Developing a spiking neural model of Long Short-Term Memory architectures

Current advances in Deep Learning have shown significant improvements in common Machine Learning applications such as image, speech and text recognition. Specifically, in order to process time series, deep Neural Networks (NNs) with Long Short-Term Memory (LSTM) units are widely used in sequence recognition problems to store recent information and to use it for future predictions. The efficiency in data analysis, especially when big-sized data sets are involved, can be greatly improved thanks to the advancement of the ongoing research on Neural Networks (NNs) and Machine Learning for many applications in Physics. However, whenever acquisition and processing of data at different time resolutions is required, a synchronization problem for which the same piece of information is processed multiple times arises, and the advantageous efficiency of NNs, which lack the natural notion of time, ceases to exist. Spiking Neural Networks (SNNs) are the next generation of NNs that allow efficient information coding and processing by means of spikes, i.e. binary pulses propagating between neurons. In this way, information can be encoded in time, and the communication of information is activated only when the input to the neurons change, thus giving higher efficiency. In the present work, analog neurons are used for training and then they are substituted with spiking neurons in order to perform tasks. The aim for this project is to find a transfer function which allows a simple and accurate switching between analog and spiking neurons, and then to prove that the obtained network performs well in different tasks. At first, an analytical transfer function for more biologically plausible values for some neuronal parameters is derived and tested. Subsequently, the stochastic nature of the biological neurons is implemented in the neuronal model used. A new transfer function is then approximated by studying the stochastic behavior of artificial neurons, allowing to implement a simplified description for the gates and the input cell in the LSTM units. The stochastic LSTM networks are then tested on Sequence Prediction and T-Maze, i.e. typical memory-involving Machine Learning tasks, showing that almost all the resulting spiking networks correctly compute the original tasks. The main conclusion drawn from this project is that by means of a neuronal model comprising of a stochastic description of the neuron it is possible to obtain an accurate mapping from analog to spiking memory networks, which gives good results on Machine Learning tasks.Spiking neurons communicate with each other by means of a telegraph-like mechanism: a message is encoded by a neuron in binary events–called spikes–that are sent to another neuron, which decodes the incoming spikes of signal by means of the same coding originally used by the sending neuron. The problem addressed in this project then was: is it possible to make a group of such neurons remember things in the short-term but for long-enough time that they are able to solve tasks that require memory? Imagine you are driving to work through a road you have never taken before, and your task is to turn right at the next traffic light. The memory-tasks we wanted the neural networks to learn and solve are of this sort, and no spiking networks exist that can do this. With regards to this goal, the approach we opted for was to train a network of standard artificial neurons and then, once the network learned how to perform the task, we would switch the standard neurons with our modeled spiking neurons. In order to do this, of course, there are some constraints, in particular the two types of neurons (standard and spiking) have to encode signals in the same way, meaning that they need to have the same coding policy. In this project, I had to find an adequate coding policy for the spiking neurons, in order to give the same policy to a network of standard neurons and to test this superposition. Turned out, after the standard networks had learned the tasks and then switched with spiking units, the spiking neurons were indeed able to remember short-term information (such as looking for a traffic light before turning right) and to perform well in such memory tasks, allowing useful computation over time. One of the scientific fields in need of improvement is, in fact, signal processing over time. Nowadays most of the detection instruments collect signal during a time window, meaning that the signal collected in a small time range is considered as a whole, instead of detecting in continuous time. In the first case, a buffer of the history of the time windows (the information gathered before meeting the traffic light) is stored, while when information is processed in continuous time only relevant information (the time at which the traffic light is encountered) is needed. Being able to classify signals as soon as they are detected is a characteristic of asynchronous detection, an example of which is our sight, or hearing. The brain, in fact, is one of the most efficient and powerful systems existent. So why not studying a computation method inspired by the brain? Spiking neurons are exactly that: artificial units performing brain-like computation. Hence, these neurons potentially offer efficient computation and an advantageous method for continuous-time signal processing, which hopefully will be implemented in many research fields in the future

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

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

Short-term plasticity as cause-effect hypothesis testing in distal reward learning

Asynchrony, overlaps and delays in sensory-motor signals introduce ambiguity as to which stimuli, actions, and rewards are causally related. Only the repetition of reward episodes helps distinguish true cause-effect relationships from coincidental occurrences. In the model proposed here, a novel plasticity rule employs short and long-term changes to evaluate hypotheses on cause-effect relationships. Transient weights represent hypotheses that are consolidated in long-term memory only when they consistently predict or cause future rewards. The main objective of the model is to preserve existing network topologies when learning with ambiguous information flows. Learning is also improved by biasing the exploration of the stimulus-response space towards actions that in the past occurred before rewards. The model indicates under which conditions beliefs can be consolidated in long-term memory, it suggests a solution to the plasticity-stability dilemma, and proposes an interpretation of the role of short-term plasticity.Comment: Biological Cybernetics, September 201