Neuronal processing depends on the input-output (I/O) relation between the frequency of synaptic stimulation and the resultant axonal firing rate. The all-or-none properties of spike generation and active membrane mechanisms can make the neuronal I/O relation very steep. The ensuing nearly bimodal behavior may severely limit information coding, as minimal input fluctuations within the expected natural variability could cause neuronal output to jump between quiescence and maximum firing rate. Here, using biophysically and anatomically realistic computational models of individual neurons, we demonstrate that feed-forward inhibition, a ubiquitous mechanism in which inhibitory interneurons and their target cells are activated by the same excitatory input, can change a steeply sigmoid I/O curve into a double-sigmoid typical of buffer systems. The addition of an intermediate plateau stabilizes the spiking response over a broad dynamic range of input frequency, ensuring robust integration of noisy synaptic signals. Both the buffered firing rate and its input firing range can be independently and extensively modulated by biologically plausible changes in the weight and number of excitatory synapses on the feed-forward interneuron. By providing a soft switch between essentially digital and analog rate-code, this continuous control of the circuit I/O could dramatically increase the computational power of neuronal integration.

Feedforward inhibition (FFI) enables pyramidal cells in area CA1 of the hippocampus (CA1PCs) to remain easily excitable while faithfully representing a broad range of excitatory inputs without quickly saturating. Despite the cortical ubiquity of FFI, its specific function is not completely understood. FFI in CA1PCs is mediated by two physiologically and morphologically distinct GABAergic interneurons: fast-spiking, perisomatic-targeting basket cells and regular-spiking, dendritic-targeting bistratified cells. These two FFI pathways might create layer-specific computational sub-domains within the same CA1PC, but teasing apart their specific contributions remains experimentally challenging. We implemented a biophysically realistic model of CA1PCs using 40 digitally reconstructed morphologies and constraining synaptic numbers, locations, amplitude, and kinetics with available experimental data. First, we validated the model by reproducing the known combined basket and bistratified FFI of CA1PCs at the population level. We then analyzed how the two interneuron types independently affected the CA1PC spike probability and timing as a function of inhibitory strength. Separate FFI by basket and bistratified respectively modulated CA1PC threshold and gain. Concomitant FFI by both interneuron types synergistically extended the dynamic range of CA1PCs by buffering their spiking response to excitatory stimulation. These results suggest testable hypotheses on the precise effects of GABAergic diversity on cortical computation.

Cortical pyramidal cells store multiple features of complex synaptic input in individual dendritic branches and independently regulate the coupling between dendritic and somatic spikes. Branch points in apical trees exhibit wide ranges of sizes and shapes, and the large diameter ratio between trunk and oblique dendrites exacerbates impedance mismatch. The morphological diversity of dendritic bifurcations could thus locally tune neuronal excitability and signal integration. However, these aspects have never been investigated. Here, we first quantified the morphological variability of branch points from two-photon images of rat CA1 pyramidal neurons. We then investigated the geometrical features affecting spike initiation, propagation, and timing with a computational model validated by glutamate uncaging experiments. The results suggest that even subtle membrane readjustments at branch points could drastically alter the ability of synaptic input to generate, propagate, and time action potentials.

Neural networks that undergo acute insults display remarkable reorganization. This injury related plasticity is thought to permit recovery of function in the face of damage that cannot be reversed. Previously, an increase in the transmission strength at Schaffer collateral to CA1 pyramidal cell synapses was observed after long-term activity reduction in organotypic hippocampal slices. Here we report that, following acute preparation of adult rat hippocampal slices and surgical removal of area CA3, input to area CA1 was reduced and Schaffer collateral synapses underwent functional strengthening. This increase in synaptic strength was limited to Schaffer collateral inputs (no alteration to temporoammonic synapses) and acted to normalize postsynaptic discharge, supporting a homeostatic or compensatory response. Short-term plasticity was not altered, but an increase in immunohistochemical labeling of GluA1 subunits was observed in the stratum radiatum (but not stratum moleculare), suggesting increased numbers of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors and a postsynaptic locus of expression. Combined, these data support the idea that, in response to the reduction in presynaptic activity caused by removal of area CA3, Schaffer collateral synapses undergo a relatively rapid increase in functional efficacy likely supported by insertion of more AMPARs, which maintains postsynaptic excitability in CA1 pyramidal neurons. This novel fast compensatory plasticity exhibits properties that would allow it to maintain optimal network activity levels in the hippocampus, a brain structure lauded for its ongoing experience-dependent malleability.

The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of brain function, potentially paving the way for earlier and more accurate detection of brain disorders and better informed intervention protocols. Despite AI's ability to create accurate predictions and classifications, in most cases it lacks the ability to provide a mechanistic understanding of how inputs and outputs relate to each other. Explainable Artificial Intelligence (XAI) is a new set of techniques that attempts to provide such an understanding, here we report on some of these practical approaches. We discuss the potential value of XAI to the field of neurostimulation for both basic scientific inquiry and therapeutic purposes, as well as, outstanding questions and obstacles to the success of the XAI approach.