Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding

/pdf/information-theory-and-consciousness-3i95bbhuf2.pdf

Information Theory and Consciousness

ABSTRACT A key driver of mammalian intelligence is the ability to represent incoming sensory information across multiple abstraction levels. For example, in the visual ventral stream, incoming signals are first represented as low-level edge filters and then transformed into high-level object representations. These same hierarchical structures routinely emerge in artificial neural networks (ANNs) trained for image/object recognition tasks, suggesting that a similar process might underlie biological neural networks. However, the classical ANN training algorithm, backpropagation, is considered biologically implausible, and thus several alternative biologically plausible methods have been developed. For instance, several cortical-inspired ANNs in which the apical dendrite of a pyramidal neuron encodes top-down prediction signals have been proposed. In this case, akin to theories of predictive coding, a prediction error can be calculated locally inside each neuron for updating its incoming weights. Notwithstanding, from a neuroscience perspective, it is unclear whether neurons could compare their apical vs. somatic spiking activities to compute prediction errors. Here, we propose a solution to this problem by adapting the framework of the apical-somatic prediction error to the temporal domain. In particular, we show that if the apical feedback signal changes the postsynaptic firing rate, we can use differential Hebbian updates, a rate-based version of the classical spiking time-dependent plasticity (STDP) updates. To the best of our knowledge, this is the first time a cortical-like deep ANN has been trained using such time-based learning rules. Overall, our work removes a key requirement of biologically plausible models for deep learning that does not align with plasticity rules observed in biology and proposes a learning mechanism that would explain how the timing of neuronal activity can allow supervised hierarchical learning. 

/pdf/learning-cortical-hierarchies-with-temporal-hebbian-updates-1pohr2rk.pdf

Learning Cortical Hierarchies with Temporal Hebbian Updates

Biological structures are hierarchical and non-homogeneous objects. In particular, in each type of the biological cell various organelles exist. Also, in the cell many nonlinear processes, like the synaptic transmission, take place. The mesh, created for the purpose of numerical simulation, should reflect accurately their nature that is usually complicated. This, in connection with the fact that the mesh should fit precisely the aforementioned structures, create substantially new problems in comparison with well worked-out engineering applications. In this paper the problem of refining the mesh of a presynaptic bouton is considered. The refinement was tested by performing simulations of nonlinear diffusion-reaction type of propagation of neurotransmitters. The results for the original and for the refined mesh were compared. The latter reflected the nature of the biological object more precisely. The benefits and the disadvantages of the proposed refinement algorithm are discussed in detail. 

Simulation of processes and structures in the synapse in the context of tetrahedral mesh quality

Abstract Anticipating future events is a key computational task for neuronal networks. Experimental evidence suggests that reliable temporal sequences in neural activity play a functional role in the association and anticipation of events in time. However, how neurons can differentiate and anticipate multiple spike sequences remains largely unknown. We implement a learning rule based on predictive processing, where neurons exclusively fire for the initial, unpredictable inputs in a spiking sequence, leading to an efficient representation with reduced post-synaptic firing. Combining this mechanism with inhibitory feedback leads to sparse firing in the network, enabling neurons to selectively anticipate different sequences in the input. We demonstrate that intermediate levels of inhibition are optimal to decorrelate neuronal activity and to enable the prediction of future inputs. Notably, each sequence is independently encoded in the sparse, anticipatory firing of the network. Overall, our results demonstrate that the interplay of self-supervised predictive learning rules and inhibitory feedback enables fast and efficient classification of different input sequences. 

Inhibitory feedback enables predictive learning of multiple sequences in neural networks

Latency reduction in postsynaptic spikes is a well-known effect of spiking time-dependent plasticity. We expand this notion for long postsynaptic spike trains on single neurons, showing that, for a fixed input spike train, STDP reduces the number of postsynaptic spikes and concentrates the remaining ones. Then, we study the consequences of this phenomena in terms of coding, finding that this mechanism improves the neural code by increasing the signal-to-noise ratio and lowering the metabolic costs of frequent stimuli. Finally, we illustrate that the reduction in postsynaptic latencies can lead to the emergence of predictions.

/pdf/spiking-time-dependent-plasticity-leads-to-efficient-coding-42rwhouthg.pdf

Spiking time-dependent plasticity leads to efficient coding of predictions

Latency reduction of postsynaptic spikes is a well-known effect of Synaptic Time-Dependent Plasticity. We expand this notion for long postsynaptic spike trains, showing that, for a fixed input spike train, STDP reduces the number of postsynaptic spikes and concentrates the remaining ones. Then we study the consequences of this phenomena in terms of coding, finding that this mechanism improves the neural code by increasing the signal-to-noise ratio and lowering the metabolic costs of frequent stimuli. Finally, we illustrate that the reduction of postsynaptic latencies can lead to the emergence of predictions.

Synaptic Time-Dependent Plasticity Leads to Efficient Coding of Predictions.

Dynamics of an interconnected population of excitatory and inhibitory spiking neurons wandering around a Bogdanov-Takens (BT) bifurcation point can generate the observed scale-free avalanches at the population level and the highly variable spike patterns of individual neurons. These characteristics match experimental findings for spontaneous intrinsic activity in the brain. In this paper, we address the mechanisms causing the system to get and remain near this BT point. We propose an effective stochastic neural field model which captures the dynamics of the mean-field model. We show how the network tunes itself through local long-term synaptic plasticity by STDP and short-term synaptic depression to be close to this bifurcation point. The mesoscopic model that we derive matches the directed percolation model at the absorbing state phase transition.

/pdf/self-organized-criticality-in-a-mesoscopic-model-of-21kkb7su.pdf

Self-organized criticality in a mesoscopic model of excitatory-inhibitory neuronal populations by short-term and long-term synaptic plasticity

https://link.springer.com/content/pdf/10.1007/s10827-022-00838-4.pdf

Scale free avalanches in excitatory-inhibitory populations of spiking neurons with conductance based synaptic currents

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Papers

Spiking time-dependent plasticity leads to efficient coding of predictions

Synaptic Time-Dependent Plasticity Leads to Efficient Coding of Predictions.

Self-organized criticality in a mesoscopic model of excitatory-inhibitory neuronal populations by short-term and long-term synaptic plasticity

Scale free avalanches in excitatory-inhibitory populations of spiking neurons with conductance based synaptic currents