Stimulus-Dependent Assembly Formation of Oscillatory Responses: III. Learning

1992 ◽  
Vol 4 (5) ◽  
pp. 666-681 ◽  
Author(s):  
Peter König ◽  
Bernd Janosch ◽  
Thomas B. Schillen
Keyword(s):  
Visual Cortex ◽  
Temporal Structure ◽  
Learning Rule ◽  
Oscillatory Activity ◽  
Connectivity Pattern ◽  
Local Learning ◽  
Weight Changes ◽  
Cell Assemblies ◽  
Learning Rules ◽  
Neuronal Oscillators

A temporal structure of neuronal activity has been suggested as a potential mechanism for defining cell assemblies in the brain. This concept has recently gained support by the observation of stimulus-dependent oscillatory activity in the visual cortex of the cat. Furthermore, experimental evidence has been found showing the formation and segregation of synchronously oscillating cell assemblies in response to various stimulus conditions. In previous work, we have demonstrated that a network of neuronal oscillators coupled by synchronizing and desynchronizing delay connections can exhibit a temporal structure of responses, which closely resembles experimental observations. In this paper, we investigate the self-organization of synchronizing and desynchronizing coupling connections by local learning rules. Based on recent experimental observations, we modify synchronizing connections according to a two-threshold learning rule, involving synaptic potentiation and depression. This rule is generalized to its functional inverse for weight changes of desynchronizing connections. We show that after training, the resulting network exhibits stimulus-dependent formation and segregation of oscillatory assemblies in agreement with the experimental data. These results indicate that local learning rules during ontogenesis can suffice to develop a connectivity pattern in support of the observed temporal structure of stimulus responses in cat visual cortex.

On the Information Storage Capacity of Local Learning Rules

1992 ◽  
Vol 4 (5) ◽  
pp. 703-711 ◽  
Author(s):  
Günther Palm
Keyword(s):  
Storage Capacity ◽  
Learning Rule ◽  
Simple Relation ◽  
Information Storage ◽  
Local Learning ◽  
Learning Rules

A simple relation between the storage capacity A for autoassociation and H for heteroassociation with a local learning rule is demonstrated: H = 2A. Both values are bounded by local learning bounds: A ≤ LA and H ≤ LH. LH = 2LA is evaluated numerically.

scholarly journals Visual motion computation in recurrent neural networks

2017 ◽  
Author(s):  
Marius Pachitariu ◽  
Maneesh Sahani
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Information ◽  
Visual Motion ◽  
Hebbian Learning ◽  
Temporal Dynamics ◽  
Temporal Structure ◽  
Receptive Fields ◽  
Learning Rule ◽  
Connectivity Matrix

AbstractPopulations of neurons in primary visual cortex (V1) transform direct thalamic inputs into a cortical representation which acquires new spatio-temporal properties. One of these properties, motion selectivity, has not been strongly tied to putative neural mechanisms, and its origins remain poorly understood. Here we propose that motion selectivity is acquired through the recurrent mechanisms of a network of strongly connected neurons. We first show that a bank of V1 spatiotemporal receptive fields can be generated accurately by a network which receives only instantaneous inputs from the retina. The temporal structure of the receptive fields is generated by the long timescale dynamics associated with the high magnitude eigenvalues of the recurrent connectivity matrix. When these eigenvalues have complex parts, they generate receptive fields that are inseparable in time and space, such as those tuned to motion direction. We also show that the recurrent connectivity patterns can be learnt directly from the statistics of natural movies using a temporally-asymmetric Hebbian learning rule. Probed with drifting grating stimuli and moving bars, neurons in the model show patterns of responses analogous to those of direction-selective simple cells in primary visual cortex. These computations are enabled by a specific pattern of recurrent connections, that can be tested by combining connectome reconstructions with functional recordings.*Author summaryDynamic visual scenes provide our eyes with enormous quantities of visual information, particularly when the visual scene changes rapidly. Even at modest moving speeds, individual small objects quickly change their location causing single points in the scene to change their luminance equally fast. Furthermore, our own movements through the world add to the velocities of objects relative to our retinas, further increasing the speed at which visual inputs change. How can a biological system process efficiently such vast amounts of information, while keeping track of objects in the scene? Here we formulate and analyze a solution that is enabled by the temporal dynamics of networks of neurons.

scholarly journals Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Aditya Gilra ◽  
Wulfram Gerstner
Keyword(s):  
The Body ◽  
Spiking Neurons ◽  
Local Learning ◽  
Weight Changes ◽  
Learning Rules ◽  
Body Dynamics ◽  
Non Linear ◽  
Stable Learning ◽  
The Brain ◽  
Linear Body

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.

