Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo

How information in the nervous system is encoded by patterns of action potentials (i.e. spikes) remains an open question. Multi-neuron patterns of single spikes are a prime candidate for spike time encoding but their temporal variability requires further characterisation. Here we show how known sources of spike count variability affect stimulus-evoked spike time patterns between neurons separated over multiple layers and columns of adult rat somatosensory cortex in vivo. On subsets of trials (clusters) and after controlling for stimulus-response adaptation, spike time differences between pairs of neurons are “time-warped” (compressed/stretched) by trial-to-trial changes in shared excitability, explaining why fixed spike time patterns and noise correlations are seldom reported. We show that predicted cortical state is correlated between groups of 4 neurons, introducing the possibility of spike time pattern modulation by population-wide trial-to-trial changes in excitability (i.e. cortical state). Under the assumption of state-dependent coding, we propose an improved potential encoding capacity.

Balanced networks under spike-time dependent plasticity

The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory–inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike–timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity–induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input.

Spike-time dependent plasticity rule in memristor models for circuits design

Abstract Spike Time-Dependent Plasticity (STDP) represents an essential learning rule found in biological synapses which is recommended for replication in neuromorphic electronic systems. This rule is defined as a process of updating synaptic weight that depends on the time difference between the pre- and post-synaptic spikes. It is well known that pre-synaptic activity preceding post-synaptic activity may induce long term potentiation (LTP) whereas the reverse case induces long term depression (LTD). Memristors, which are two-terminal memory devices, are excellent candidates to implement such a mechanism due to their distinctive characteristics. In this article, we analyze the fundamental characteristics of three of the most known memristor models, and then we simulate it in order to mimic the plasticity rule of biological synapses. The tested models are the linear ion drift model (HP), the Voltage ThrEshold Adaptive Memristor (VTEAM) model and the Enhanced Generalized Memristor (EGM) model. We compare the I-V characteristics of these models with an experimental memristive device based on Ta2O5. We simulate and validate the STDP Hebbian learning algorithm proving the capability of each model to reproduce the conductance change for the LTP and LTD functions. Thus, our simulation results explore the most suitable model to operate as a synapse component for neuromorphic circuits.

CRBA: A Competitive Rate-Based Algorithm Based on Competitive Spiking Neural Networks

In this paper we present a Competitive Rate-Based Algorithm (CRBA) that approximates operation of a Competitive Spiking Neural Network (CSNN). CRBA is based on modeling of the competition between neurons during a sample presentation, which can be reduced to ranking of the neurons based on a dot product operation and the use of a discrete Expectation Maximization algorithm; the latter is equivalent to the spike time-dependent plasticity rule. CRBA's performance is compared with that of CSNN on the MNIST and Fashion-MNIST datasets. The results show that CRBA performs on par with CSNN, while using three orders of magnitude less computational time. Importantly, we show that the weights and firing thresholds learned by CRBA can be used to initialize CSNN's parameters that results in its much more efficient operation.

Dopamine and serotonin interplay for valence-based spatial learning

Dopamine and serotonin are important modulators of synaptic plasticity and their action has been linked to our ability to learn the positive or negative outcomes or valence learning. In the hippocampus, both neuromodulators affect long-term synaptic plasticity but play different roles in the encoding of uncertainty or predicted reward. Here, we examine the differential role of these modulators on learning speed and cognitive flexibility in a navigational model. We compare two reward-modulated spike time-dependent plasticity (R-STDP) learning rules to describe the action of these neuromodulators. Our results show that the interplay of dopamine (DA) and serotonin (5-HT) improves overall learning performance and can explain experimentally reported differences in spatial task performance. Furthermore, this system allows us to make predictions regarding spatial reversal learning.

Improved hyperacuity estimation of spike timing from calcium imaging

Two-photon imaging is a major recording technique used in neuroscience. However, it suffers from several limitations, including a low sampling rate, the nonlinearity of calcium responses, the slow dynamics of calcium dyes and a low SNR, all of which severely limit the potential of two-photon imaging to elucidate neuronal dynamics with high temporal resolution. We developed a hyperacuity algorithm (HA_time) based on an approach that combines a generative model and machine learning to improve spike detection and the precision of spike time inference. Bayesian inference was performed to estimate the calcium spike model, assuming constant spike shape and size. A support vector machine using this information and a jittering method maximizing the likelihood of estimated spike times enhanced spike time estimation precision approximately fourfold (range, 2–7; mean, 3.5–4.0; 2SEM, 0.1–0.25) compared to the sampling interval. Benchmark scores of HA_time for biological data from three different brain regions were among the best of the benchmark algorithms. Simulation of broader data conditions indicated that our algorithm performed better than others with high firing rate conditions. Furthermore, HA_time exhibited comparable performance for conditions with and without ground truths. Thus HA_time is a useful tool for spike reconstruction from two-photon imaging.

Simultaneous spike-time locking to multiple frequencies

Locking of neuronal spikes to external and internal signals is a ubiquitous neurophysiological mechanism that has been extensively studied in several brain areas and species. Using experimental data from the electrosensory system and concise mathematical models, we analyze how a single neuron can simultaneously lock to multiple frequencies. Our findings demonstrate how temporal and rate codes can complement each other and lead to rich neuronal representations of sensory signals.