Presynaptic modulation as fast synaptic switching: state-dependent modulation of task performance

Neuromodulatory receptors in presynaptic position have the ability to suppress synaptic transmission for seconds to minutes when fully engaged. This effectively alters the synaptic strength of a connection. Much work on neuromodulation has rested on the assumption that these effects are uniform at every neuron. However, there is considerable evidence to suggest that presynaptic regulation may be in effect synapse-specific. This would define a second "weight modulation" matrix, which reflects presynaptic receptor efficacy at a given site. Here we explore functional consequences of this hypothesis. By analyzing and comparing the weight matrices of networks trained on different aspects of a task, we identify the potential for a low complexity "modulation matrix", which allows to switch between differently trained subtasks while retaining general performance characteristics for the task. This means that a given network can adapt itself to different task demands by regulating its release of neuromodulators. Specifically, we suggest that (a) a network can provide optimized responses for related classification tasks without the need to train entirely separate networks and (b) a network can blend a "memory mode" which aims at reproducing memorized patterns and a "novelty mode" which aims to facilitate classification of new patterns. We relate this work to the known effects of neuromodulators on brain-state dependent processing.Comment: 6 pages, 13 figure

The Effects of Dopamine on Frequency Dependent Short Term Synaptic Plasticity: A Comparison of Layer Contributions and Rhythmic Dynamics

Executive functions (e.g. working memory [WM]) are known to be mediated by prefrontal cortical areas of the human brain which share homology with mouse medial prefrontal cortex (mPFC). Furthermore, it is well established that optimal dopaminergic input is required for proper WM function in the PFC. While it is well established that the mPFC receives inputs from several different brain areas, impinging on different compartmental regions of cells, it remains unknown how layer V pyramidal cells, the major output cells of the mPFC, integrate this information. Additionally, it remains unknown how dopamine modulates this integration by way of separate afferents and compartments within the PFC. A subset of studies presented here focus attention on the excitatory synaptic responses of layer V cells in response to compartmentalized stimulation (i.e. within the somatic region [layer V] or within the apical tufts [layer I]). Overall, these data suggest that dopamine, through D1 receptor (R) activation promotes local connectivity (primarily layer V to layer V connections) in the somatic region, while simultaneously inhibiting synaptic plasticity within the apical tufts through the suppression of NMDAR-mediated responses. Additionally, D2R activation had no effect on local layer V connectivity, but may play a role in regulating the signal-to-noise ratio in the apical tufts, by inhibiting low-frequency inputs and promoting inputs firing at high frequencies. Taken together, these results suggest that in the presence of normal dopamine levels local influences (i.e. environmental / bottom-up ) and plasticity will be promoted within layer V, while top-down\u22 or contextual information impinging on layer I is stabilized. Additional studies presented here focus attention on the excitatory synaptic responses, and modulation of dopamine, of layer V pyramids in response to inputs from the contralateral mPFC. These data suggest that D1R modulation enhances the ability of layer V cells to integrate information from the contralateral mPFC. In combination, these experiments provide insight into how normal dopaminergic receptor activation alters signal processing and integration properties of layer V cells within the mPFC and shed light on cellular mechanism disruptions in schizophrenia, a disorder characterized by dopaminergic dysregulation

Extracting Cancer pagurus stomatogastric ganglion pyloric rhythm frequency via voltage-sensitive dye imaging data using signal processing techniques

Voltage-sensitive dye imaging (VSDI) has been widely used in the past few decades in both vertebrates and invertebrates to study, in vitro and in vivo, the nervous systems. Cancer pagurus is a seawater crab whose nervous system has a ganglion, the stomatogastric ganglion (STG) that contains a relatively small number of neurons and two rhythm forming central pattern generators (CPGs). The pyloric rhythm is one such spontaneous rhythm that can be readily observed in vitro, which makes the STG an ideal ganglion to study using VSDI. However, a major impediment to the effectiveness of VSDI is that the optically recorded data is often noisy with poor signal-to-noise ratios (SNR), rendering it difficult to study and analyse. This thesis describes the first-ever development of computational signal processing procedures that sought to extract the pyloric rhythm directly from the VSDI data, thus facilitating an accurate identification of the individual neurons in the pyloric circuit. Specifically, a multiresolution procedure based on the sequential Singular Spectrum Analysis (s-SSA) was first constructed to separate the pyloric rhythm from the noisy VSDI recording, enabling potential pyloric neurons to be detected by the presence of the pyloric frequency in the computed spectra of the respective cells. To facilitate identifying the pyloric neurons, the duty cycle (DC) was devised as a biometric, and the corresponding ratio of harmonics (RH) was determined in terms of the harmonic content of the spectrum computed for each cell/neuron as described above. Here, the instantaneous phase of the detected pyloric rhythm was also estimated, allowing it to be compared and aligned with the three distinctive pyloric phases (PD-, LP- and PY-timed) readily measured on the lateral ventricular nerve (lvn). As proof of concepts, finally, an automated method to determine the pyloric frequency directly from VSDI data was developed, over a range of SNRs, demonstrating the possibility to identify prospective pyloric neurons based on the estimated DCs and respective phase shifts measured against the analogue lvn recording

Extracting Cancer pagurus stomatogastric ganglion pyloric rhythm frequency via voltage-sensitive dye imaging data using signal processing techniques

Voltage-sensitive dye imaging (VSDI) has been widely used in the past few decades in both vertebrates and invertebrates to study, in vitro and in vivo, the nervous systems. Cancer pagurus is a seawater crab whose nervous system has a ganglion, the stomatogastric ganglion (STG) that contains a relatively small number of neurons and two rhythm forming central pattern generators (CPGs). The pyloric rhythm is one such spontaneous rhythm that can be readily observed in vitro, which makes the STG an ideal ganglion to study using VSDI. However, a major impediment to the effectiveness of VSDI is that the optically recorded data is often noisy with poor signal-to-noise ratios (SNR), rendering it difficult to study and analyse.This thesis describes the first-ever development of computational signal processing procedures that sought to extract the pyloric rhythm directly from the VSDI data, thus facilitating an accurate identification of the individual neurons in the pyloric circuit. Specifically, a multiresolution procedure based on the sequential Singular Spectrum Analysis (s-SSA) was first constructed to separate the pyloric rhythm from the noisy VSDI recording, enabling potential pyloric neurons to be detected by the presence of the pyloric frequency in the computed spectra of the respective cells. To facilitate identifying the pyloric neurons, the duty cycle (DC) was devised as a biometric, and the corresponding ratio of harmonics (RH) was determined in terms of the harmonic content of the spectrum computed for each cell/neuron as described above. Here, the instantaneous phase of the detected pyloric rhythm was also estimated, allowing it to be compared and aligned with the three distinctive pyloric phases (PD-, LP- and PY-timed) readily measured on the lateral ventricular nerve (lvn). As proof of concepts, finally, an automated method to determine the pyloric frequency directly from VSDI data was developed, over a range of SNRs, demonstrating the possibility to identify prospective pyloric neurons based on the estimated DCs and respective phase shifts measured against the analogue lvn recording