Stimulus-Dependent Assembly Formation of Oscillatory Responses: II. Desynchronization

1991 ◽  
Vol 3 (2) ◽  
pp. 167-178 ◽  
Author(s):  
Thomas B. Schillen ◽  
Peter König
Keyword(s):  
Visual Cortex ◽  
Temporal Structure ◽  
Nonlinear Oscillators ◽  
Oscillatory Activity ◽  
Two Dimensional ◽  
Potential Mechanism ◽  
Neuronal Responses ◽  
Binding Problem ◽  
Cortical Areas ◽  
The Brain

Recent theoretical and experimental work suggests a temporal structure of neuronal spike activity as a potential mechanism for solving the binding problem in the brain. In particular, recordings from cat visual cortex demonstrate the possibility that stimulus coherency is coded by synchronization of oscillatory neuronal responses. Coding by synchronized oscillatory activity has to avoid bulk synchronization within entire cortical areas. Recent experimental evidence indicates that incoherent stimuli can activate coherently oscillating assemblies of cells that are not synchronized among one another. In this paper we show that appropriately designed excitatory delay connections can support the desynchronization of two-dimensional layers of delayed nonlinear oscillators. Closely following experimental observations, we then present two examples of stimulus-dependent assembly formation in oscillatory layers that employ both synchronizing and desynchronizing delay connections: First, we demonstrate the segregation of oscillatory responses to two overlapping but incoherently moving stimuli. Second, we show that the coherence of movement and location of two stimulus bar segments can be coded by the correlation of oscillatory activity.

scholarly journals Stable task information from an unstable neural population

2019 ◽  
Author(s):  
Michael E. Rule ◽  
Adrianna R. Loback ◽  
Dhruva V. Raman ◽  
Laura Driscoll ◽  
Christopher D. Harvey ◽  
...  
Keyword(s):  
Neural Activity ◽  
Sensorimotor Cortex ◽  
Learning Rule ◽  
Neural Population ◽  
Local Learning ◽  
Significant Component ◽  
Obvious Change ◽  
Consistent Information

AbstractOver days and weeks, neural activity representing an animal’s position and movement in sensorimotor cortex has been found to continually reconfigure or ‘drift’ during repeated trials of learned tasks, with no obvious change in behavior. This challenges classical theories which assume stable engrams underlie stable behavior. However, it is not known whether this drift occurs systematically, allowing downstream circuits to extract consistent information. We show that drift is systematically constrained far above chance, facilitating a linear weighted readout of behavioural variables. However, a significant component of drift continually degrades a fixed readout, implying that drift is not confined to a null coding space. We calculate the amount of plasticity required to compensate drift independently of any learning rule, and find that this is within physiologically achievable bounds. We demonstrate that a simple, biologically plausible local learning rule can achieve these bounds, accurately decoding behavior over many days.

Auditory modulation of oscillatory activity in extra-striate visual cortex and its contribution to audio–visual multisensory integration: A human intracranial EEG study

2012 ◽  
Vol 25 (0) ◽  
pp. 198
Author(s):  
Manuel R. Mercier ◽  
John J. Foxe ◽  
Ian C. Fiebelkorn ◽  
John S. Butler ◽  
Theodore H. Schwartz ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Multisensory Integration ◽  
Multisensory Processing ◽  
Brain Activity ◽  
Visual Stimuli ◽  
Additive Model ◽  
Oscillatory Activity ◽  
Time Frequency ◽  
Electrical Brain Activity ◽  
Sensory Cortices

Investigations have traditionally focused on activity in the sensory cortices as a function of their respective sensory inputs. However, converging evidence from multisensory research has shown that neural activity in a given sensory region can be modulated by stimulation of other so-called ancillary sensory systems. Both electrophysiology and functional imaging support the occurrence of multisensory processing in human sensory cortex based on the latency of multisensory effects and their precise anatomical localization. Still, due to inherent methodological limitations, direct evidence of the precise mechanisms by which multisensory integration occurs within human sensory cortices is lacking. Using intracranial recordings in epileptic patients () undergoing presurgical evaluation, we investigated the neurophysiological basis of multisensory integration in visual cortex. Subdural electrical brain activity was recorded while patients performed a simple detection task of randomly ordered Auditory alone (A), Visual alone (V) and Audio–Visual stimuli (AV). We then performed time-frequency analysis: first we investigated each condition separately to evaluate responses compared to baseline, then we indexed multisensory integration using both the maximum criterion model (AV vs. V) and the additive model (AV vs. A+V). Our results show that auditory input significantly modulates neuronal activity in visual cortex by resetting the phase of ongoing oscillatory activity. This in turn leads to multisensory integration when auditory and visual stimuli are simultaneously presented.

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