Autapses enable temporal pattern recognition in spiking neural networks

© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Most sensory stimuli are temporal in structure. How action potentials encode the information incoming from sensory stimuli remains one of the central research questions in neuroscience. Although there is evidence that the precise timing of spikes represents information in spiking neuronal networks, information processing in spiking networks is still not fully understood. One feasible way to understand the working mechanism of a spiking network is to associate the structural connectivity of the network with the corresponding functional behaviour. This work demonstrates the structure-function mapping of spiking networks evolved (or handcrafted) for a temporal pattern recognition task. The task is to recognise a specific order of the input signals so that the Output neurone of the network spikes only for the correct placement and remains silent for all others. The minimal networks obtained for this task revealed the twofold importance of autapses in recognition; first, autapses simplify the switching among different network states. Second, autapses enable a network to maintain a network state, a form of memory. To show that the recognition task is accomplished by transitions between network states, we map the network states of a functional spiking neural network (SNN) onto the states of a finite-state transducer (FST, a formal model of computation that generates output symbols, here: spikes or no spikes at specific times, in response to input, here: a series of input signals). Finally, based on our understanding, we define rules for constructing the topology of a network handcrafted for recognising a subsequence of signals (pattern) in a particular order. The analysis of minimal networks recognising patterns of different lengths (two to six) revealed a positive correlation between the pattern length and the number of autaptic connections in the network. Furthermore, in agreement with the behaviour of neurones in the network, we were able to associate specific functional roles of locking, switching, and accepting to neurones

Conversations on Empathy

In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice

Conversations on Empathy

In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice

The Evolution, Analysis, and Design of Minimal Spiking Neural Networks for Temporal Pattern Recognition

All sensory stimuli are temporal in structure. How a pattern of action potentials encodes the information received from the sensory stimuli is an important research question in neurosciencce. Although it is clear that information is carried by the number or the timing of spikes, the information processing in the nervous system is poorly understood. The desire to understand information processing in the animal brain led to the development of spiking neural networks (SNNs). Understanding information processing in spiking neural networks may give us an insight into the information processing in the animal brain. One way to understand the mechanisms which enable SNNs to perform a computational task is to associate the structural connectivity of the network with the corresponding functional behaviour. This work demonstrates the structure-function mapping of spiking networks evolved (or handcrafted) for recognising temporal patterns. The SNNs are composed of simple yet biologically meaningful adaptive exponential integrate-and-fire (AdEx) neurons. The computational task can be described as identifying a subsequence of three signals (say ABC) in a random input stream of signals ("ABBBCCBABABCBBCAC"). The topology and connection weights of the networks are optimised using a genetic algorithm such that the network output spikes only for the correct input pattern and remains silent for all others. The fitness function rewards the network output for spiking after receiving the correct pattern and penalises spikes elsewhere. To analyse the effect of noise, two types of noise are introduced during evolution: (i) random fluctuations of the membrane potential of neurons in the network at every network step, (ii) random variations of the duration of the silent interval between input signals. It has been observed that evolution in the presence of noise produced networks that were robust to perturbation of neuronal parameters. Moreover, the networks also developed a form of memory, enabling them to maintain network states in the absence of input activity. It has been demonstrated that the network states of an evolved network have a one-to-one correspondence with the states of a finite-state transducer (FST) { a model of computation for time-structured data. The analysis of networks indicated that the task of recognition is accomplished by transitions between network states. Evolution may overproduce synaptic connections, pruning these superfluous connections pronounced structural similarities among individuals obtained from different independent runs. Moreover, the analysis of the pruned networks highlighted that memory is a property of self-excitation in the network. Neurons with self-excitatory loops (also called autapses) could sustain spiking activity indefinitely in the absence of input activity. To recognise a pattern of length n, a network requires n+1 network states, where n states are maintained actively with autapses and the penultimate state is maintained passively by no activity in the network. Simultaneously, the role of other connections in the network is identified. Of particular interest, three interneurons in the network are found to have a specialized role: (i) the lock neuron is always active, preventing the output from spiking unless it is released by the penultimate signal in the correct pattern, exposing the output neuron to spike for the correct last signal, (ii) the switch neuron is responsible for switching the network between the inter-signal states and the start state, and (iii) the accept neuron produces spikes in the output neuron when the network receives the last correct input. It also sends a signal to the switch neuron, transforming the network back into the start state Understanding how information is processed in the evolved networks led to handcrafting network topologies for recognising more extended patterns. The proposed rules can extend network topologies to recognize temporal patterns up to length six. To validate the handcrafted topology, a genetic algorithm is used to optimise its connection weights. It has been observed that the maximum number of active neurons representing a state in the network increases with the pattern length. Therefore, the suggested rules can handcraft network topologies only up to length 6. Handcrafting network topologies, representing a network state with a fixed number of active neurons requires further investigation

Pattern Activation/Recognition Theory of Mind

In his 2012 book How to Create a Mind, Ray Kurzweil defines a Pattern Recognition Theory of Mind that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call Pattern Activation/Recognition Theory of Mind. While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation