Higher Order Spike Synchrony in Prefrontal Cortex during Visual Memory

Precise temporal synchrony of spike firing has been postulated as an important neuronal mechanism for signal integration and the induction of plasticity in neocortex. As prefrontal cortex plays an important role in organizing memory and executive functions, the convergence of multiple visual pathways onto PFC predicts that neurons should preferentially synchronize their spiking when stimulus information is processed. Furthermore, synchronous spike firing should intensify if memory processes require the induction of neuronal plasticity, even if this is only for short-term. Here we show with multiple simultaneously recorded units in ventral prefrontal cortex that neurons participate in 3 ms precise synchronous discharges distributed across multiple sites separated by at least 500 μm. The frequency of synchronous firing is modulated by behavioral performance and is specific for the memorized visual stimuli. In particular, during the memory period in which activity is not stimulus driven, larger groups of up to seven sites exhibit performance dependent modulation of their spike synchronization

Bivariate and Multivariate NeuroXidence: A Robust and Reliable Method to Detect Modulations of Spike–Spike Synchronization Across Experimental Conditions

Synchronous neuronal firing has been proposed as a potential neuronal code. To determine whether synchronous firing is really involved in different forms of information processing, one needs to directly compare the amount of synchronous firing due to various factors, such as different experimental or behavioral conditions. In order to address this issue, we present an extended version of the previously published method, NeuroXidence. The improved method incorporates bi- and multivariate testing to determine whether different factors result in synchronous firing occurring above the chance level. We demonstrate through the use of simulated data sets that bi- and multivariate NeuroXidence reliably and robustly detects joint-spike-events across different factors

Spike train auto-structure impacts post-synaptic firing and timing based plasticity

Cortical neurons are typically driven by several thousand synapses. The precise spatio-temporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impacts the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modelled by renewal gamma processes with varying shape factors for modelling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e. hard bound spike timing dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes is modulated by the temporal structure of the input. Finally we highlight that the sensitivity of both the post-synaptic firing as well as the spike timing dependent plasticity on the auto structure of the input of a neuron could be used to modulate the learning rate of synaptic modification

Missing mass approximations for the partition function of stimulus driven Ising models

Ising models are routinely used to quantify the second order, functional structure of neural populations. With some recent exceptions, they generally do not include the influence of time varying stimulus drive. Yet if the dynamics of network function are to be understood, time varying stimuli must be taken into account. Inclusion of stimulus drive carries a heavy computational burden because the partition function becomes stimulus dependent and must be separately calculated for all unique stimuli observed. This potentially increases computation time by the length of the data set. Here we present an extremely fast, yet simply implemented, method for approximating the stimulus dependent partition function in minutes or seconds. Noting that the most probable spike patterns (which are few) occur in the training data, we sum partition function terms corresponding to those patterns explicitly. We then approximate the sum over the remaining patterns (which are improbable, but many) by casting it in terms of the stimulus modulated missing mass (total stimulus dependent probability of all patterns not observed in the training data). We use a product of conditioned logistic regression models to approximate the stimulus modulated missing mass. This method has complexity of roughly O(LNN[subscript pat]) where is L the data length, N the number of neurons and N[subscript pat] the number of unique patterns in the data, contrasting with the O(L2[superscript N]) complexity of alternate methods. Using multiple unit recordings from rat hippocampus, macaque DLPFC and cat Area 18 we demonstrate our method requires orders of magnitude less computation time than Monte Carlo methods and can approximate the stimulus driven partition function more accurately than either Monte Carlo methods or deterministic approximations. This advance allows stimuli to be easily included in Ising models making them suitable for studying population based stimulus encoding.National Institutes of Health (U.S.) (Grant K25 NS052422-02

Application of Parallel Factor Analysis (PARAFAC) to Electrophysiological Data

The identification of important features in multi-electrode recordings requires the decomposition of data in order to disclose relevant features and to offer a clear graphical representation. This can be a demanding task. Parallel Factor Analysis (PARAFAC; Hitchcock, 1927; Carrol & Chang, 1970; Harshman, 1970) is a method to decompose multi-dimensional arrays in order to focus on the features of interest, and provides a distinct illustration of the results. We applied PARAFAC to analyse spatio-temporal patterns in the functional connectivity between neurons, as revealed in their spike trains recorded in cat primary visual cortex (area 18). During these recordings we reversibly deactivated feedback connections from higher visual areas in the pMS (posterior middle suprasylvian) cortex in order to study the impact of these top-down signals. Cross correlation was computed for every possible pair of the electrodes in the electrode array. PARAFAC was then used to reveal the effects of time, stimulus, and deactivation condition on the correlation patterns. Our results show that PARAFAC is able to reliably extract changes in correlation strength for different experimental conditions and display the relevant features. Thus, PARAFAC proves to be well-suited for the use in the context of electrophysiological (action potential) recordings