| || Models ||Description|
Acetylcholine-modulated plasticity in reward-driven navigation (Zannone et al 2018)
||"Neuromodulation plays a fundamental role in the acquisition of new behaviours. In previous
experimental work, we showed that acetylcholine biases hippocampal synaptic plasticity towards
depression, and the subsequent application of dopamine can retroactively convert depression into
potentiation. We also demonstrated that incorporating this sequentially neuromodulated Spike-
Timing-Dependent Plasticity (STDP) rule in a network model of navigation yields effective learning
of changing reward locations. Here, we employ computational modelling to further characterize the
effects of cholinergic depression on behaviour. We find that acetylcholine, by allowing learning from
negative outcomes, enhances exploration over the action space. We show that this results in a variety
of effects, depending on the structure of the model, the environment and the task. Interestingly,
sequentially neuromodulated STDP also yields flexible learning, surpassing the performance of other
reward-modulated plasticity rules."
Alternative time representation in dopamine models (Rivest et al. 2009)
||Combines a long short-term memory (LSTM) model of the cortex to a temporal difference learning (TD) model of the basal ganglia. Code to run simulations similar to the published data: Rivest, F, Kalaska, J.F., Bengio, Y. (2009) Alternative time representation in dopamine models. Journal of Computational Neuroscience.
See http://dx.doi.org/10.1007/s10827-009-0191-1 for details.
Cerebellar memory consolidation model (Yamazaki et al. 2015)
||"Long-term depression (LTD) at parallel fiber-Purkinje cell (PF-PC)
synapses is thought to underlie memory formation in cerebellar motor
Recent experimental results, however, suggest that multiple
plasticity mechanisms in the cerebellar cortex and
cerebellar/vestibular nuclei participate in memory formation.
examine this possibility, we formulated a simple model of the
cerebellum with a minimal number of components based on its known
anatomy and physiology, implementing both LTD and long-term
potentiation (LTP) at PF-PC synapses and mossy fiber-vestibular
nuclear neuron (MF-VN) synapses.
With this model, we conducted a
simulation study of the gain adaptation of optokinetic response (OKR)
Our model reproduced several important aspects of
previously reported experimental results in wild-type and
cerebellum-related gene-manipulated mice.
Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)
||A simulator and the configuration files for three publications are
provided. First, "A hybrid generative and predictive model of the motor
cortex" (Weber at al. 2006) which uses reinforcement learning to set up a
toy action scheme, then uses unsupervised learning to "copy" the learnt
action, and an attractor network to predict the hidden code of the
unsupervised network. Second, "A Self-Organizing Map of Sigma-Pi Units"
(Weber and Wermter 2006/7) learns frame of reference transformations on
population codes in an unsupervised manner. Third, "A possible
representation of reward in the learning of saccades" (Weber and Triesch,
2006) implements saccade learning with two possible learning schemes for
horizontal and vertical saccades, respectively.
Dynamic dopamine modulation in the basal ganglia: Learning in Parkinson (Frank et al 2004,2005)
||See README file for all info on how to run models under different tasks and simulated Parkinson's and medication conditions.
Hebbian STDP for modelling the emergence of disparity selectivity (Chauhan et al 2018)
||This code shows how Hebbian learning mediated by STDP mechanisms could explain the emergence of disparity selectivity in the early visual system. This upload is a snapshot of the code at the time of acceptance of the paper. For a link to a soon-to-come git repository, consult the author's website: www.tusharchauhan.com/research/ .
The datasets used in the paper are not provided due to size, but download links and expected directory-structures are. The user can (and is strongly encouraged to) experiment with their own dataset. Let me know if you find something interesting!
Finally, I am very keen on a redesign/restructure/adaptation of the code to more applied problems in AI and robotics (or any other field where a spiking non-linear approach makes sense). If you have a serious proposal, don't hesitate to contact me [research AT tusharchauhan DOT com ].
Modeling hebbian and homeostatic plasticity (Toyoizumi et al. 2014)
We propose a
model in which synaptic strength is the product of
a synapse-specific Hebbian factor and a postsynaptic-
cell-specific homeostatic factor, with each factor
separately arriving at a stable inactive state.
model captures ODP dynamics and has plausible
We confirm model predictions
experimentally that plasticity is inactive at
stable states and that synaptic strength overshoots
during recovery from visual deprivation.
Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014)
In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling.
Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers.
Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex.
After training with natural images, the neurons display heightened sensitivity to specific colors.
Towards a biologically plausible model of LGN-V1 pathways (Lian et al 2019)
||"Increasing evidence supports the hypothesis that the visual system
employs a sparse code to represent visual stimuli, where information
is encoded in an efficient way by a small population of cells that
respond to sensory input at a given time. This includes simple cells
in primary visual cortex (V1), which are defined by their linear
spatial integration of visual stimuli. Various models of sparse coding
have been proposed to explain physiological phenomena observed in
simple cells. However, these models have usually made the simplifying
assumption that inputs to simple cells already incorporate linear
spatial summation. This overlooks the fact that these inputs are known
to have strong non-linearities such as the separation of ON and OFF
pathways, or separation of excitatory and inhibitory
neurons. Consequently these models ignore a range of important
experimental phenomena that are related to the emergence of linear
spatial summation from non-linear inputs, such as segregation of ON
and OFF sub-regions of simple cell receptive fields, the push-pull
effect of excitation and inhibition, and phase-reversed
cortico-thalamic feedback. Here, we demonstrate that a two-layer model
of the visual pathway from the lateral geniculate nucleus to V1 that
incorporates these biological constraints on the neural circuits and
is based on sparse coding can account for the emergence of these
experimental phenomena, diverse shapes of receptive fields and
contrast invariance of orientation tuning of simple cells when the
model is trained on natural images. The model suggests that sparse
coding can be implemented by the V1 simple cells using neural circuits
with a simple biologically plausible architecture."