Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify, arbitrarily, neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.Comment: In submission to Frontiers in Neuromorphic Engineerin

Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks

We study the collective dynamics of a Leaky Integrate and Fire network in which precise relative phase relationship of spikes among neurons are stored, as attractors of the dynamics, and selectively replayed at differentctime scales. Using an STDP-based learning process, we store in the connectivity several phase-coded spike patterns, and we find that, depending on the excitability of the network, different working regimes are possible, with transient or persistent replay activity induced by a brief signal. We introduce an order parameter to evaluate the similarity between stored and recalled phase-coded pattern, and measure the storage capacity. Modulation of spiking thresholds during replay changes the frequency of the collective oscillation or the number of spikes per cycle, keeping preserved the phases relationship. This allows a coding scheme in which phase, rate and frequency are dissociable. Robustness with respect to noise and heterogeneity of neurons parameters is studied, showing that, since dynamics is a retrieval process, neurons preserve stablecprecise phase relationship among units, keeping a unique frequency of oscillation, even in noisy conditions and with heterogeneity of internal parameters of the units

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

Dynamic Control of Network Level Information Processing through Cholinergic Modulation

Acetylcholine (ACh) release is a prominent neurochemical marker of arousal state within the brain. Changes in ACh are associated with changes in neural activity and information processing, though its exact role and the mechanisms through which it acts are unknown. Here I show that the dynamic changes in ACh levels that are associated with arousal state control informational processing functions of networks through its effects on the degree of Spike-Frequency Adaptation (SFA), an activity dependent decrease in excitability, synchronizability, and neuronal resonance displayed by single cells. Using numerical modeling I develop mechanistic explanations for how control of these properties shift network activity from a stable high frequency spiking pattern to a traveling wave of activity. This transition mimics the change in brain dynamics seen between high ACh states, such as waking and Rapid Eye Movement (REM) sleep, and low ACh states such as Non-REM (NREM) sleep. A corresponding, and related, transition in network level memory recall is also occurs as ACh modulates neuronal SFA. When ACh is at its highest levels (waking) all memories are stably recalled, as ACh is decreased (REM) in the model weakly encoded memories destabilize while strong memories remain stable. In levels of ACh that match Slow Wave Sleep (SWS), no encoded memories are stably recalled. This results from a competition between SFA and excitatory input strength and provides a mechanism for neural networks to control the representation of underlying synaptic information. Finally I show that during the low ACh conditions, oscillatory conditions allow for external inputs to be properly stored in and recalled from synaptic weights. Taken together this work demonstrates that dynamic neuromodulation is critical for the regulation of information processing tasks in neural networks. These results suggest that ACh is capable of switching networks between two distinct information processing modes. Rate coding of information is facilitated during high ACh conditions and phase coding of information is facilitated during low ACh conditions. Finally I propose that ACh levels control whether a network is in one of three functional states: (High ACh; Active waking) optimized for encoding of new information or the stable representation of relevant memories, (Mid ACh; resting state or REM) optimized for encoding connections between currently stored memories or searching the catalog of stored memories, and (Low ACh; NREM) optimized for renormalization of synaptic strength and memory consolidation. This work provides a mechanistic insight into the role of dynamic changes in ACh levels for the encoding, consolidation, and maintenance of memories within the brain.PHDNeuroscienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147503/1/roachjp_1.pd

Recent Advances in Metasurface Design and Quantum Optics Applications with Machine Learning, Physics-Informed Neural Networks, and Topology Optimization Methods

As a two-dimensional planar material with low depth profile, a metasurface can generate non-classical phase distributions for the transmitted and reflected electromagnetic waves at its interface. Thus, it offers more flexibility to control the wave front. A traditional metasurface design process mainly adopts the forward prediction algorithm, such as Finite Difference Time Domain, combined with manual parameter optimization. However, such methods are time-consuming, and it is difficult to keep the practical meta-atom spectrum being consistent with the ideal one. In addition, since the periodic boundary condition is used in the meta-atom design process, while the aperiodic condition is used in the array simulation, the coupling between neighboring meta-atoms leads to inevitable inaccuracy. In this review, representative intelligent methods for metasurface design are introduced and discussed, including machine learning, physics-information neural network, and topology optimization method. We elaborate on the principle of each approach, analyze their advantages and limitations, and discuss their potential applications. We also summarise recent advances in enabled metasurfaces for quantum optics applications. In short, this paper highlights a promising direction for intelligent metasurface designs and applications for future quantum optics research and serves as an up-to-date reference for researchers in the metasurface and metamaterial fields