Self-Organizing Recurrent Neural Network (SORN)¶
Self-Organizing Recurrent Neural (SORN) networks are a class of reservoir computing models build based on plasticity mechanisms in biological brain. Recent studies on SORN shows that such models can mimic neocortical circuit’s ability of learning and adaptation through neuroplasticity mechanisms. Structurally, unlike other liquid state models, SORN consists of pool of excitatory neurons and small population of inhibitory neurons. First such network was introduced with three fundamental plasticity mechanisms found in neocortex, namely Spike timing dependent plasticity (STDP), intrinsic plasticity (IP) and Synaptic scaling (SS). Spike Timing-Dependent Plasticity or Hebbian Learning with positive feedback (rapid cycle of synaptic potentials) selectively strengthens correlated synapses and weaken the uncorrelated. Such activity dependent rules lead to Long Time Potentiation (LTP) and Long Time Depression (LTD).
Biologically, both LTP and LDP are assumed to possess substrates of learning and memory at the cellular level of neocortex. However, in dynamical systems, such phenomena will drive the network either towards the state of bursting activity in case of LTP or towards state of attenuation due to LTD. These destabilizing influences of STDP are counteracted by homeostatic plasticity mechanisms. Homeostatic mechanisms are a set of negative feedback (action potential suppressing) regulatory mechanisms that scales incoming synaptic strengths and balances neuronal activity through synaptic normalization and intrinsic plasticity. Experimental evidences also prove that synaptic scaling found to balance the activity between excitatory and inhibitory neurons in-vivo. Together, they maintain the overall activity of network within subcritical range, despite the network being driven by positive feedback from fast Hebbian plasticity.
In recent proposed models, SORN is extended with two more plasticity mechanisms, inhibitory spike timing dependent plasticity and structural plasticity. While connections between excitatory neurons (E-E) subjected to STDP rules, connections from inhibitory population to excitatory populations(E-I) are regulated by iSTDP. Structural plasticity, generates new connections constantly at a smaller rate between unconnected synapses. Many studies argued that, such structural changes induce neuronal morphogenesis which leads to network re-organization with functional consequences over learning and memory. The mathematical descriptions of plasticity mechanisms proposed in SORN simplifies the structural and functional connectivity mechanisms that resembles information processing, learning and memory phenomena that occur in neuro-synapses of neocortex region. Recent experimental evidences confirm that SORN outperforms other static reservoir networks in spatio-temporal tasks and maintains the dynamics of the network in subcritical state suitable for learning. Further research on such network mechanisms unravels the underlying features of synaptic connections and network activity in real cortical circuits. Hence investigating the characteristics of SORN and extending its structural and functional attributes by replicating the recent findings in neural connectomics may reveal the dominating principles of self-organization and self-adaptation in neocortical circuits at microscopic level. Moreover, characterizing these mechanisms individually at that level may also help us to understand some fundamental aspects of brain networks at mesoscopic and macroscopic scales.