Coexistence of reward and unsupervised learning during the operant conditioning of neural firing rates

Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters

Studies of Boolean recurrent neural networks are briefly introduced with an emphasis on the attractor dynamics determined by the sequence of distinct attractors observed in the limit cycles. We apply this framework to a simplified model of the basal ganglia-thalamocortical circuit where each brain area is represented by a "neuronal" node in a directed graph. Control parameters ranging from neuronal excitability that affects all cells to targeted local connections modified by a new adaptive plasticity rule, and the regulation of the interactive feedback affecting the external input stream of information, allow the network dynamics to switch between stable domains delimited by highly discontinuous boundaries and reach very high levels of complexity with specific configurations. The significance of this approach with regard to brain circuit studies is briefly discussed.

Linking Neuromodulated Spike-Timing Dependent Plasticity with the Free-Energy Principle

The free-energy principle is a candidate unified theory for learning and memory in the brain that predicts that neurons, synapses, and neuromodulators work in a manner that minimizes free energy. However, electrophysiological data elucidating the neural and synaptic bases for this theory are lacking. Here, we propose a novel theory bridging the information-theoretical principle with the biological phenomenon of spike-timing dependent plasticity (STDP) regulated by neuromodulators, which we term mSTDP. We propose that by integrating an mSTDP equation, we can obtain a form of Friston's free energy (an information-theoretical function). Then we analytically and numerically show that dopamine (DA) and noradrenaline (NA) influence the accuracy of a principal component analysis (PCA) performed using the mSTDP algorithm. From the perspective of free-energy minimization, these neuromodulatory changes alter the relative weighting or precision of accuracy and prior terms, which induces a switch from pattern completion to separation. These results are consistent with electrophysiological findings and validate the free-energy principle and mSTDP. Moreover, our scheme can potentially be applied in computational psychiatry to build models of the faulty neural networks that underlie the positive symptoms of schizophrenia, which involve abnormal DA levels, as well as models of the NA contribution to memory triage and posttraumatic stress disorder.

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons’ action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

Pitch is the perceptual correlate of sound frequency. Our auditory system has a sophisticated mechanism to process and perceive the neural information corresponding to pitch. This mechanism employs both the place and the temporal pattern of pitch-evoked neural events. Based on the known functions of the auditory system, we develop a computational model of pitch perception using a network of neurons with modifiable connections. We demonstrate that a well-known neural learning rule that is based on the timing of the neural events can identify and strengthen the neuronal connections that are most effective for the extraction of pitch. By providing an insight into how the auditory system interprets pitch information, the results of our study can be used to develop improved sound processing strategies for cochlear implants. In cochlear implant hearing, auditory percept is generated by stimulating the auditory neurons with controlled electrical impulses, enhancing which with the help of the model would lead to a better representation of pitch and would subsequently improve music perception and speech understanding in noisy environments in cochlear implant users.