Neural Readout of a Latency Code in the Active Electrosensory System

The latency of spikes relative to a stimulus conveys sensory information across modalities. However, in most cases it remains unclear whether and how such latency codes are utilized by postsynaptic neurons. In the active electrosensory system of mormyrid fish, a latency code for stimulus amplitude in electroreceptor afferent nerve fibers (EAs) is hypothesized to be read out by a central reference provided by motor corollary discharge (CD). Here we demonstrate that CD enhances sensory responses in postsynaptic granular cells of the electrosensory lobe, but is not required for reading out EA input. Instead, diverse latency and spike count tuning across the EA population gives rise to graded information about stimulus amplitude that can be read out by standard integration of converging excitatory synaptic inputs. Inhibitory control over the temporal window of integration renders two granular cell subclasses differentially sensitive to information derived from relative spike latency versus spike count.

Dynamic Spatiotemporal Pattern Recognition with Recurrent Spiking Neural Network

Our real-time actions in everyday life reflect a range of spatiotemporal dynamic brain activity patterns, the consequence of neuronal computation with spikes in the brain. Most existing models with spiking neurons aim at solving static pattern recognition tasks such as image classification. Compared with static features, spatiotemporal patterns are more complex due to their dynamics in both space and time domains. Spatiotemporal pattern recognition based on learning algorithms with spiking neurons therefore remains challenging. We propose an end-to-end recurrent spiking neural network model trained with an algorithm based on spike latency and temporal difference backpropagation. Our model is a cascaded network with three layers of spiking neurons where the input and output layers are the encoder and decoder, respectively. In the hidden layer, the recurrently connected neurons with transmission delays carry out high-dimensional computation to incorporate the spatiotemporal dynamics of the inputs. The test results based on the data sets of spiking activities of the retinal neurons show that the proposed framework can recognize dynamic spatiotemporal patterns much better than using spike counts. Moreover, for 3D trajectories of a human action data set, the proposed framework achieves a test accuracy of 83.6% on average. Rapid recognition is achieved through the learning methodology–based on spike latency and the decoding process using the first spike of the output neurons. Taken together, these results highlight a new model to extract information from activity patterns of neural computation in the brain and provide a novel approach for spike-based neuromorphic computing.

FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency

Neural modelling tools are increasingly employed to describe, explain, and predict the human brain’s behavior. Among them, spiking neural networks (SNNs) make possible the simulation of neural activity at the level of single neurons, but their use is often threatened by the resources needed in terms of processing capabilities and memory. Emerging applications where a low energy burden is required (e.g. implanted neuroprostheses) motivate the exploration of new strategies able to capture the relevant principles of neuronal dynamics in reduced and efficient models. The recent Leaky Integrate-and-Fire with Latency (LIFL) spiking neuron model shows some realistic neuronal features and efficiency at the same time, a combination of characteristics that may result appealing for SNN-based brain modelling. In this paper we introduce FNS, the first LIFL-based SNN framework, which combines spiking/synaptic modelling with the event-driven approach, allowing us to define heterogeneous neuron groups and multi-scale connectivity, with delayed connections and plastic synapses. FNS allows multi-thread, precise simulations, integrating a novel parallelization strategy and a mechanism of periodic dumping. We evaluate the performance of FNS in terms of simulation time and used memory, and compare it with those obtained with neuronal models having a similar neurocomputational profile, implemented in NEST, showing that FNS performs better in both scenarios. FNS can be advantageously used to explore the interaction within and between populations of spiking neurons, even for long time-scales and with a limited hardware configuration.

nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift

The recent “multi-neuronal spike sequence detector” (MNSD) architecture integrates the weight- and delay-adjustment methods by combining heterosynaptic plasticity with the neurocomputational feature spike latency, representing a new opportunity to understand the mechanisms underlying biological learning. Unfortunately, the range of problems to which this topology can be applied is limited because of the low cardinality of the parallel spike trains that it can process, and the lack of a visualization mechanism to understand its internal operation. We present here the nMNSD structure, which is a generalization of the MNSD to any number of inputs. The mathematical framework of the structure is introduced, together with the “trapezoid method,” that is a reduced method to analyze the recognition mechanism operated by the nMNSD in response to a specific input parallel spike train. We apply the nMNSD to a classification problem previously faced with the classical MNSD from the same authors, showing the new possibilities the nMNSD opens, with associated improvement in classification performances. Finally, we benchmark the nMNSD on the classification of static inputs (MNIST database) obtaining state-of-the-art accuracies together with advantageous aspects in terms of time- and energy-efficiency if compared to similar classification methods.

