogeneity exists in epileptogenic human cortical tissue, that this difference yields dynamical changes in neural networks paralleling ictogenesis, and that there is a fundamental explanation for these dynamics based in mathematically characterized effects of heterogeneity. These interdisciplinary results provide convincing evidence that biophysical diversity imbues neural circuits with resilience to seizure and a new lens through which to view epilepsy, the most common serious neurological disorder in the world, that could reveal new targets for clinical treatment.

The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output. However, the majority of current attempts at biologically-plausible learning methods are either non-local in time, require highly specific connectivity motives, or have no clear link to any known mathematical optimization method. Here, we introduce Deep Feedback Control (DFC), a new learning method that uses a feedback controller to drive a deep neural network to match a desired output target and whose control signal can be used for credit assignment. The resulting learning rule is fully local in space and time and approximates Gauss-Newton optimization for a wide range of feedback connectivity patterns. To further underline its biological plausibility, we relate DFC to a multi-compartment model of cortical pyramidal neurons with a local voltage-dependent synaptic plasticity rule, consistent with recent theories of dendritic processing. By combining dynamical system theory with mathematical optimization theory, we provide a strong theoretical foundation for DFC that we corroborate with detailed results on toy experiments and standard computer-vision benchmarks.

Sensory experience and perceptual learning changes receptive field properties of cortical pyramidal neurons possibly mediated by long-term potentiation (LTP) of synapses. We have previously shown in the mouse somatosensory cortex (S1) that sensory-driven LTP in layer (L) 2/3 pyramidal neurons is dependent on higher order thalamic feedback from the posteromedial nucleus (POm), which is thought to convey contextual information from various cortical regions integrated with sensory input. We have followed up on this work by dissecting the cortical microcircuitry that underlies this form of LTP. We found that repeated pairing of Pom thalamocortical and intracortical pathway activity in brain slices induces NMDAr-dependent LTP of the L2/3 synapses that are driven by the intracortical pathway. Repeated pairing also recruits activity of vasoactive intestinal peptide (VIP) interneurons, whereas it reduces the activity of somatostatin (SST) interneurons. VIP interneuron-mediated inhibition of SST interneurons has been established as a motif for the disinhibition of pyramidal neurons. By chemogenetic interrogation we found that activation of this disinhibitory microcircuit motif by higher-order thalamic feedback is indispensable for eliciting LTP. Preliminary results in vivo suggest that VIP neuron activity also increases during sensory-evoked LTP. Together, this suggests that the higherorder thalamocortical feedback may help modifying the strength of synaptic circuits that process first-order sensory information in S1. To start characterizing the relationship between higher-order feedback and cortical plasticity during learning in vivo, we adapted a perceptual learning paradigm in which head-fixed mice have to discriminate two types of textures in order to obtain a reward. POm axons or L2/3 pyramidal neurons labeled with the genetically encoded calcium indicator GCaMP6s were imaged during the acquisition of this task as well as the subsequent learning of a new discrimination rule. We found that a subpopulation of the POm axons and L2/3 neurons dynamically represent textures. Moreover, upon a change in reward contingencies, a fraction of the L2/3 neurons re-tune their selectivity to the texture that is newly associated with the reward. Altogether, our data indicates that higher-order thalamic feedback can facilitate synaptic plasticity and may be implicated in dynamic sensory stimulus representations in S1, which depends on higher-order features that are associated with the stimuli.

Arguably one of the biggest mysteries in neuroscience is how the brain stores long-term memories. The major challenge for investigating the neural circuit underlying memory formation in the neocortex is the distributed nature of the resulting memory trace throughout the cortex. Here, we used a new behavioral paradigm that enabled us to generate memory traces in a specific cortical location and to specifically examine the mechanisms of memory formation in that region. We found that medial-temporal inputs arrive in neocortical layer 1 where the apical dendrites of cortical pyramidal neurons predominate. These dendrites have active properties that make them sensitive to contextual inputs from other areas that also send axons to layer 1 around the cortex. Blocking the influence of these medial-temporal inputs prevented learning and suppressed resulting dendritic activity. We conclude that layer 1 is the locus for hippocampal-dependent memory formation in the neocortex and propose that this process enhances the sensitivity of the tuft dendrites to contextual inputs.

The remarkable cognitive abilities characterizing humans has been linked to unique patterns of connectivity characterizing the neocortex. Comparative studies have shown that human cortical pyramidal neurons (PN) receive a significant increase of synaptic inputs when compared to other mammals, including non-human primates and rodents, but how this may relate to changes in cortical connectivity and function remained largely unknown. We previously identified a human-specific gene duplication (HSGD), SRGAP2C, that, when induced in mouse cortical PNs drives human-specific features of synaptic development, including a correlated increase in excitatory (E) and inhibitory (I) synapse density through inhibition of the ancestral SRGAP2A protein (Charrier et al. 2012; Fossatti et al. 2016; Schmidt et al. 2019). However, the origin and nature of this increased connectivity and its impact on cortical circuit function was unknown. I will present new results exploring these questions (see Schmidt et al. (2020) https://www.biorxiv.org/content/10.1101/852970v1). Using a combination of transgenic approaches and quantitative monosynaptic tracing, we discovered that humanization of SRGAP2C expression in the mouse cortex leads to a specific increase in local and long-range cortico-cortical inputs received by layer 2/3 cortical PNs. Moreover, using in vivo two-photon imaging in the barrel cortex of awake mice, we show that humanization of SRGAP2C expression increases the reliability and selectivity of sensory- evoked responses in layer 2/3 PNs. We also found that mice humanized for SRGAP2C in all cortical pyramidal neurons and throughout development are characterized by improved behavioural performance in a novel whisker-based sensory discrimination task compared to control wild-type mice. Our results suggest that the emergence of SRGAP2C during human evolution underlie a new substrate for human brain evolution whereby it led to increased local and long-range cortico-cortical connectivity and improved reliability of sensory-evoked cortical coding. References cited Charrier C.*, Joshi K. *, Coutinho-Budd J., Kim, J-E., Lambert N., de Marchena, J., Jin W-L., Vanderhaeghen P., Ghosh A., Sassa T, and Polleux F. (2012) Inhibition of SRGAP2 function by its human-specific paralogs induces neoteny of spine maturation. Cell 149:923-935. * Co-first authors. Fossati M, Pizzarelli R, Schmidt ER, Kupferman JV, Stroebel D, Polleux F*, Charrier C*. (2016) SRGAP2 and Its Human-Specific Paralog Co-Regulate the Development of Excitatory and Inhibitory Synapses. Neuron. 91(2):356-69. * Co-senior corresponding authors. Schmidt E.R.E., Kupferman J.V., Stackmann M., Polleux F. (2019) The human-specific paralogs SRGAP2 and SRGAP2C differentially modulate SRGAP2A-dependent synaptic development. Scientific Rep. 9(1):18692. Schmidt E.R.E, Zhao H.T., Hillman E.M.C., Polleux F. (2020) Humanization of SRGAP2C expression increases cortico-cortical connectivity and reliability of sensory-evoked responses in mouse brain. Submitted. See also: https://www.biorxiv.org/content/10.1101/852970v1