This webinar will showcase new approaches for electrophysiological recordings using our silicon neural probes and surface arrays combined with diverse optical methods such as wide-field or 2-photon imaging, fiber photometry, and optogenetic perturbations in awake, behaving mice. Multi-modal recording of single units and local field potentials across cortex, hippocampus and thalamus alongside calcium activity via GCaMP6F in cortical neurons in triple-transgenic animals or in hippocampal astrocytes via viral transduction are brought to bear to reveal hitherto inaccessible and under-appreciated aspects of coordinated dynamics in the brain.

In understanding circuit operations, a key issue is the extent to which neuronal spiking reflects local computation or responses to upstream inputs. Because pyramidal cells in CA1 do not have local recurrent projections, it is currently assumed that firing in CA1 is inherited from its inputs – thus, entorhinal inputs provide communication with the rest of the neocortex and the outside world, whereas CA3 inputs provide internal and past memory representations. Several studies have attempted to prove this hypothesis, by lesioning or silencing either area CA3 or the entorhinal cortex and examining the effect of firing on CA1 pyramidal cells. Despite the intense and careful work in this research area, the magnitudes and types of the reported physiological impairments vary widely across experiments. At least part of the existing variability and conflicts is due to the different behavioral paradigms, designs and evaluation methods used by different investigators. Simultaneous manipulations in the same animal or even separate manipulations of the different inputs to the hippocampal circuits in the same experiment are rare. To address these issues, I used optogenetic silencing of unilateral and bilateral mEC, of the local CA1 region, and performed bilateral pharmacogenetic silencing of the entire CA3 region. I combined this with high spatial resolution recording of local field potentials (LFP) in the CA1-dentate axis and simultaneously collected firing pattern data from thousands of single neurons. Each experimental animal had up to two of these manipulations being performed simultaneously. Silencing the medial entorhinal (mEC) largely abolished extracellular theta and gamma currents in CA1, without affecting firing rates. In contrast, CA3 and local CA1 silencing strongly decreased firing of CA1 neurons without affecting theta currents. Each perturbation reconfigured the CA1 spatial map. Yet, the ability of the CA1 circuit to support place field activity persisted, maintaining the same fraction of spatially tuned place fields, and reliable assembly expression as in the intact mouse. Thus, the CA1 network can maintain autonomous computation to support coordinated place cell assemblies without reliance on its inputs, yet these inputs can effectively reconfigure and assist in maintaining stability of the CA1 map.

The interaction between spontaneously and externally evoked neuronal activity is fundamental for a functional brain. Increasing evidence suggests that bursts of high-power oscillations in the 15-30 Hz beta-band represent activation of resting state networks and can mask perception of external cues. Yet demonstration of the effect of beta power modulation on perception in real-time is missing, and little is known about the underlying mechanism. In this talk I will present the methods we developed to fill this gap together with our recent results. We used a closed-loop stimulus-intensity adjustment system based on online burst-occupancy analyses in rats involved in a forepaw vibrotactile detection task. We found that the masking influence of burst-occupancy on perception can be counterbalanced in real-time by adjusting the vibration amplitude. Offline analysis of firing-rates and local field potentials across cortical layers and frequency bands confirmed that beta-power in the somatosensory cortex anticorrelated with sensory evoked responses. Mechanistically, bursts in all bands were accompanied by transient synchronization of cell assemblies, but only beta-bursts were followed by a reduction of firing-rate. Our closed loop approach reveals that spontaneous beta-bursts reflect a dynamic state that competes with external stimuli.

A recent formulation of predictive coding theory proposes that a subset of neurons in each cortical area encodes sensory prediction errors, the difference between predictions relayed from higher cortex and the sensory input. Here, we test for evidence of prediction error responses in spiking responses and local field potentials (LFP) recorded in primary visual cortex and area V4 of macaque monkeys, and in complementary electroencephalographic (EEG) scalp recordings in human participants. We presented a fixed sequence of visual stimuli on most trials, and violated the expected ordering on a small subset of trials. Under predictive coding theory, pattern-violating stimuli should trigger robust prediction errors, but we found that spiking, LFP and EEG responses to expected and pattern-violating stimuli were nearly identical. Our results challenge the assertion that a fundamental computational motif in sensory cortex is to signal prediction errors, at least those based on predictions derived from temporal patterns of visual stimulation.

Electro- and magneto-encephalography (EEG/MEG) are the leading methods to non-invasively record human neural dynamics with millisecond temporal resolution. However, it can be extremely difficult to infer the underlying cellular and circuit level origins of these macro-scale signals without simultaneous invasive recordings. This limits the translation of E/MEG into novel principles of information processing, or into new treatment modalities for neural pathologies. To address this need, we developed the Human Neocortical Neurosolver (HNN: https://hnn.brown/edu ), a new user-friendly neural modeling tool designed to help researchers and clinicians interpret human imaging data. A unique feature of HNN’s model is that it accounts for the biophysics generating the primary electric currents underlying such data, so simulation results are directly comparable to source localized data. HNN is being constructed with workflows of use to study some of the most commonly measured E/MEG signals including event related potentials, and low frequency brain rhythms. In this talk, I will give an overview of this new tool and describe an application to study the origin and meaning of 15-29Hz beta frequency oscillations, known to be important for sensory and motor function. Our data showed that in primary somatosensory cortex these oscillations emerge as transient high power ‘events’. Functionally relevant differences in averaged power reflected a difference in the number of high-power beta events per trial (“rate”), as opposed to changes in event amplitude or duration. These findings were consistent across detection and attention tasks in human MEG, and in local field potentials from mice performing a detection task. HNN modeling led to a new theory on the circuit origin of such beta events and suggested beta causally impacts perception through layer specific recruitment of cortical inhibition, with support from invasive recordings in animal models and high-resolution MEG in humans. In total, HNN provides an unpresented biophysically principled tool to link mechanism to meaning of human E/MEG signals.

Starting with the work of Hodgkin and Huxley in the 1950s, we now have a fairly good understanding of how the spiking activity of neurons can be modelled mathematically. For cortical circuits the understanding is much more limited. Most network studies have considered stylized models with a single or a handful of neuronal populations consisting of identical neurons with statistically identical connection properties. However, real cortical networks have heterogeneous neural populations and much more structured synaptic connections. Unlike typical simplified cortical network models, real networks are also “multipurpose” in that they perform multiple functions. Historically the lack of computational resources has hampered the mathematical exploration of cortical networks. With the advent of modern supercomputers, however, simulations of networks comprising hundreds of thousands biologically detailed neurons are becoming feasible (Einevoll et al, Neuron, 2019). Further, a large-scale biologically network model of the mouse primary visual cortex comprising 230.000 neurons has recently been developed at the Allen Institute for Brain Science (Billeh et al, Neuron, 2020). Using this model as a starting point, I will discuss how we can move towards multipurpose models that incorporate the true biological complexity of cortical circuits and faithfully reproduce multiple experimental observables such as spiking activity, local field potentials or two-photon calcium imaging signals. Further, I will discuss how such validated comprehensive network models can be used to gain insights into the functioning of cortical circuits.