Switching in Cerebellar Stellate Cell Excitability in Response to a Pair of Inhibitory/Excitatory Presynaptic Inputs: A Dynamical System Perspective

Cerebellar stellate cells form inhibitory synapses with Purkinje cells, the sole output of the cerebellum. Upon stimulation by a pair of varying inhibitory and fixed excitatory presynaptic inputs, these cells do not respond to excitation (i.e., do not generate an action potential) when the magnitude of the inhibition is within a given range, but they do respond outside this range. We previously used a revised Hodgkin–Huxley type of model to study the nonmonotonic first-spike latency of these cells and their temporal increase in excitability in whole cell configuration (termed run-up). Here, we recompute these latency profiles using the same model by adapting an efficient computational technique, the two-point boundary value problem, that is combined with the continuation method. We then extend the study to investigate how switching in responsiveness, upon stimulation with presynaptic inputs, manifests itself in the context of run-up. A three-dimensional reduced model is initially derived from the original six-dimensional model and then analyzed to demonstrate that both models exhibit type 1 excitability possessing a saddle-node on an invariant cycle (SNIC) bifurcation when varying the amplitude of [Formula: see text]. Using slow-fast analysis, we show that the original model possesses three equilibria lying at the intersection of the critical manifold of the fast subsystem and the nullcline of the slow variable [Formula: see text] (the inactivation of the A-type K[Formula: see text] channel), the middle equilibrium is of saddle type with two-dimensional stable manifold (computed from the reduced model) acting as a boundary between the responsive and non-responsive regimes, and the (ghost of) SNIC is formed when the [Formula: see text]-nullcline is (nearly) tangential to the critical manifold. We also show that the slow dynamics associated with (the ghost of) the SNIC and the lower stable branch of the critical manifold are responsible for generating the nonmonotonic first-spike latency. These results thus provide important insight into the complex dynamics of stellate cells.

Capsaicin-Induced Impairment of Functional Network Dynamics in Mouse Hippocampus via a TrpV1 Receptor-Independent Pathway: Putative Involvement of Na+/K+-ATPase

The vanilloid compound capsaicin (Cp) is best known to bind to and activate the transient receptor potential vanilloid receptor-1 (TrpV1). A growing number of studies use capsaicin as a tool to study the role of TrpV1 in the central nervous system (CNS). Although most of capsaicin’s CNS effects have been reported to be mediated by TrpV1 activation, evidence exists that capsaicin can also trigger functional changes in hippocampal activity independently of TrpV1. Recently, we have reported that capsaicin induces impairment in hippocampal gamma oscillations via a TrpV1-independent pathway. Here, we dissect the underlying mechanisms of capsaicin-induced alterations to functional network dynamics. We found that capsaicin induces a reduction in action potential (AP) firing rate and a subsequent loss of synchronicity in pyramidal cell (PC) spiking activity in hippocampus. Moreover, capsaicin induces alterations in PC spike-timing since increased first-spike latency was observed after capsaicin treatment. First-spike latency can be regulated by the voltage-dependent potassium current D (ID) or Na+/K+-ATPase. Selective inhibition of ID via low 4-AP concentration and Na+/K+-ATPase using its blocker ouabain, we found that capsaicin effects on AP spike timing were completely inhibited by ouabain but not with 4-AP. In conclusion, our study shows that capsaicin in a TrpV1-independent manner and possibly involving Na+/K+-ATPase activity can impair cognition-relevant functional network dynamics such as gamma oscillations and provides important data regarding the use of capsaicin as a tool to study TrpV1 function in the CNS.